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Understanding Science

How science REALLY works...

• The philosophy of science is a field that deals with what science is, how it works, and the logic through which we build scientific knowledge.
• In this website, we present a rough synthesis of some new and some old ideas from the philosophy of science.

The philosophy of science

In this website, we use a practical checklist to get a basic picture of what ​​ science  is and a flexible flowchart to depict how science works. For most everyday purposes, this gives us a fairly complete picture of what science is and is not. However, there is an entire field of rigorous academic study that deals specifically with what science is, how it works, and the logic through which we build scientific knowledge. This branch of philosophy is handily called the philosophy of science. Many of the ideas that we present in this website are a rough synthesis of some new and some old ideas from the philosophy of science.

Despite its straightforward name, the field is complex and remains an area of current inquiry. Philosophers of science actively study such questions as:

• What is a ​​ law  of nature? Are there any in non-physical sciences like biology and psychology?
• What kind of ​​ data  can be used to distinguish between real causes and accidental regularities?
• How much ​​ evidence  and what kinds of evidence do we need before we accept ​​ hypotheses ?
• Why do scientists continue to rely on ​​ models  and ​​ theories  which they know are at least partially inaccurate (like Newton’s physics)?

Though they might seem elementary, these questions are actually quite difficult to answer satisfactorily. Opinions on such issues vary widely within the field (and occasionally part ways with the views of scientists themselves — who mainly spend their time  doing  science, not analyzing it abstractly). Despite this diversity of opinion, philosophers of science can largely agree on one thing: there is no single, simple way to define science!

Though the field is highly specialized, a few touchstone ideas have made their way into the mainstream. Here’s a quick explanation of just a few concepts associated with the philosophy of science, which you might (or might not) have encountered.

• Epistemology  — branch of philosophy that deals with what knowledge is, how we come to ​​ accept  some things as true, and how we justify that acceptance.
• Empiricism  — set of philosophical approaches to building knowledge that emphasizes the importance of ​​ observable  evidence from the ​​ natural world .
• Induction  — method of reasoning in which a generalization is argued to be true based on individual examples that seem to fit with that generalization. For example, after observing that trees, bacteria, sea anemones, fruit flies, and humans have cells, one might  inductively  ​​ infer  that all organisms have cells.
• Deduction  — method of reasoning in which a conclusion is logically reached from premises. For example, if we know the current relative positions of the moon, sun, and Earth, as well as exactly how these move with respect to one another, we can ​​ deduce  the date and location of the next solar eclipse.
• Parsimony/Occam’s razor  — idea that, all other things being equal, we should prefer a simpler explanation over a more complex one.
• Demarcation problem  — the problem of reliably distinguishing science from non-science. Modern philosophers of science largely agree that there is no single, simple criterion that can be used to demarcate the boundaries of science.
• Falsification  — the view, associated with philosopher Karl Popper, that evidence can only be used to rule out ideas, not to support them. Popper proposed that scientific ideas can only be ​​ tested  through ​​ falsification , never through a search for supporting evidence.
• Paradigm shifts and scientific revolutions  — a view of science, associated with philosopher Thomas Kuhn, which suggests that the history of science can be divided up into times of normal science (when scientists add to, elaborate on, and work with a central, accepted scientific theory) and briefer periods of revolutionary science. Kuhn asserted that during times of revolutionary science, anomalies refuting the accepted theory have built up to such a point that the old theory is broken down and a new one is built to take its place in a so-called “paradigm shift.”

Who’s who in the philosophy of science

If you’re interested in learning more about the philosophy of science, you might want to begin your investigation with some of the big names in the field:

Aristotle (384-322 BC) — Arguably the founder of both science and philosophy of science. He wrote extensively about the topics we now call physics, astronomy, psychology, biology, and chemistry, as well as logic, mathematics, and epistemology.

Francis Bacon (1561-1626) — Promoted a scientific method in which scientists gather many ​​ facts  from observations and ​​ experiments , and then make ​​ inductive inferences  about patterns in nature.

Rene Descartes (1596-1650) — Mathematician, scientist, and philosopher who promoted a scientific method that emphasized deduction from first principles. These ideas, as well as his mathematics, influenced Newton and other figures of the Scientific Revolution.

Piere Duhem (1861-1916) — Physicist and philosopher who defended an extreme form of empiricism. He argued that we cannot draw conclusions about the existence of unobservable entities conjectured by our theories such as atoms and molecules.

Carl Hempel (1905-1997) — Developed influential theories of scientific explanation and theory confirmation. He argued that a phenomenon is “explained” when we can see that it is the logical consequence of a law of nature. He championed a hypothetico-deductive account of confirmation, similar to the way we characterize “making a ​​ scientific argument ” in this website.

Karl Popper (1924-1994) — Argued that falsifiability is both the hallmark of scientific theories and the proper methodology for scientists to employ. He believed that scientists should always regard their theories with a skeptical eye, seeking every opportunity to try to falsify them.

Thomas Kuhn (1922-1996) — Historian and philosopher who argued that the picture of science developed by logical empiricists such as Popper didn’t resemble the history of science. Kuhn famously distinguished between normal science, where scientists solve puzzles within a particular framework or paradigm, and revolutionary science, when the paradigm gets overturned.

Paul Feyerabend (1924-1994) — A rebel within the philosophy of science. He argued that there is no scientific method or, in his words, “anything goes.” Without regard to rational guidelines, scientists do whatever they need to in order to come up with new ideas and persuade others to accept them.

Evelyn Fox Keller (1936-) — Physicist, historian, and one of the pioneers of feminist philosophy of science, exemplified in her study of Barbara McClintock and the history of genetics in the 20th century.

Elliott Sober (1948-) — Known for his work on ​​ parsimony  and the conceptual foundations of evolutionary biology. He is also an important contributor to the biological theory of group selection.

Nancy Cartwright (1944-) — Philosopher of physics known for her claim that the laws of physics “lie” — i.e., that the laws of physics only apply in highly idealized circumstances. She has also worked on causation, interpretations of probability and quantum mechanics, and the metaphysical foundations of modern science.

• Take a sidetrip

Learn about specialized topics in the philosophy of science with the  Stanford Encyclopedia of Philosophy .

Source material: Godfrey-Smith, P. 2003. Theory and Reality. Chicago: The University of Chicago Press.

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• CAREER COLUMN
• 23 April 2021

How philosophy is making me a better scientist

• Rasha Shraim 0

Rasha Shraim is a PhD student at Trinity College Dublin, on a programme run by the SFI Centre for Research Training in Genomics Data Science.

You can also search for this author in PubMed   Google Scholar

I am the only student on my PhD programme in genomics data science with an undergraduate degree in biology and philosophy. Initially, I saw these as separate fields: I was writing about theories of morality in one class and memorizing the Krebs cycle in another. It was only after picking up first-hand research experience while working on my final-year biology thesis at New York University Abu Dhabi that I began to understand how philosophy can make me a better scientist. As I progress through the early stages of my PhD, I can see how impactful reading and studying philosophy have been in shaping my career so far, and how much they will continue to influence me in my future work.

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The Oxford Handbook of Philosophy of Science

Paul Humphreys is Professor of Philosophy at the University of Virginia. His current research interests include computational science, emergence, and heterodox economics. Recent publications include Extending Ourselves: Computational Science, Empiricism and Scientific Method (Oxford, 2004); and Emergence: Contemporary Readings in Science and Philosophy, co‐edited with Mark Bedau (MIT Press, 2008).

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This Handbook provides the reader with access to core areas in the philosophy of science and to new directions in the discipline. Part I contains broad overviews of the main lines of research and the state of established knowledge in six principal areas of the discipline, including computational, physical, biological, psychological, and social sciences, as well as general philosophy of science. Part II covers what are considered to be the traditional topics in the philosophy of science such as causation, probability, models, ethics and values, and explanation. Part III identifies new areas of investigation that show promise of becoming important areas of research, including the philosophy of astronomy and astrophysics, data, complexity theory, neuroscience, simulations, post-Kuhnian philosophy, post-empiricist epistemology, and emergence. Most chapters are accessible to scientifically educated non-philosophers as well as to professional philosophers. The authors bring different perspectives from the North American, European, and Australasian research communities, and all are leading researchers in their fields. All the contributors were encouraged to provide a new perspective on the topic at hand in addition to providing basic information about the subject.

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• University of Pennsylvania
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University of Pennsylvania Philosophy

Philosophy of Science

Penn has considerable strength in philosophy of science and related areas of science studies. We are especially strong in the philosophy of the life and social sciences, the relations between the history of philosophy and the history of science, and the history of the philosophy of science.

Philosophy of the Natural Sciences and Mathematics

Our faculty work on diverse topics in the philosophy of natural sciences, but areas of special interest interest include philosophy of biology (Spencer, Weisberg), psychology and vision (Hatfield), learning theory (Bicchieri, Weinstein), the history of biology and psychology (Detlefsen, Hatfield), philosophy of chemistry (Weisberg), and Public Understanding of Science (Weisberg). A number of faculty also work directly in areas of the natural and formal sciences including cognitive science (Bicchieri, Hatfield, Weinstein, Weisberg), evolutionary and ecological modeling (Bicchieri, Weisberg), and computer science (Weinstein). Spencer, and Weisberg also work on many central topics of philosophy of science including explanation, the structure of theories, confirmation, and the social structure of science.

In addition, William Ewald (Law) teaches history and philosophy of mathematics, Steve Kimbrough (Wharton) teaches modeling, machine learning, and induction, Alan Kors and Ann Moyer (Department of History) offer courses in early modern intellectual history and history of science. History and Sociology of Science regularly offers courses in the history of biology (Lindee), the Scientific and Romantic revolutions (Kucuk, Tresch), and the history of technology (Voskuhl).

Philosophy of Social Science

Our faculty are also interested in some of the central questions in contemporary social science, such as: Are social beings with intentions producing collective outcomes nobody planned or predicted? Can groups of rational agents act in collectively beneficial ways? Can we explain features of the social world, like conventions and social norms, as the result of individuals’ beliefs and desires? How institutions evolve, and how can we model their dynamics?  

Cristina Bicchieri is interested in how norms may emerge and become stable, why an established norm may suddenly be abandoned, how is it possible that inefficient or unpopular norms survive, and what motivates people to obey norms. In order to answer some of these questions, she has combined evolutionary and game-theoretic tools with models of decision making drawn from cognitive and social psychology. For example, she has developed a theory of context-dependent preferences that explains the observed variability in norm compliance and is testing it in experimental games that involve pro-social norms of fairness and reciprocity.

The emergence of norms can be modeled in several ways, depending upon the type of norm that is investigated. Bicchieri and her students have studied how unpopular descriptive norms such as "bad" fashions and fads may occur as the result of negative informational cascades when agents are in the grip of 'pluralistic ignorance'. Often what we call a social norm is a stable behavioral disposition that is supported by a variety of strategies. Impersonal trust, for example, can evolve as a stable disposition in a population of conditionally "nice" agents. A surprising result of this evolutionary model is that what we take to be unconditional moral norms can only survive in populations of conditional choosers.

Michael Weisberg has developed agent-based models that explains how scientific communities coordinate in producing scientific results, and how the division of cognitive labor affects the success of the scientific enterprise. In particular, Weisberg has developed an agent-based computational model in which different research approaches (strategies) are distributed within an epistemic landscape, each approach having its own epistemic payoff. A natural question to ask is how much attention should scientists pay to what other scientists are doing, and what are the costs/benefits of doing so. Weisberg shows that the most effective communities (i.e., those with the highest payoff) are a combination of trendsetters (who open new research paths) and followers (who imitate the most successful members of the scientific community).

Philosophy and Science: What Can I Know?

• Open Access
• First Online: 11 April 2019

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• James B. Glattfelder 9  

Part of the book series: The Frontiers Collection ((FRONTCOLL))

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Philosophy is a thorny subject. Many philosophical statements cannot be formally proven, resulting in clever but endless debates. Scientists usually shy away from such ambiguity and retreat into their safe world of perceived clarity. Nevertheless, the philosophical study of nature is the wellspring of science. Simply asking “What is a law of nature?” poses a philosophical challenge. The philosophy of science is concerned with the foundations, methods, and implications of science and how scientists conduct their research. The first attempts to systematize the scientific method was based on common sense: from observations abstract laws are found. This view turned out to be untenable and both inductive and deductive reasoning suffer from conceptual problems. Indeed, science is not an incremental process accumulating knowledge but is greatly influenced by social and cultural conditions. It comes perhaps as no surprise that an animosity exists between science and philosophy. For instance, the controversial philosopher of science, Paul Feyerabend, continually challenged the scientific establishment. Historically, the emergence of modern physics overthrew nearly every postulate of classical science and replaced them with bizarre new concepts, from elusive quantum fluctuations to the fabric of space-time. The aftershocks of this fundamental transformation still echo to this day. On the one hand the universe is, miraculously, comprehensible to the human mind, but on the other hand scientific progress appears to be slowing down. Paradoxically, every question answered raises more and harder questions and theories appear to be losing meaning. If asked, some scientists will admit to these shortcomings: uncertainty and ignorance are inherent and ubiquitous in science. The final blow to a clear foundation of knowledge comes from the discoveries that incompleteness and randomness lurk at the heart of mathematics.

Level of mathematical formality : low.

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Science works! This is attested by the spectacular display of human technological prowess. Indeed, technological advancements, made possible by the scientific understanding of the universe, are becoming ever more disruptive and frequent. How is it then justifiable to speak of the crisis of science and even allude to the end of science? This chapter will explore between the poles of perceived knowledge and inescapable ignorance—between the illusion of certainty and limits of reason.

1 The Philosophy of Science

Even the simplest of questions can have the power to open Pandora’s box of existential dilemmas. All attempts to answer the innocent question “What can I know?” have been inconclusive at best. This question has a long history that has accompanied mankind during its efforts to scale the mountain of knowledge and has continued to eluded the conceptual grasp of our minds.

The journey begins with one of history’s first scientists (Grant 2002 , p. 33):

No one in the history of civilization has shaped our understanding of science and natural philosophy more than the great Greek philosopher and scientist Aristotle (384–322 B.C.), who exerted a profound and pervasive influence for more than two thousand years [...].

As one of the first thinkers he introduce logic as a means of reasoning. He had a clear vision of what knowledge constitutes and he founded it on intuition (Ross 1963 , Book VI, Chapter 6):

Scientific knowledge is judgment about things that are universal and necessary, and the conclusions of demonstration, and all scientific knowledge, follow from first principles [but] it is intuitive reason that grasps the first principles.

Nearly two thousand years later, not much had changed. But then, in 1620, the philosopher Francis Bacon presented modifications to Aristotle’s ideas (Bacon 2000 ). In essence, a new logic, a reductionist approach, the focus on inductive reasoning, and the aspiration that scientific knowledge should foster technology, introduced what has become known as the modern scientific method. Bacon paved the way for a new, contemporary understanding of scientific inquiry. Approximately at the same time, Robert Boyle, seen by some as one of the founder of modern chemistry, was instrumental in establishing experiments as the cornerstone of physical sciences, working with an air pump (Boyle 1682 ; Shapin and Schaffer 2011 ).

The folowing six sections are adapted and expanded from Glattfelder ( 2013 ).

1.1 Logical Empiricism

By the early 20th Century, the notion that science is based on experience (i.e., empiricism) and logic, where knowledge is intersubjectively testable, has had a long history. The philosophical school of logical empiricism (or logical positivism) tried to formalize these ideas. Notably, utilizing the tools of mathematical logic, which had matured extensively under the contributions of Betrand Russell. Footnote 1 The Vienna Circle , a group of philosophers, scientist, and mathematics meeting regularly from 1924 to 1936 at the University of Vienna, was a major social hub of the movement. Some notable proponents were Rudolf Carnap, Kurt Gödel, Otto Neurath, Karl Popper, Hilary Putnam, Willard Van Orman Quine, Hans Reichenbach, and Ludwig Wittgenstein (Creath 2013 ). See also Sect.  2.2.1 .

In this paradigm, science is viewed as a building comprised of logical building blocks based on an empirical foundation. A theory is understood as having the following structure:

Observation \(\rightarrow \) Empirical concepts \(\rightarrow \) Formal notions \(\rightarrow \) Abstract laws

In essence, a sequence of ever higher abstractions. This notion of unveiling laws of nature by starting with individual observations is called inductive reasoning. Conversely, deductive reasoning starts with the abstract laws and seeks knowledge by finding a tangible factual description.

What started off as a well-founded and legitimate inquiry into the workings of nature soon faced serious difficulties and the opposition of influential scholars, some even from within the movement. As an example, Popper later claimed to have “killed” logical empiricism. Problems appeared on many fronts. For instance:

How can one construct pure formal concepts that solely reflect empirical facts without already anticipating a theoretical framework?

How does one link theoretical concepts (like electrons, inflational cosmology, Higgs bosons, utility functions in economics, ...) to experiential notions?

How can one distinguish science from pseudo-science?

Somewhat technical, these challenges highlight that the logical empiricists where engaging with the notion of knowledge at a very subtle level, invoking the proverb “the devil is in the details.” However, some glaring problems surfaced as well. One central issue concerns the legitimacy of inductive logic: can inductive reasoning lead to new knowledge? Not really, as deriving a generalization from multiple observations or repeated experiences is unjustified:

black swan: no matter how often I observe white swans, I cannot exclude the existence of a non-white one;

the future resembles the past: to assume that a sequence of events in the future will occur like it always has in the past, requires the complete knowledge of how the future evolves from the present according to laws of nature Footnote 2 ;

from the particular to the general: for instance, declaring that all wooden bodies float, based on the observation that a single piece of wood floats, is untenable without infusing additional, auxiliary knowledge, like Archimedes’ principle.

The problem of inductive reasoning in logic then also challenges notions of causality and causal relations. See Brun and Kuenzle ( 2008 ).

So, in 1967, the philosopher John Passmore declared: “Logical positivism, then, is dead, or as dead as a philosophical movement ever becomes” (as quoted in Creath 2013 ).

1.2 Critical Rationalism

While empiricism historically was shaped by the insights of John Locke and David Hume, that all knowledge stems from experience, rationalism was crucially influenced by René Descartes and Gottfried Wilhelm Leibniz: knowledge can have aspects that do not stem from experience, i.e., there is an immanent reality to the mind.

The critical rationalists believed they could fix the problems the logical empiricists had faced. Popper was a key figure advancing this epistemological philosophy (Popper 1934 ). The central theme, referred to by the adjective “critical,” revolves around the idea of falsifiability or fallibility. The idea, that insights gained by pure thought can never be justified but only critically tested by experience and thus discarded if discrepancies are observed. In a nutshell, no number of experiments can ever be used to prove a theory, but a single experiment suffices to contradict an established theory. Moreover, any claims of ultimate justification only lead to the so-called Münchhausen Footnote 3 trilemma (Albert 1968 ) . That is, one of the following consequences will necessarily be encountered by any attempt to prove a truth:

an infinite regress of justifications;

circular reasoning;

axiomatic or dogmatic termination of reasoning.

To address the problem of inductive reasoning, the critical rationalist turned the tables. Now, from a currently (not falsified) theory or premise, a logically certain conclusion is sought, which can be observed in nature. This top-down logic moves from the abstract to the empirical. As a result, science is no more understood as a linear accumulation of knowledge, metaphorically assembling the edifice of science. In its new incarnation, science is a construct that is invented by people who relentlessly test and adapt its contents. The progression of science is hence seen as an evolutionary, organic process.

Ultimately however, also the school of critical rationalism faced insurmountable challenges. In a nutshell (Brun and Kuenzle 2008 ):

How can basic formal concepts be derived from experience without the help of induction and how can they be shown to be true?

What parts of a theory need to be discarded once it is falsified?

But most crucially, the principle of deduction is also plagued by epistemic problems. For how can these principles of deduction be justified in the first place? Moreover (Markie 2015 ):

Intuition and deduction can provide us with knowledge of necessary truths such as those found in mathematics and logic, but such knowledge is not substantive knowledge of the external world. It is only knowledge of the relations of our own ideas.

A colorful account, of how the conclusion of even a simple deductive argument cannot be logically enforce, can be found in one of the writings of Lewis Carroll, the mathematician who conceived the Alice in Wonderland stories (Carroll 1895 ).

This is indeed a surprising turn of events. Induction and deduction ultimately fail as rigorous logical tools to generate knowledge of the outer world. Furthermore, many technicalities prevent, or seriously challenge, a clear understanding and justification of what is actually going on in the scientists’ minds when they engage in science. But perhaps science never was what we humans have idealized it to be for millennia. Perhaps science is a messier and murkier enterprise after all.

1.3 The Kuhnian View

Thomas Kuhn’s enormously influential work on the history of science is called the The Structure of Scientific Revolutions (Kuhn 1962 ). He irrevocably overthrew the idealized notion that science is an incremental process accumulating more and more knowledge. Instead, he identified the following phases in the evolution of science:

prehistory: many schools of thought coexist and controversies are abundant;

history proper: one group of scientists establishes a new solution to an existing problem which opens the doors to further inquiry and a so called paradigm emerges;

paradigm based science: unity in the scientific community on what the fundamental questions and central methods should be (generally a problem or puzzle solving process within the boundaries of unchallenged rules, analogous to solving a Sudoku challenge);

crisis: more and more anomalies and boundaries start to appear, questioning and challenging the established rules;

revolution: a new theory and Weltbild takes over solving the anomalies and a new paradigm is born.

Kuhn cites the Copernican revolution as an example. Puzzled by the movements of planets and stars in the night sky, ancient humans offered many colorful myths as explanation. Then, around 100 C.E., Claudius Ptolemy had a breakthrough with his geocentric model, building on insights gained by Aristotle and others. Suddenly, fairly accurate predictions could be made about celestial mechanics. With time it became more and more apparent, that the model needed to be adapted and adjusted to account for new observational data. Employing ever more epicycles, that is circles within circles, it was hoped that the model’s predictions would increase in accuracy. However, the mixing of epicycles led to a nearly unworkable system by the time Nicolaus Copernicus entered the scene, in the mid 16th Century. He boldly laid out a new paradigm by placing the sun at the center of the solar system (Copernicus 1543 ). Initially, the heliocentric model was not preciser than the geocentric model. Only the verification of novel predictions brought the final success, establishing the new paradigm.

Another core concept found in The Structure of Scientific Revolutions is called incommensurability . The term was introduced in the early 1960s by Kuhn and, independently, by the radical philosopher of science Paul Feyerabend (Preston 1997 ). Basically, if a scientist is too deeply embedded and invested in a specific conceptual framework, worldview, or paradigm, he or she will be unable to understand the reasoning of an outsider scientist, constrained by their own paradigm. More technically, every rule is part of a paradigm and there exist no trans-paradigmatic rules. The consequences are startling: the languages within different paradigms do not overlap enough to enable scientists to compare their theories. While Kuhn understood incommensurability as locally confined and only applicable to some terms and concepts, Feyerabend saw this as a global feature affecting every theory. In effect, scientists are plagued by blind spots and other cognitive biases (Sects.  11.3.2 and 14.4.2 ). More on Feyerabend and his unapologetic belief, see Sect.  9.1.6 below.

Perhaps a nice illustration of such myopia among scientists can be found in the controversy surrounding the reality of the (electromagnetic 4-vector) potential \(A_\mu \) . This is the quantum field underlying the electric and magnetic fields and was introduced in the context of gauge theory. For nearly thirty years people believed that it could not produce any observable effects and was hence a fictitious filed. Then, however, a simple but ingenious experiment established the reality of the potential field, verified by the Aharonov-Bohm effect. In the words of Nobel laureate Richard Feynman (Feynman et al. 1964 , p. 15–12):

The implication was there all the time, but no one paid attention to it. Thus many people were rather shocked when the matter was brought up. [...]. It is interesting that something like this can be around for thirty years but, because of certain prejudices of what is and is not significant, continues to be ignored.

See Sect.  4.2 for details on the potential \(A_\mu \) , defined in ( 4.12 ), its role in gauge theory, and the Aharonov-Bohm effect.

Another disturbing consequence of Kuhn’s inquiries is that scientific revolutions are not at all rational processes governed by insights and reason. To the contrary, as Max Planck outlines in his autobiography (Planck 1950 , pp. 33f.):

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.

Quite a dire analysis indeed, far removed from the idealized scientist’s eureka moment. In the same vein, the words of Nobel laureate Steven Weinberg (Weinberg 2003 , p. 191):

Kuhn made the shift from one paradigm to another seem more like a religious conversion than an exercise in reason. He argued that our theories change so much in a paradigm shift that it is nearly impossible for scientists after a scientific revolution to see things as they had been seen under the previous paradigm.

Kuhn goes on to give additional blows to a commonsensical understanding of science with the help of Norwood Hanson and Quine:

every human observation of reality contains an a priori theoretical framework: this implies that two scientists looking at the same aspect of reality do not necessarily see the same things;

underdetermination of belief by evidence: any theory can be made compatible with recalcitrant observations by adaptions made to the background assumptions and thus data do not determine theories;

every experiment is based on auxiliary hypotheses: initial conditions, the proper functioning of the apparatus, the chosen experimental setup, the selected modes for interpreting the experimental data (what exactly are the scientists at CERN seeing, when they observe subatomic particles as peaks in a diagram, the abstract information the LHC relays from the quantum world?).

See Hanson ( 2010 ), Quine ( 1951 ), and also Brun and Kuenzle ( 2008 ).

What are the consequences of these unexpected and profound failings of the most simplest premises one would wish science to be grounded on? If logic, empiricism, objectivity, rationality, cohesion, structure, method, and a foundation are not inherently found in the way real humans conduct science, what are we left with? Indeed, people slowly started to realize the very serious consequences illuminated by the relentless but diligent inquiry led by the philosophy of science.

1.4 Postmodernism

Modernism describes the development of the Western industrialized society since the beginning of the 19th Century. Central ideas were the understanding that there exist objective true beliefs and that progression is always linear, steadily improving the status quo.

Postmodernism replaces these notions with the conviction that many different opinions and forms can coexist and even find acceptance. Core ideas are diversity, differences and intermingling. In the 1970s postmodernism is seen to enter cultural thinking, impacting art, music, and architecture. It is a notoriously hard concept to define due to its multifaceted nature. One attempt at a succinct definition centers around the idea of the meta-narrative. This is a narrative about narratives, relating to meaning, experience, and knowledge, which offers legitimation to a society. Then, in the words of the philosopher, sociologist, and literary theorist Jean-François Lyotard (Lyotard 1984 . p xxiv):

I define postmodern as incredulity toward meta-narratives.

Abroader and more vivid characterization is offered by Tarnas ( 1991 , excerpts from the Chapter “The Postmodern Mind”, p. 396f.):

What is called postmodern varies considerably according to context, but in its most general and widespread form, the postmodern mind may be viewed as open-ended, indeterminate set of attitudes that has been shaped by a great diversity of intellectual and cultural currents. There is an appreciation of the plasticity and constant change of reality and knowledge, a stress on the priority of concrete experience over fixed abstract principles, and a conviction that no single a priori thought system should govern belief or investigation. It is recognized that human knowledge is subjectively determined by a multitude of factors; that objective essences, or things-in-themselves, are neither accessible nor possible; and that the value of all truths and assumptions must be continually subjected to direct testing. The critical search for truth is constrained to be tolerant of ambiguity and pluralism, and its outcome will necessarily be knowledge that is relative and fallible rather than absolute or certain. Reality is not a solid, self-contained given but a fluid, unfolding process, an “open universe,” continually affected and molded by one’s actions and beliefs. [...]. Reality is in some sense constructed by the mind, not simply perceived by it, and many such constructions are possible, none necessarily sovereign. Hence all meaning is ultimately undecidable, and there is no “true” meaning. No underlying primal reality can be said to provide the foundation for human attempts to represent truth. [...]. The multiplicity of incommensurable human truths exposes and defeats the conventional assumption that the mind can progress ever forward to a nearer grasp of reality.

Indeed, postmodernism can be understood as a call to cultivate one’s own inner authority (Tarnas 1991 , p. 404):

The postmodern collapse of meaning has thus been countered by an emerging awareness of the individual’s self-responsibility and capacity for creative innovation and self-transformation in his or her existential and spiritual response to life.

Such personal exposure to postmodernism in everyday life is perhaps nicely captured by Sarah Kay’s experience. She is known for her spoken-word poetry and while teaching a class, Kay came up with the assignment to write a list of “10 Things I Know to be True.” By comparing one’s own list with the lists of enough other people one can observe the following Footnote 4 :

affirmation: someone has the exact same, or very similar, things as you have on your list;

dissonance: someone has the complete and total opposite to something you know is true;

novel thoughts: someone has something you have never even heard of before;

limited scope: someone has something you thought you knew everything about, but they are introducing a new angle to look at it or are offering an extended scope.

As expected, many scientists were unsympathetic to such an outlook on life and recoiled at most of the ideas associated with postmodernism. Physicist David Deutsch sees postmodern as “bad philosophy” and criticizes (Deutsch 2011 , p. 314):

It [postmodernism] is a narrative that resists rational criticism or improvement, precisely because it rejects all criticism as mere narrative. Creating a successful postmodernist theory is indeed purely a matter of meeting the criteria of the postmodernist community—which have evolved to be complex, exclusive and authority-based. Nothing like that is true of rational ways of thinking.

But not only postmodernism, with its radical epistemology and ontology, faced fierce opposition from scientists, indeed the whole idea of philosophy in general. Some scientists believe it is an irrelevant enterprise, as echoed in a quip usually attributed to Feynman: “The philosophy of science is about as useful to scientists as ornithology is to birds.” Others have openly expressed their contempt, like the eminent physicist Freeman Dyson. In an article he wrote, reviewing the book of a philosopher (Holt 2012 ), he described today’s philosophers as “a sorry bunch of dwarfs” compared to the giants of the past and portrayed contemporary philosophy as “a toothless relic of past glories” (Dyson 2012 ). Such scornful attitudes can perhaps be understood as the aftershocks of the Science Wars of the 1990s. Scientists then accused certain philosophers of having effectively rejected realism, objectivity, and rationality. They believed the scientific method, and even scientific knowledge, to be under siege. Footnote 5

In this environment, some scientists sought to defend their cherished enterprise from postmodern attacks they perceived as anti-intellectual. In one incidence, called the Sokal hoax, physicist Alan Sokal got a nonsensical paper published in a journal of postmodern cultural studies. Already the grandiloquent title does not disappoint: “Transgressing the Boundaries: Towards a Transformative Hermeneutics of Quantum Gravity” (Sokal 1996 ). By flattering the editor’s ideology with nonsense that sounds scientific and meaningful, Sokal got his 35 page long article, with profuse citations, accepted for publication. Indeed, the text is mainly a conflation of academic terms and buzzwords with sociopolitical and economic notions, as illustrated by the following except (Sokal 1996 , p. 242):

Thus, a liberatory science cannot be complete without a profound revision of the canon of mathematics. As yet no such emancipatory mathematics exists, and we can only speculate upon its eventual content. We can see hints of it in the multidimensional and nonlinear logic of fuzzy systems theory; but this approach is still heavily marked by its origins in the crisis of late-capitalist production relations. Catastrophe theory, with its dialectical emphases on smoothness/discontinuity and metamorphosis/unfolding, will indubitably play a major role in the future mathematics; but much theoretical work remains to be done before this approach can become a concrete tool of progressive political praxis.

Interestingly, modern physics has also suffered a similar embarrassment in 2002. Indeed, editors of scientific journals can just as easily succumb to imagining meaning where there is perhaps only empty jargon. The Bogdanov affair centers around the French twins and TV personalities Igor and Grichka Bogdanov. They enjoyed celebrity status and hosted a French science fiction television program. Today, they attract a lot of curiosity due to their physical appearance. What appears to be the result of extreme plastic surgery, gives the twins an eerie extraterrestrial look: drastically pronounced chins, cheekbones, and lips. The Bogdanov affair was an academic dispute regarding the legitimacy of the work produced by the twins. This included a series of theoretical physics papers published in reputable, peer-reviewed scientific journals, and their Ph.D. thesis, awarded by the University of Bourgogne in 2000. It was alleged that the contents was a meaningless combinations of buzzwords and the affair was covered in the mainstream media. The matter has also been referred to as the “reverse Sokal” hoax. To this day the Bogdanov twins have insisted upon the validity of their work, however, the controversy has prompted reflections upon the peer-review system. Declan Butler, a senior reporter for Nature Magazine, had the following to offer (Butler 2002 ):

Take a deep breath, and give the following sentence a go. “We demonstrate that the lorentzian signature of the space-time metric ( \(+ + + -\) ) is not fixed at the Planck scale and shows ‘quantum fluctuation’ between the lorentzian and euclidean ( \(+ + +\) ) forms until the 0 scale where it becomes euclidean ( \(+ + + +\) ).” Confused? Don’t worry, you’re in good company. Physicists around the world have been unable to agree on whether the Ph.D. thesis this line comes from is good, bad or a hoax. [...]. So are the papers good science or not? Enquiries by Nature show that few theoretical physicists, including some who reviewed the brothers’ Ph.D. theses, are completely certain.

Has modern physics itself really transformed into a postmodern narrative that defies meaning, clarity, and understanding? An edifice lingering in a state of undecidability? See Chap.  10 .

On a more humorous note, the website http://www.snarXiv.org/ is aimed at spoofing the theoretical, high-energy physics section of the popular scientific archive for electronic preprints http://www.arXiv.org , by automatically generating Footnote 6 meaningless titles and abstracts, infused with a barrage of buzzwords. As an example, consider the following:

Which title and abstract belongs to a legitimate contribution? For which one would you expect that there actually exists an article, building on the previous work of others, as highlighted by the many technical expressions introduced in the abstract, uncovering a novel detail of specialist knowledge? The author of the spoof website even based an online game on this idea, where the player is offered two titles, an actual high-energy physics paper from the arXiv, and a completely fake title randomly generated by the snarXiv. The aim of the game is to spot as many fake titles as possible.

However, postmodernism is only the tip of the iceberg of epistemic threats to knowledge and certainty. For one, it invokes the ghost of extreme skepticism in the form of solipsism, the belief that only one’s own mind is certain to exist while any other knowledge is necessarily unsure. A feeling epitomized by Descartes’ infamous sentence “ cogito ergo sum ” (Descartes 1937 ) and George Berkeley’s notorious denial of a material external reality in favor of a reality exclusively comprised of minds and their ideas (Downing 2013 ). But more unsettling, postmodernism opens the doors to the Scylla and Charybdis of constructivism and relativism, discussed next.

See Sect.  6.2.2 for the related notion of poststructuralism. Indeed, postmodernism (Cilliers and Spurrett 1999 ) and poststructuralism (Sect.  6.2.2 ) are the preferred philosophies of complexity. Moreover, note the debate on the foundations of mathematics and the roots of postmodern thought (Tasić 2001 ).

1.5 Constructivism

Kuhn’s analysis challenges the objective and universal nature of scientific knowledge. In essence, this knowledge is demoted to an edifice contingent upon the idiosyncrasies of human beings and their social and cultural imprintings. This predicament is lamented by Weinberg ( 2003 , p. 190f.):

What does bother me on reading [ The Structure of Scientific Revolutions ] and some of Kuhn’s later writings is his radically skeptical conclusions about what is accomplished in the work of science. And it is just these conclusions that have made Kuhn a hero to the philosophers, historians, sociologists, and cultural critics who question the objective character of scientific knowledge, and who prefer to describe scientific theories as social constructs, not so different in this respect from democracy or baseball.

Furthermore (Weinberg 2003 , p. 192):

If the transition from one paradigm to another cannot be judged by any external standard, then perhaps it is culture rather than nature that dictates the content of scientific theories.

This impact of the social and cultural conditions on science is today studied under the label of the sociology of scientific knowledge. Proponents of the University of Edinburgh and the University of Bath poularized this field of inquiry (Bloor 1976 ; Shapin et al. 1985 ; Collins 1985 ; Shapin 1994 ).

In effect, what is being proposed here is the notion that knowledge is effectively constructed: always a product of the different factors conditioning scientists. An example of such culturally influenced constructions is related to gender. Feminist critiques of science have been thematically linked to the sociology of scientific knowledge, namely the marginalization of points of view based on gender, ethnicity, socio-economic status, and political status. Given science’s long tradition of excluding women as practitioners, such critique is not unwarranted. The view that women are unfit for science, or vice versa, has been haunting the minds of male scientists since the beginning. For more on the feminist perspective on science, see Crasnow et al. ( 2015 ).

But more in general, constructivism maintains that all humans construct personal knowledge and meaning from the interactions of their subjective experiences and their ideas. Constructivist epistemology is a branch of the philosophy of science that argues that science is simply a product of such mental constructs, devised to explain the sensory experiences of the natural world. Essentially, scientific knowledge is merely constructed by the current scientific community, seeking to understand and build models of the world. More on constructivism can be found in Watzlawick ( 1984 ), Jonassen ( 1991 ), Perkins ( 1999 ).

The philosopher Ernst von Glasersfeld further added an uncompromising twist to theses ideas by introducing the notion radical constructivism (Von Glasersfeld 1984 , 1989 , 2002 ). In this relentless version, constructivism fully abandons objectivity. Or in the words of the physicist Heinz von Foerster (Schülein and Reitze 2002 , p. 174, translation mine):

Objectivity is the illusion that observations are made without an observer.

At its heart, radical constructivism questions the validity of any external sensory input. The subjective observer is placed at the center of the experience, but without the means to probe the external world conclusively. An analogy would be the submarine captain who has to rely on instruments to indirectly gain knowledge about the outside world. In detail, it is understood that perception never yields a faithful image of outer reality but is always an inner construction, derived from sensory input but dependent on the cognitive apparatus of each individual. Indeed, radical constructivists are motivated and validated by modern insights gained by neuroscience. Instead of reality being passively recorded by the brain, it is thought to be actively constructed by it. In effect, our brains sample just a small bit of the surrounding physical world from which normal perception is constructed. This “normal” mode of experiencing reality hardly differs from hallucinations or dreams which are not at all anchored by external input. The enigma of perception, and neuroscience in general, will be discussed in greater detail in Chap.  11 .

1.6 Relativism

Constructivism opens the door to the next epistemic threat: relativism. If knowledge is constructed and hence contingent, then it can be rational for a group A to believe a fact \(\mathcal {P}\) , while at the same time it is rational for group B to believe in negation of \(\mathcal {P}\) . Again, in the words of Weinberg ( 2003 , p. 192):

If scientific theories can be judged only within the context of a particular paradigm, then in this respect the scientific theories of any one paradigm are not privileged over other ways of looking at the world, such as shamanism and creationism.

Relativism is the antipodal idea of absolutism. Whereas absolutism gives comforting certainty and clarity, offering solace to both scientifically and religiously minded people by invoking the idea of objective or absolute truth, relativism is a direct existential assault undermining and tainting any notion relating to meaning, truth or belief. Relativism summons disconcerting feelings of doubt, uncertainty, and ambiguity. Recall the long history of thinkers troubled by self-doubt and ambivalent knowledge, detailed in Sect.  8.1 .

It is an interesting, albeit expected, observation that theologians and scientists alike show the same deep-seated disdain towards the idea of relativism: it is a double-edged sword threatening any institutionalized orthodoxy or scientific consensus. In the words of Joseph Ratzinger, delivered in his homily at the beginning of the conclave in 2005 from which he would emerge as Pope Benedict XVI:

We are building a dictatorship of relativism that does not recognize anything as definitive and whose ultimate standard consists solely of one’s own ego and desires.

On June 6, 2005, as Pope, he was quick to reiterate this point in the “Address of His Holiness Benedict XVI to the Participants in the Ecclesial Diocesan Convention of Rome”:

Today, a particularly insidious obstacle to the task of educating is the massive presence in our society and culture of that relativism which, recognizing nothing as definitive, leaves as the ultimate criterion only the self with its desires. And under the semblance of freedom it becomes a prison for each one, for it separates people from one another, locking each person into his or her own “ego”.

Also Pope Benedict XVI’s reformist and liberal successor, Pope Francis, adheres to this belief, viewing relativism as a vice which “makes everyone his own criterion and endangers the coexistence of peoples” (as seen in his address to the Diplomatic Corps accredited to the Holy See, on March 22, 2013).

In contrast, relativism is more prevalent in Eastern thought systems, where ideas are widespread that carry more holistic or pantheistic rings. An example can be found in Jainism, an ancient, radically non-violent Indian religion, that shares in its cosmology many of the elements of pre-Socratic Greek philosophies (see Nakamura 1998 and Sect.  3.1 ). In essence (Nakamura 1998 , p. 167):

The fundamental standpoint of Jainism [...] signifies that the universe can be looked at from many points of view, and that each viewpoint yields a different conclusion. Therefore, no conclusion is decisive.

At its heart, “Jainism shows extreme caution and anxiety to avoid all possible dogma in defining the nature of reality” (Nakamura 1998 , p. 169).

In Western philosophy, relativism can be attributed to the Greek thinkers Heraclitus and Protagoras. So, even before Aristotle would set out to revolutionize the way we think about reality, a process ultimately leading to science, the seeds of relativity were sown, which would, about two and a half millennia later, prompt thinkers to view science as just one of many ways of knowing the world. In this context we encounter the influential, colorful, and controversial philosopher of science, Paul Feyerabend. He was truly the enfant terrible of relativism (Kidd 2011 ):

Feyerabend was famously dubbed “the worst enemy of science” by Science , and even today philosophers of science will tend to associate his name with anti-science polemics, defences of voodoo and astrology, and more besides.

This is perhaps not surprising, looking at the titles of the books he published:

Against Method: Outline of an Anarchistic Theory of Knowledge (Feyerabend 2008 );

Farewell to Reason (Feyerabend 1999 );

The Tyranny of Science (Feyerabend and Oberheim 2011 ).

Indeed, Feyerabend initially gained notoriety for his anarchistic rallying cry “anything goes,” a vision of scientific anarchy that would send shivers down the spins of many scientists. In his own words (Feyerabend 2008 , p. 9):

The following essay is written in the conviction that anarchism , while perhaps not the most attractive political philosophy, is certainly excellent medicine for epistemology , and for the philosophy of science .

In essence, he observed (Feyerabend 2008 , p. 1):

The events, procedures and results that constitute the sciences have no common structure.

In Feyerabend ( 2008 ) he outlined his program for the philosophy of science. It is a very sympathetic, open-minded, idiosyncratic, and personal exposé. In an analytical index, Feyerabend offers a sketch of the main argument, summarized as a few sentences per chapter (Feyerabend 2008 , p. 5f.):

Science is an essentially anarchic enterprise: theoretical anarchism is more humanitarian and more likely to encourage progress than its law-and-order alternatives.

This is shown both by an examination of historical episodes and by an abstract analysis of the relation between idea and action. The only principle that does not inhibit progress is: anything goes .

For example, we may use hypotheses that contradict well-confirmed theories and/or well-established experimental results. We may advance science by proceeding counterinductively.

The consistency condition which demands that new hypotheses agree with accepted theories is unreasonable because it preserves the older theory, and not the better theory. Hypotheses contradicting well-confirmed theories give us evidence that cannot be obtained in any other way. Proliferation of theories is beneficial for science, while uniformity impairs its critical power. Uniformity also endangers the free development of the individual.

There is no idea, however ancient and absurd, that is not capable of improving knowledge. [...]

No theory ever agrees with all the facts in its domain, yet it is not always the theory that is to blame. Facts are constituted by older ideologies, and a clash between facts and theories may be proof of progress. [...]

[...] Copernicanism and other essential ingredients of modern science survived only because reason was frequently overruled in the past.

Neither science nor rationality are universal measures of excellence. They are particular traditions, unaware of their historical grounding.

Yet it is possible to evaluate standards of rationality and to improve them. The principles of improvement are neither above tradition nor beyond change and it is impossible to nail them down.

Science is neither a single tradition, nor the best tradition there is, except for people who have become accustomed to its presence, its benefits and its disadvantages. [...]

Of course, Feyerabend also espoused very radical and provocative ideas. For instance Feyerabend ( 2008 , p. 238):

In a democracy it [science] should be separated from the state just as churches are now separated from the state.

As expected, such utterances helped draw the wrath of the scientific community. Feyerabend was heavily criticized and vilified. However, accusations were often countered by him pointing out where he had been misinterpreted and by reemphasizing his ruthless commitment to open-mindedness. Like in the following example (Feyerabend 2008 , p. 122):

A few years ago Martin Gardner, the pitbull of scientism, published an article with the title “Anti-Science, the Strange Case of Paul Feyerabend” Critical Inquiry , Winter 1982/83. The valiant fighter seems to have overlooked these and other passages [in Against Method ]. I am not against science. I praise its foremost practitioners and (next chapter) suggest that their procedures be adopted by philosophers. What I object to is narrow-minded philosophical interference and a narrow-minded extension of the latest scientific fashions to all areas of human endeavor—in short what I object to is a rationalistic interpretation and defense of science.

Perhaps what infuriated Feyerabend’s critics the most was the adaptability of his beliefs, as one might expect from a relativist (Feyerabend 2008 , p. 268):

In a critical notice of my book Farewell to Reason Andrew Lugg suggests “that Feyerabend and likeminded social critics should treat relativism with the disdain that they normally reserve for rationalism” . This I have now done, in Three Dialogues of Knowledge where I say that relativism gives an excellent account of the relation between dogmatic world-views but is only a first step towards an understanding of live traditions, and in Beyond Reason: Essays on the Philosophy of Paul Feyerabend , where I write “relativism is as much of a chimera as absolutism (the idea that there exists an objective truth) , its cantankerous twin”. [...] In both cases I raise objections against relativism, indicating why I changed my mind and mention some of the remaining difficulties.

With refreshing candidness he confessed (quoted in Horgan 1997 , p. 50):

I have opinions that I defend rather vigorously, and then I find out how silly they are, and I give them up.

Furthermore, as one might expect, Feyerabend adhered to no systematicity in his work, often emphasizing the ad hoc and random nature of his undertakings. For instance, as seen in the analytical index (Feyerabend 2008 , p. 8f.):

  Chapter 20: The point of view underlying this book is not the result of a well-planned train of thought but of arguments prompted by accidental encounters.  

Or more generally (Feyerabend 2008 , p. 159):

“Anything goes” does not mean that I shall read every single paper that has been written—God forbid!—it means that I make my selection in a highly individual and idiosyncratic way, partly because I can’t be bothered to read, partly because I can’t be bothered to read what doesn’t interest me—and my interests change from week to week and day to day—partly because I am convinced that humanity and even Science will profit from everyone doing his own thing [...].

Perhaps Feyerabend was indeed profoundly misunderstood, a fact he himself would probably never worry about and try to amend. To illustrate, a more sympathetic reading of Feyerabend (Kidd 2011 ):

The Tyranny of Science should therefore be interpreted as Feyerabend’s attempts to dissolve conflicts and establish harmony between science, society, and philosophy, on the one hand, and between scientists, philosophers, and the public, on the other. The concerns and alarms that concerned Feyerabend are not the exclusive preserve of any of those domains—scientific, public, or philosophical—and to properly understand and address them each must cooperate with the other. Tyranny only arises when one of those would try to dominate the others, and Feyerabend’s book offers an engaging and entertaining case against such tyranny.

2 The Evolution of Science

During the meandering evolution of science many pressing issues have been raised, relating to truth, knowledge, and beliefs. Inconspicuous and commonsensical ideas of rationality, objectivity, and universality came under siege. However, more drastically, and despite the remarkable success of science in continually uncovering knowledge of the world, our idea of reality itself emerged as conceptually flawed. A paradox surfaced (Tarnas 1991 , p. 333):

For at the same time that modern man was vastly extending his effective knowledge of the world, his critical epistemology inexorably revealed the disquieting limits beyond which his knowledge could not claim to penetrate.

The envisaged role of the human being, taken to be that of the detached onlooker observing and interpreting a world of mind-independent objects, began to pose a problem. Indeed, our common sense and intuition, longing for an objective reality which can be comprehended by the human mind through unambiguous, justified knowledge of true facts, started to appear misguided. Today we know that “fundamental physics has a long history of disregarding our common sense notions” (Gefter 2012 ).

Ironically, harmless-looking anomalies Footnote 7 led to the absolutely unexpected discoveries of new realms of reality, fundamentally and irreversibly disrupting the prevailing classical worldview. One was the uncovering of the discrete nature of reality, as revealed by quantum phenomena (Sect.  4.3.4 ), the other was the finding of the malleability of space and time (special and general relativity, discussed in Sects.  3.2.1 and 4.1 and 10.1.2 , respectively). The deeper the human mind probed reality, the more outlandish the stories became that it has to tell itself about these new planes of existence. Troublingly (Tarnas 1991 , p. 358):

[...] the concepts derived from the new physics not only were difficult for the layperson to comprehend, they presented seemingly insuperable obstacles to the human intuition generally: a curved space, finite yet unbounded; a four-dimensional space-time continuum; mutually exclusive properties possessed by the same subatomic entity; objects that were not really things at all but processes or patterns of relationships; phenomena that took no decisive shape until observed; particles that seemed affect each other at a distance with no known causal link; the existence of fundamental fluctuations of energy in a total vacuum.

These issues are discussed in Chap.  10 . The possible implications are truly unsettling to the Western mind and many of humanity’s century old concepts and beliefs appear to be in danger. The ramifications of such uprooting discoveries left scars in the psyche of scientists (Tarnas 1991 , p. 356):

By the end of the third decade of the twentieth century, virtually every major postulate of the earlier scientific conception had been controverted: the atoms as solid, indestructible, and separate building-blocks of nature, space and time as independent absolutes, the strict mechanistic causality of all phenomena, the possibility of objective observation of nature. Such fundamental transformation in the scientific world picture was staggering, and for no one was this more true than the physicists themselves. Confronted with the contradictions observed in subatomic phenomena, Albert Einstein wrote: “All my attempts to adapt the theoretical foundation of physics to this knowledge failed completely. It was as if the ground had been pulled out from under one, with no firm foundation to be seen anywhere upon which one could have built.” Heisenberg similarly realized that “the foundations of physics have started moving... [and] this motion has caused the feeling that the ground would be cut from science.”

See Chap.  10 for an overview of the struggles of physicsc in general and Sect.  10.3.2 for specific introduction to the bizarre realm of the quantum.

What did other practitioners of science have to say to all of this? At first, the philosophical conundrums of the novel physical theories were acknowledged. Indeed, any understanding of the newly discovered subatomic reality appeared to require the reintegration of philosophy into science (Kaiser 2011 , p. 2):

Most of its [quantum mechanics] creators—towering figures like Niels Bohr, Werner Heisenberg, and Erwin Schrödinger—famously argued that quantum mechanics was first and foremost a new way of thinking. Ideas that had guided scientists for centuries were to be cast aside Bohr constantly spoke of the “general epistemological lessons” of the new quantum era.

However, after World War II, the philosophically inspired attempts at understanding the quantum world quickly faded, nearly vanishing during the Cold War with the emerging new rallying cry: “Shut up and calculate!” (see Sect.  2.2.1 ). With the scope of physics steadily increasing, mathematical prowess became the most vital skill, leaving not much room for grander musings. The question of what the mathematical symbols being manipulated really mean were ignored as was their relationship to reality. The attitudes scientists adopted towards philosophy now ranged between indifference and hostility. As mentioned above, some eminent physicists are on record expressing their contempt for philosophy. Needles to say, philosophers insisted on analyzing these conceptual puzzles, pestering the scientists. Understandably, it is hard to accept that the sanctity of science can be soiled by the very human nature of scientists (recall Sect.  9.1.5 ). Moreover, it is not easy to admit that (Tarnas 1991 , p. 358):

Physicists failed to come to any consensus as to how the existing evidence should be interpreted with respect to defining the ultimate nature of reality. Conceptual contradictions, disjunctions, and paradoxes were ubiquitous, and stubbornly evaded resolution. A certain irreducible irrationality, already recognized in in the human psyche, now emerged in the structure of the physical world itself.

Perhaps it is consoling to some scientists that philosophy itself also suffer in the modern era (Tarnas 1991 , p. 354):

As philosophy became more technical, more concerned with methodology, and more academic, and as philosophers increasingly wrote not for the public but for each other, the discipline of philosophy lost much of its former relevance and importance for the intelligent layperson, and thus much of its former cultural power.

2.1 The Comprehensible Universe

Of all the magnificent capabilities of the modern human mind, one is especially curious: the ability to be unimpressed by existence. While as children we are dumbfounded by the unfathomable reality encompassing us, as adults we are so often caught up in our bland daily routines that we cease to wonder. But not everyone. The philosopher Alan Watts confessed (Watts 1971 p. 23):

As Aristotle put it, the beginning of philosophy is wonder. I am simply amazed to find myself living on a ball of rock that swings around an immense spherical fire. I am more amazed that I am a maze—a complex wiggliness, an arabesque of tubes, filaments, cells, fibers, and films that are various kinds of palpitation in this stream of liquid energy.

In a similar vein, Einstein’s musings (Einstein 2007 , p. 5):

The fairest thing we can experience is the mysterious. It is the fundamental emotion which stands at the cradle of true art and science. He who knows it not and can no longer wonder, no longer feel amazement, is as good as dead, a snuffed-out candle.

Ranking second, after blissful wonder, is perhaps the realization that reality can be comprehended. Indeed, it is a striking hidden assumptions of science that the universe is understandable to the human mind. Why should the mysterious workings of the grand universe find a correspondence in our minds and hence a correlate in our brains? Why should the formal abstract thoughts systems the human mind can access—even while sitting in Plato’s cave—relate to anything in the outer world? Why is there an overlap between inner and outer structures? In other words, why does a Book of Nature exist at all and why is it written in a language the human mind can read? These issues are detailed in Part I.

This astounding fact has had an enchanting effect on some eminent scientists. For instance, the theoretical physicist, mathematician, and Nobel laureate Eugene Wigner. In 1960 he published an article with the striking title: The Unreasonable Effectiveness of Mathematics in the Natural Sciences Footnote 8 (Wigner 1960 ). There he observed:

[T]he enormous usefulness of mathematics in the natural sciences is something bordering on the mysterious and [...] there is no rational explanation for it. [I]t is not at all natural that “laws of nature” exist, much less that man is able to discover them. [T]he two miracles of the existence of laws of nature and of the human mind’s capacity to divine them. [F]undamentally, we do not know why our theories work so well.

Also Einstein did not hide his bewilderment (Isaacson 2007 , p. 462):

The eternal mystery of the world is its comprehensibility. The fact that it is comprehensible is a miracle.

Even as one of the greatest minds in physics he did not resist the temptation to express his views on science in an unscientific way which would have been scorned by many scientists had they been uttered by a lesser colleague (Einstein 1918 ):

The supreme task of the physicist is to arrive at those universal elementary laws from which the cosmos can be built up by pure deduction. There is no logical path to these laws; only intuition, resting on sympathetic understanding of experience, can reach them.

Einstein continues (Einstein 1918 ):

The state of mind which enables a man to do work of this kind [science] is akin to that of the religious worshiper or the lover; the daily effort comes from no deliberate intention or program, but straight from the heart.

Also the great cosmologist Stephen Hawking was tempted to dive deep into the metaphysical underbelly (Hawking 2008 , p. 142):

What is it that breathes fire into the equations and makes a universe for them to describe?

To escape such metaphysical and existential challenges, scientists have been know to invoke the concept of beauty or some kind of divinity. Recall the words of the theoretical physicist and Nobel laureate Steven Weinberg from Sect.  4.4 :

We believe that, if we ask why the world is the way it is and then ask why that answer is the way it is, at the end of this chain of explanations we shall find a few simple principles of compelling beauty.

Again, Einstein (Isaacson 2007 , p. 388f.):

I believe in Spinoza’s God, who reveals himself in the harmony of all that exists, not in a God who concerns himself with the fate and the doings of mankind.

The rationalist philosopher Baruch Spinoza offered a vision of God as the essence of the universe (Nadler 2016 ):

God is the infinite, necessarily existing (that is, uncaused), unique substance of the universe. There is only one substance in the universe; it is God; and everything else that is, is in God.

Some have described such an understanding of God as pantheistic while others have seen links to Hinduism (Van Bunge and Klever 1996 ). In Einstein’s own words (Frankenberry 2008 , p. 147):

Every one who is seriously involved in the pursuit of science becomes convinced that a spirit is manifest in the laws of the Universe—a spirit vastly superior to that of man, and one in the face of which we with our modest powers must feel humble.

Einstein would later define a principle of cosmic religion, see Sect.  15.3.1 .

Even though Einstein’s intuition was so acute that it allowed him to access and uncover new facets of reality that were unimaginable before him, he appeared to have hit a dead end while pondering quantum phenomena. Not only did he reject their reality, tragically, his attempts at an alternative formulation would preoccupy his mind in vain until his death (see Sects.  4.3.4 , 10.3.2.1 , and 4.3.5 ). Perhaps this next quote from him best summarizes the inner turmoil felt by the practitioners of science (quoted in Hoffmann and Dukas 1973 , p. vii):

One thing I have learned in a long life: That all our science, measured against reality, is primitive and childlike—and yet it is the most precious thing we have.

Moreover, Einstein held the following personal conviction (quoted in Dukas and Hoffmann 2013 , p. 39):

What I see in Nature is a magnificent structure that we can comprehend only very imperfectly, and that must fill a thinking person with a feeling of humility.

Returning to philosophy, one may ask the following question: What if the observable and comprehensible universe is only a slice of the totality of reality? What if the fabric of reality is vastly richer than we can perceive and fathom? Such a concession would allow for the notions of teleology and entelechy to enter the picture as explanatory templates, without the need to invoke the divine. Such musings are entertained in Part III.

2.2 The End of Science?

In the history of science, there have been many occasions where it was believed that nearly all of the workings of the universe had been decoded. Again and again, tempted by the dream of being only a small step away from a complete description of nature, scientists have made exuberant claims. For instance Graham et al. ( 1983 , p. 38):

Indeed, it seemed to some physicists in the closing year of the nineteenth century that taken together, Newton’s celestial mechanics and Maxwell’s equations indicated that the prospect of completing physics was in sight.

In the words of the eminent experimentalist Albert A. Michelson in 1894 (quoted in Graham et al. 1983 , p. 38):

While it is never safe to affirm that the future of physical science has no marvels in store even more astonishing than those of the past, it seems probable that most of the grand underlying principles have been firmly established and that further advances are to be sought chiefly in the rigorous application of these principles to all the phenomena which come under our notice. It is here that the science of measurement shows its importance—where quantitative work is more to be desired than qualitative work. An eminent physicist remarked that the future truths of physical science are to be looked for in the sixth place of decimals.

Then, in 1920 (Hawking 1980 , p. 1):

[...] Max Born told a group of scientists visiting Göettingen that “Physics, as we know it, will be over in six months.”

In 1980, Stephen Hawking gave his inaugural lecture as Lucasian professor of mathematics at the University of Cambridge, England, titled Is the End in Sight for Theoretical Physics? He opened with (Hawking 1980 ):

In this lecture I want to discuss the possibility that the goal of theoretical physics might be achieved in the not too far future, say, by the end of the century. By this I mean that we might have a complete, consistent and unified theory of the physical interactions which would describe all possible observations.

Ten years later Hawking updated his prediction: “Give it twenty or twenty-five years” (Ferguson 2011 , p. 214; see also Sect.  4.3.2 ). However, in 1998 the optimism started to diminish (Smith 2016 ):

It doesn’t look as if we are going to quite make it.

Finally, in 2002 (Hawking 2002 ):

Some people will be very disappointed if there is not an ultimate theory, that can be formulated as a finite number of principles. I used to belong to that camp, but I have changed my mind.

The end of science, in the sense that everything there is to know became known to the human mind, never transpired. The science journalist John Horgan took a more sinister take on the end of science in his book of the same name (Horgan 1997 ). He argued that science is loosing its momentum to uncover knowledge, slowly grinding to a halt. For the book, he interviewed prominent scientists and philosophers. The likes of Popper, Kuhn, Feyerabend, Daniel Dennett, Hawking, Weinberg, Feynman, Dyson, Roger Penrose, Murray Gell-Mann, Sheldon Glashow, Edward Witten, John Wheeler, David Bohm, Philip Anderson, Ilya Prigogine, Mitchell Feigenbaum, Gregory Chaitin, John Casti, Francis Crick, Richard Dawkins, Stuart Kauffman, and Edward O. Wilson. Horgan identifies the demise of progress in

the end of philosophy;

the end of physics;

the end of cosmology;

the end of evolutionary biology;

the end of social science;

the end of neuroscience;

the end of chaoplexity (the portmanteau of chaos and complexity);

the end of machine science.

Naturally, many people were not amused. The biologist Lynn Margulis perhaps captured this best (quoted in Horgan 2015 ):

He’s a very nice guy and he wrote a very bad book.

Looking back, Horgan assesses (Horgan 2015 ):

The re-launch [Basic Book’s 2015 edition of The End of Science ] has stirred up many memories—and forced me to evaluate my thesis. My book has now sustained almost two decades worth of attacks, some triggered by genuine scientific advances, from the completion of the Human Genome Project to the discovery of the Higgs boson. So do I take anything back? Hell no.

In a nutshell, taken from the preface of the new edition (Horgan 2015 ):

Our descendants will learn much more about nature, and they will invent gadgets even cooler than smart phones. But their scientific version of reality will resemble ours, for two reasons: First, ours [...] is in many respects true; most new knowledge will merely extend and fill in our current maps of reality rather than forcing radical revisions. Second, some major remaining mysteries—Where did the universe come from? How did life begin? How, exactly, does a chunk of meat make a mind?—might be unsolvable.

Indeed, today, we are still waiting for a coherent and unified theory describing the physical world. Or, at least, a theory of quantum gravity (Sect.  10.2 ). Unfortunately, the outlook is as bleak as ever and the understanding of ourselves and the world we live in continues to run into dead ends. It is as if nature enjoys teasing the human mind, by pretending to reveal her workings, only to present us with the next enigmas and then turn away. Science has become like the Red Queen in Lewis Carroll’s writings about Alice’s adventures in wonderland: by running faster and faster she stays at the same place. Similarly, science discovers more and more knowledge without fundamentally progressing anymore. For an assessment of the status of modern theoretical physics see Baggott ( 2013 ), Unzicker and Jones ( 2013 ).

Furthermore, science has been beset by various crisis. For instance, the reproducability crisis (Baker 2016 ):

More than 70% of researchers have tried and failed to reproduce another scientist’s experiments, and more than half have failed to reproduce their own experiments. Those are some of the telling figures that emerged from Nature’s survey of 1,576 researchers who took a brief online questionnaire on reproducibility in research.

The application of bad statistics (Nieuwenhuis et al. 2011 ) has also raised questions. Analyzing 513 papers published in five prestigious neuroscience journals over two years, 157 studies where identified where a potential statistical fallacy could have been committed. Indeed, out of these publications, 50% contained the error. Generally, the whole notion of statistical significance can be called into question (Ziliak and McCloskey 2008 ). Moreover, as science advances, it relies more and more heavily upon very complex machinery and highly sophisticated software. This can be another source of error. For instance, a bug found in the software used by researchers to interpret fMRI data was found to result in false positive rates up to 70%, calling 15 years of research into question (Reynolds 2016 ). Some researchers have even alleged that “most published research findings are false” (Ioannidis 2005 ). The observation, that the number of scientific retractions is increasing (Steen et al. 2013 ), could be a sign of scientific self-correction or simply the result of poor scientific practices (Smaldino and McElreath 2016 ).

However, perhaps the biggest threat to science are the scientists themselves. Like any other social human endeavor, academia can be plagued by blind obedience to authority, Footnote 9 groupthink, corruption, and fraud. Furthermore, the unrelenting pressure to “publish or perish” expects scientists to be inexhaustible creative content-providers—with very possible negative consequences (Smaldino and McElreath 2016 ). The following anecdotes highlights some of these problems.

A publication in the prestigious Proceedings of the National Academy of Sciences ( PNAS ) claimed to have detected a universal pattern in how complex systems organize (Preis et al. 2011 ). Specifically, the authors reported on scaling laws found in financial data. Footnote 10 The study became famous not only among quantitative analysts. However, when the physicist, econophysicist, and complexity scientist Didier Sornette reproduced the study utilizing purely random data, surprisingly, the same patterns emerged. A “selection of biased statistical subsets of realizations in otherwise featureless processes such as random walks” (Filimonov and Sornette 2012 ) was responsible for the signal. In other words, the publication was meaningless, as the researchers did not reject the null hypothesis. To his utter dismay, when Sornette submitted these findings to PNAS , his paper was rejected. In an open letter he vented his frustration Footnote 11 :

Dear Editor, As a coauthor of the paper Spurious switching processes in financial markets that you just rejected, I cannot remain silent and have to express my concern with how science is handled in general in journals such as PNAS . You are not alone as Science and Nature have in general the same reactions. I know that you will not change your mind in this instance, but I do hope that, little by little, the whole editorial community may become a bit wiser over time. In a nutshell, your policy stating “it would have to go beyond a simple refutation of the earlier work and significantly add to the field”, implies 1. a fundamental error can remain published as “truth” in PNAS without the normal debate that should be the domain of real science. In my opinion, this is especially harmful to Science, given that this specific spurious claim for discovery has been highly publicized in different journals, in the media and many conferences. 2. a paper that does the solid work to demonstrate that the spurious claim is unsupported will most likely be considered as “not adding significantly to the field”. In other words, we can add “shit” to the field but we cannot correct and remove “shit” from the field, and in so doing teach how to develop better statistical tests. [...] I hope that any editor could realize the moral hazard and wrong incentives permeating more and more the sociology of science encouraged by editors such as you (no personal attack, I know that you are just following “orders” of a general stance dictated by editorial boards of journals), in a way analogous to a graft from the scandalous behaviors observed in the financial industry. Excuse my strong colorful words, but I consider that they convey my shock and repulsion to what I consider a violation of good scientific endeavor. Sincerely, Prof. D. Sornette

In 1956, two researchers applied a recently developed technique for analyzing human cells and counted 46 chromosomes. This was puzzling, as everyone familiar with biology knew that the correct answer was, since 1912, 48. After consulting with colleagues, it emerged that, surprisingly, other researcher had encountered the same problem. Some even stopped their work prematurely, as they could only find 46 of the 48 chromosomes which had to be there. Not our two researchers, who boldly, and correctly, claimed that everyone else was wrong. See Arbesman ( 2012 ). In a similar vein, albeit more trivial, how many scientists know the following (Dicken 2018 , back cover):

When Galileo dropped cannonballs from the top of the Leaning Tower of Pisa, he did more than overturn centuries of scientific orthodoxy. At a stroke, he established a new conception of the scientific method based upon careful experimentation an rigorous observation [...]. The problem is that Galileo never performed his most celebrated experiment in Pisa. In fact, he rarely conducted any experiments at all.

Also recall Sect.  5.3.1 describing the initially favorable relationship Galileo and the Church enjoyed.

Finally, it is worth noting that science has no intrinsic aim, other than blind advancement, and is also not goal-driven. Kuhn famously and influentially argued that sciences progresses by sudden, unforeseeable disruptions. Initially, he viewed these paradigm shifts in science (recall Sect.  9.1.3 ) as being based on faith, fashion, and peer pressure, where evidence and reason only play a minor role. Moreover, he believed science was largely a non-rational activity. Kuhn later moderated his tone and offered a less radical vision of his ideas. For instance, the notion that there exist no algorithms for theory choice in science—scientific progress is inherently opaque. See Okasha ( 2002 ). In any case, how free are scientists really to steer the direction of research? As an example (Harari 2015 , p. 303):

During the past 500 years modern science has achieved wonders thanks largely to the willingness of governments, businesses, foundations and private donors to channel billions of dollars into scientific research. [...] Why did the billions start flowing from government and business coffers into labs and universities? In academic circles, many are naive enough to believe in pure science. They believe the government and businesses altruistically give them money to pursue whatever research projects strike their fancy.

Essentially (Harari 2015 , p. 304):

Scientists themselves are not always aware of the political, economic and religious interests that control the flow of money; many scientists do, in fact, act out of pure intellectual curiosity. However, only rarely do scientists dictate the scientific agenda.

Today, many scientists feel a lot of pressure, as they see the amount of scientific funding declining globally. More and more time is spent drafting funding proposals, which can drain a lot of resources from research (Powell 2016 ). Aspects relating to marketing and bureaucracy become relevant. Will science simply come to an end because the general population fails to see its benefits anymore and many politicians will thus be happy to pull the plug? A very real concern, in our post-truth world, where a climate of rising populism sees experts as a threat.

2.3 The Fractal Nature of Knowledge

The epitome of scientific progress is recounted by the theoretical physicist Sidney Coleman (quoted in Moriyasu 1983 , p. 119):

There is a popular model of a breakthrough in theoretical physics. A field of physics is afflicted with a serious contradiction. Many attempts are made to resolve the contradiction; finally, one succeeds. The solution involves deep insights and concepts previously thought to have little or nothing to do with the problem. It unifies old phenomena and predicts unexpected (but eventually observed) new ones. Finally, it generates new physics: the methods used are successfully extended beyond their original domain.

While such upheavals were common in the past, today they have become exceedingly rare events. The increments at which science progresses appear to be becoming infinitesimal. Knowledge seems to posses a fractal-like nature, akin to an abstract space into which the human mind can zoom in indefinitely and the richness of structure does not diminish.

This paradox has been observed by some thinkers. In the words of Deutsch ( 2011 , p. 64):

The deeper an explanation is, the more new problems it creates. That must be so, if only because there can be no such thing as an ultimate explanation: just as “the gods did it” is always a bad explanation, so any other purported foundation of all explanations must be bad too.

Similarly, Popper’s eloquent prose, taken from Popper ( 1992 , p. 8):

I think there is only one way to science—or to philosophy, for that matter: to meet a problem, to see its beauty and fall in love with it; to get married to it and to live with it happily, till death do ye part—unless you should meet another and even more fascinating problem or unless, indeed, you should obtain a solution. But even if you do obtain a solution you may then discover, to your delight, the existence of a whole family of enchanting, though perhaps difficult, problem children.

In his widely acclaimed bestselling novel Zen and the Art of Motorcycle Maintenance , which was rejected by over hundred publishers, Robert M. Pirsig addresses related issues. The book “should in no way be associated with that great body of factual information relating to orthodox Zen Buddhist practice,” neither is it “very factual on motorcycles” (Pirsig 1981 , Author’s Note). It is, however, a miniature study of the art of rationality itself. Pirsig argues that although thought may find truth, it may not be valid for all experiences. Again, the closer one examines a phenomenon, the more perplexing it becomes as every explanation seems to open the door to countless new puzzles (Pirsig 1981 , p. 101):

The more you look, the more you see. [A]s you try to move toward unchanging truth through the application of scientific method, you actually do not move toward it at all. You move away from it! It is your application of scientific method that is causing it to change!

Finally (Pirsig 1981 , p. 101):

Through multiplication upon multiplication of facts, information, theories and hypotheses, it is science itself that is leading mankind from single absolute truths to multiple, relative ones.

These words have a distinct postmodern ring to them. Finally, Wheeler, offers a haunting paradox (quoted in Horgan 1997 , p. 84):

At the heart of everything is a question, not an answer. When we peer down into the deepest recesses of matter or at the farthest edge of the universe, we see, finally, our own puzzled faces looking back at us.

3 The Practitioners of Science

Usually, scientists aren’t very vocal about their personal experiences of practicing science. Science is a craft not to be burdened with intangible and immaterial overhead—in stark contrast to philosophers, whose trade it is to unearth vexing issues relating to the nature of knowledge, reality, and the human mind. The problem with knowing what beliefs scientists hold dear is that, by definition, this information is non-scientific. Hence, one searches for such revelations in the peer-reviewed literature without avail. Footnote 12 Sometimes though, scientists will give a glimpse of their inner worlds in popular science books they author. Other times, they are explicitly asked to reveal their very personal ideas about the universe.

In 1988, the literary agent and author John Brockman founded the Edge Foundation . Footnote 13 It has become an intellectual platform for scientists and deep thinkers to directly convey their thoughts to the public in a readily accessible manner to non-specialist. The tag-line on their website reads:

To arrive at the edge of the world’s knowledge, seek out the most complex and sophisticated minds, put them in a room together, and have them ask each other the questions they are asking themselves.

Since 1998, the Edge poses an annual question Footnote 14 to a diverse group of physicists, mathematicians, biologists, computer scientists, philosophers, etc. In 2005, the question was: What do you believe is true even though you cannot prove it? The compilation of the answers was published as a book edited by Brockman, titled What We Believe but Cannot Prove: Today’s Leading Thinkers on Science in the Age of Certainty (Brockman 2006 ). Ever since, the annual question has resulted in a book:  

What is your dangerous idea? (Brockman 2007a )

What are you optimistic about? (Brockman 2007b )

What have you changed your mind about? (Brockman 2009 )

What will change everything? (Brockman 2010 )

How is the Internet changing the way you think? (Brockman 2011 )

What scientific concept would improve everybody’s cognitive toolkit? (Brockman 2012 )

What is your favorite deep, elegant, or beautiful explanation? (Brockman 2013 )

What should we be worried about? (Brockman 2014 )

What scientific idea is ready for retirement? (Brockman 2015a )

What do you think about machines that think? (Brockman 2015b )

What do you consider the most interesting recent [scientific] news? (Brockman 2016 )

What scientific term or concept ought to be more widely known? (Brockman 2017 )

What is the last question?

Browsing through these book will give the reader insights into the amazing diversity and creativity of ideas. Naturally, many of the revealed beliefs are polar opposites—divergence and contradictions abound. Nonetheless, in such moments of honesty and intimacy we can glimpse behind the scenes and gauge the minds of contemporary intellectuals. Footnote 15 What becomes apparent is that many thinkers can acknowledge limits in knowledge and accept uncertainty and ambiguity—and even ignorance. Looking back at the spectacular success of human knowledge generation (see Part I), we are, somewhat anxiously, anticipating a future, where from the borders of knowledge radical and transcending new visions of the true nature of reality and consciousness are expected to emerge (see Part III).

3.1 On Philosophy

Sam Harris (Brockman 2015a ):

Search your mind, or pay attention to the conversations you have with other people, and you will discover that there are no real boundaries between science and philosophy. We must abandon the idea that science is distinct from the rest of human rationality.

Rebecca Newberger Goldstein (Brockman 2015a ):

You can’t argue for science making philosophy obsolete without indulging in philosophical arguments. You’re going to need to argue, for example, for a clear criterion for distinguishing between scientific and non-scientific theories of the world. A triumphalist scientism needs philosophy to support itself.

Paul Bloom (Brockman 2012 ):

Scientists can reject common wisdom, they can be persuaded by data and argument to change their minds. It is through these procedures that we have discovered extraordinary facts about the world, such as the structure of matter and the evolutionary relationship between monkey and man. The cultivation of reason isn’t unique to science; other disciplines such as mathematics and philosophy possess it as well. But it is absent in much of the rest of life.

Melanie Swan (Brockman 2013 ):

Therefore some of the best explanations may have the parameters of being intuitively beautiful and elegant, offering an explanation for the diverse and complicated phenomena found in the natural universe and human-created world, being universally applicable or at least portable to other contexts, and making sense of things at a higher order. Fields like cosmology, philosophy, and complexity theory have already delivered in this exercise: they encompass many other science fields in their scope and explain a variety of micro and macro scale phenomena.

3.2 On Objectivity, Truth, Knowledge, and Certainty

Gavin Schmidt (Brockman 2015a ):

We continually read about the search for the one method that will allow us to cut through the confusion, the one piece of data that tell us the “truth” , or the final experiment that will “prove” the hypothesis. But almost all scientists will agree that these are fool’s errands—that science is [a] method for producing incrementally more useful approximations to reality, not a path to absolute truth.

Mihaly Csikszentmihalyi (Brockman 2015a ):

What needs to be retired is the faith that what scientists say are objective truths, with a reality independent of scientific claims. Some are indeed true, but others depend on so many initial conditions that they straddle the boundary between reality and fiction.

Scott Sampson (Brockman 2015a ):

One of the most prevalent ideas in science is that nature consists of objects. Yet this pervasive, centuries-old trend toward reductionism and objectification tends to prevent us from seeing nature as subjects, though there’s no science to support such myopia.

Alan Kay (Brockman 2006 ):

When we guess in science we are guessing about approximations and mappings to languages, we are not guessing about “the truth” (and we are not in a good state of mind for doing science if we think we are guessing “the truth” or “finding the truth”) . This is not at all well understood outside of science, and there are unfortunately a few people with degrees in science who don’t seem to understand it either.

Timothy Taylor (Brockman 2006 ):

If science fetishized truth, it would be religion, which it is not.

Michael Shermer (Brockman 2006 ):

Our knowledge of nature remains provisional because we can never know if we have final Truth. In science, knowledge is fluid and certainty fleeting.

Clifford Pickover (Brockman 2014 ):

Should we be so worried that we will not really be able to understand subatomic physics, quantum theory, cosmology, or the deep recesses of mathematics and philosophy? Perhaps we can let our worries slightly recede and just accept our models of the universe when they are useful.

Nicholas G. Carr (Brockman 2017 ):

But what if our faith in nature’s knowability is just an illusion, a trick of the overconfident human mind?

Lawrence M. Krauss (Brockman 2017 ):

Nothing feels better than being certain, but science has taught us over the years that certainty is largely an illusion. In science, we don’t “believe” in things, or claim to know the absolute truth.

3.3 On Laws of Nature, Reality, and Science

Lawrence M. Krauss (Brockman 2015a ):

[T]he laws of nature we measure may be totally accidental, local to our environment (namely our Universe), not prescribed with robustness by any universal principle, and by no means generic or required. [T]here may be nothing fundamental whatsoever about the “fundamental” laws we measure in our universe. They could simply be accidental. Physics becomes, in this sense, an environmental science.

Gregory Benford (Brockman 2009 ):

I once thought that the laws of our universe were unquestionable, in that there was no way for science to address the question. Now I’m not so sure. Can we hope to construct a model of how laws themselves arise?

Charles Seife (Brockman 2009 ):

Science is about freedom of thought, yet at the same time it imposes a tyranny of ideas.

Colin Tudge (Brockman 2009 ):

I have changed my mind about the omniscience and omnipotence of science. I now realize that science is strictly limited, and that it is extremely dangerous not to appreciate this.

Haim Harari (Brockman 2009 ):

The public thinks, incorrectly, that science is a very accurate discipline where everything is well defined.

Donald D. Hoffman (Brockman 2015a ):

Observation is the empirical foundation of science. The predicates of this foundation, including space, time, physical objects and causality, are a species-specific adaptation, not an insight.

Ian Bogost (Brockman 2015a ):

To think that science has a special relationship to observations about the material world isn’t just wrong, it’s insulting. But ironically, in its quest to prove itself as the supreme form of secular knowledge, science has inadvertently elevated itself into a theology. Science is not a practice so much as it is an ideology.

Satyajit Das (Brockman 2015a ):

While not strictly a scientific theorem, anthropocentrism, the assessment of reality through an exclusively human perspective, is deeply embedded in science and culture. Like a train that can only run on tracks that determine direction and destination, human knowledge may ultimately be constrained by what evolution has made us. Science, paradoxically, seems to also have inbuilt limits. Like an inexhaustible Russian doll, quantum physics is an endless succession of seemingly infinitely divisible particles. Werner Heisenberg’s uncertainty principle posits that human knowledge about the world is always incomplete, uncertain and highly contingent. Kurt Gödel’s incompleteness theorems of mathematical logic establish inherent limitations of all but the most trivial axiomatic systems of arithmetic.

Sarah Demers (Brockman 2015a ):

Of course, including aesthetic considerations in the scientific toolbox has resulted in huge leaps forward. At this stage, with 96% of the universe’s content in the dark, it is a mistake for us to put aesthetic concerns in the same realm as contradictions when it comes to theoretical motivation. With no explanation for dark energy, no confirmed detection of dark matter and no sufficient mechanism for matter/antimatter asymmetry, we have too many gaps to worry about elegance.

Max Tegmark (Brockman 2009 ):

After all, physical reality has turned out to be very different from how it seems, and I feel that most of our notions about it have turned out to be illusions. From your subjectively perceived frog perspective, the world turns upside down when you stand on your head and disappears when you close your eyes, yet you subconsciously interpret your sensory inputs as though there is an external reality that is independent of your orientation, your location and your state of mind.

Jean Paul Schmetz (Brockman 2006 ):

[...] our body of scientific knowledge is surely full of statements we believe to be true but will eventually be proved to be false.

Donald D. Hoffman (Brockman 2016 ):

Nobel Laureate David Gross observed, “Everyone in string theory is convinced...that spacetime is doomed. But we don’t know what it’s replaced by.” Fields medalist Edward Witten also thought that space and time may be “doomed.” Nathan Seiberg of the Institute for Advanced Study at Princeton said, “I am almost certain that space and time are illusions. These are primitive notions that will be replaced by something more sophisticated.”

Tor Nørretranders (Brockman 2010 ):

The visual world, what we see, is an illusion, but then a very sophisticated one. There are no colours, no tones, no constancy in the “real” world, it is all something we make up. We do so for good reasons and with great survival value.

3.4 On Ignorance and Irrationality

Paul Saffo (Brockman 2015a ):

The science establishment justifies its existence with the big idea that it offers answers and ultimately solutions. But privately, every scientist knows that what science really does is discover the profundity of our ignorance.

Robert Provine (Brockman 2015a ):

We fancy ourselves intelligent, conscious and alert, and thinking our way through life. This is an illusion. We are deluded by our brain’s generation of a sketchy, rational narrative of subconscious, sometimes irrational or fictitious events that we accept as reality.

Tom Griffiths (Brockman 2015a ):

And when psychology experiments show that people are systematically biased in the judgments they form and the decisions they make, we begin to question human rationality.

Alex Pentland (Brockman 2015a ):

It is time that we dropped the fiction of individuals as the unit of rationality, and recognized that we are embedded in the surrounding social fabric.

Margaret Wertheim (Brockman 2006 ):

In truth our ignorance is vast—and personally I believe it will always be so.

Dylan Evans (Brockman 2017 ):

If we could represent the knowledge in any given brain as dry land, and ignorance as water, then even Einstein’s brain would contain just a few tiny islands scattered around in a vast ocean of ignorance. Yet most of us find it hard to admit how little we really know.

3.5 On the Mind

Susan Blackmore (Brockman 2015a ):

Consciousness is not some weird and wonderful product of some brain processes but not others. Rather, it is an illusion constructed by a clever brain and body in a complex social world. We can speak, think, refer to ourselves as agents and so build up the false idea of a persisting self that has consciousness and free will.

Jerry A. Coyne (Brockman 2015a ):

In short, the traditional notion of free will—defined by Anthony Cashmore as “a belief that there is a component to biological behavior that is something more than the unavoidable consequences of the genetic and environmental history of the individual and the possible stochastic laws of nature”—is dead on arrival.

Tania Lombrozo (Brockman 2015a ):

In our enthusiasm to find a scientifically-acceptable alternative to dualism, some of us have gone too far the other way, adopting a stark reductionism. Understanding the mind is not just a matter of understanding the brain.

Bruce Hood (Brockman 2015a ):

We know that the self is constructed because it can be so easily deconstructed through damage, disease and drugs. It must be an emergent property of a parallel system processing input, output and internal representations. It is an illusion because it feels so real, but that experience is not what it seems.

Daniel Goleman (Brockman 2009 ):

Science found that, compared to novices, highly adept meditators generated far more high-amplitude gamma wave activity—which reflects finely focused attention—in areas of the prefrontal cortex while meditating. The seasoned meditators in this study—all Tibetan lamas—had undergone cumulative levels of mental training akin to the amount of lifetime sports practice put in by Olympic athletes: 10,000 to 50,000 hours. Novices tended to increase gamma activity by around 10 to 15 percent in the key brain area, while most experts had increases on the order of 100 percent from baseline. What caught my eye in this data was not this difference between novices and experts (which might be explained in any number of ways, including a self-selection bias), but rather a discrepancy in the data among the group of Olympic-level meditators. Although the experts’ average boost in gamma was around 100 percent, two lamas were “outliers” : their gamma levels leapt 700 to 800 percent. This goes far beyond an orderly dose-response relationship—these jumps in high-amplitude gamma activity are the highest ever reported in the scientific literature apart from pathological conditions like seizures. Yet the lamas were voluntarily inducing this extraordinarily heightened brain activity for just a few minutes at a time—and by meditating on “pure compassion,” no less. I have no explanation for this data, but plenty of questions. At the higher reaches of contemplative expertise, do principles apply (as the Dalai Lama has suggested in dialogues with neuroscientists) that we do not yet grasp? If so, what might these be? In truth, I have no idea. But these puzzling data points have pried open my mind a bit as I’ve had to question what had been a rock-solid assumption of my own.

Lutz et al. ( 2004 ) is the publication Goleman is referring to here. See also Sect.  7.4.2.1 for an account of Matthieu Ricard, a molecular geneticist turned Buddhist monk, displaying exceptional powers of self-awareness and control, in the context of compassion and meditation.

3.6 And More

W. Daniel Hillis (Brockman 2015a ):

The cause-and-effect paradigm works particularly well when science is used for engineering, to arrange the world for our convenience. In this case, we can often set things up so that the illusion of cause-and-effect is almost a reality. The notion of cause-and-effect breaks down when the parts that we would like to think of as outputs affect the parts that we would prefer to think of as inputs. The paradoxes of quantum mechanics are a perfect example of this, where our mere observation of a particle can “cause” a distant particle to be in a different state. Of course there is no real paradox here, there is just a problem with trying to apply our storytelling framework to a situation where it does not match.

Nigel Goldenfeld (Brockman 2015a ):

If the stuff that makes the universe is strongly connected in space and not usefully thought of as the aggregate of its parts, then attributing a cause of an event to a specific component may not be meaningful either. Just as you can’t attribute the spin of a proton to any one of its constituents, you can’t attribute an event in time to a single earlier cause. Complex systems have neither a useful notion of individuality nor a proper notion of causality.

Marcelo Gleiser (Brockman 2015a ):

The trouble starts when we take this idea too far and search for the Über -unification, the Theory of Everything, the arch-reductionist notion that all forces of nature are merely manifestations of a single force. This is the idea that needs to go. And I say this with a heavy heart; my early career aspirations and formative years were very much fueled by the impulse to unify it all. Why do so many insist in finding the One in Nature while Nature keeps telling us it’s really about the Many? For one thing, the scientific impulse to unify is crypto-religious. The West has bathed in monotheism for thousands of years [...]. The belief is that nature’s ultimate code exists in the ethereal world of mathematical truths and we can decipher it. Recent experimental data has been devastating to such belief [...]. We may hold perfection in our mind’s eye as a sort of ethereal muse. Meanwhile nature is out there doing its thing. That we manage to catch a glimpse of its inner workings is nothing short of wonderful.

Marcelo Gleiser (Brockman 2009 ):

The model of unification, which is so aesthetically appealing, may be simply this, an aesthetically appealing description of Nature, which, unfortunately, doesn’t correspond to physical reality. Nature doesn’t share our myths.

4 The Limits of Mathematics

The metaphor of the Book of Nature relies on the assumption that mathematics is the sole source of all exact knowledge of the world. By translating aspects of the physical world into formal abstractions, the human mind can unlock novel understanding of the workings of the universe (Sect.  2.1 ). Indeed, mathematical beauty was understood as a guiding principle in physics and a seemingly simple principle of symmetry unearthed some of the deepest understanding of reality (Chaps.  3 and 4 ). Platonism is the notion that a realm of perfect abstractions exists where all mathematical entities reside. In other words, mathematics has its own reality. In this sense, mathematics is discovered and not invented by the human mind. Notwithstanding the philosophical issues which are implied (Sect.  2.2.1 ), many of the greatest mathematicians were and are self-proclaimed Platonists (Sect.  2.2 ).

4.1 Inherent Randomness

Similar to the decline of science and philosophy chronicled in this chapter, mathematics also experienced a demotion. Ironically, at a time when mathematicians were establishing the foundations of mathematics, based on a complete set of consistent axioms, Footnote 16 disaster struck. Out of nowhere, Kurt Gödel—also a defender of mathematical Platonism—destroyed any hopes of establishing a solid foundation of mathematics. His incompleteness theorems proved that every formal axiomatic system containing basic arithmetic is inconsistent and incomplete (Sect.  2.2 ). In other words, the basic expectations, that a statement is true because there is a proof of the statement and that if a statement is true there is a proof of the statement, are—to everyone’s dismay—untenable.

Building on Gödel’s work, Alan Turing expanded the scope of the conundrum to computation (Turing 1936 ). In essence, the uncertainty discovered by Gödel now spread and plagued the mathematical foundations of the newly emerging computer science. Turing’s so-called halting problem is about undecidability. It is impossible for a computer to decide in advance whether a given program will ever finish its task and halt. The only way to find out if a program will ever halt is to run it and wait—ten minutes, ten billion years, or forever. See also Sect.  13.1.2 .

Decades later, the mathematician and computer scientist Gregory Chaitin continued where Turing left off, yet again extending Gödel’s haunting legacy. He translated Turing’s question, of whether a program halts, into a real number between 0 and 1. In essence, this uncomputable number—called Omega—reflects the probability that an arbitrary computer program will eventually halt (Chaitin 1975 ). “It’s the outstanding example of something which is unknowable in mathematics,” Chaitin says (quoted in Chown 2001 ).

Unfortunately, Omega is more than an academic curiosity. It is not some esoteric number appearing at the fringes of mathematics. Chaitin’s halting probability is intimately linked to simple mathematical operations, such as the addition and multiplication of whole numbers. Randomness lurks at the heart of mathematics. After decades of fundamental research, the verdict is out (Calude and Chaitin 1999 ):

[...] randomness is as fundamental and as pervasive in pure mathematics as it is in theoretical physics. In our opinion it also gives further support to “experimental mathematics”, and to the “quasi-empirical” view of mathematics which says that although mathematics and physics are different, it is more a matter of degree than black and white.

For millennia, people have regarded mathematics as an outstanding intellectual construction of humankind. Mathematics was viewed as the pinnacle of rational thinking and human reasoning. Alas, today we know, as explained in the words of Chaitin, that (quoted in Chown 2001 ):

Mathematicians are simply acting on intuition and experimenting with ideas, just like everyone else. Zoologists think there might be something new swinging from branch to branch in the unexplored forests of Madagascar, and mathematicians have hunches about which part of the mathematical landscape to explore. The subject is no more profound than that. Most of mathematics is true for no particular reason. Maths is true by accident.

Chaitin’s mathematical curse grows worse. There exist even more disturbing numbers, called Super-Omegas (Becher et al. 2001 ). All these “incalculable numbers reveal that mathematics is not simply moth-eaten, it is mostly made of gaping holes. Anarchy, not order, is at the heart of the Universe” (Chown 2001 ). This is a truly unexpected and devastating blow to the supremacy of mathematics and any intellectual tradition building upon it. Indeed (Chaitin 2005 , p.146):

Let me repeat: formal axiomatic systems are a failure!

There exists a real-world problem related to randomness. In 1930, the philosopher, mathematician, and economist Frank P. Ramsey proved an innocuous theorem in graph theory (Ramaey 1930 ). In detail, the proof concerned itself with the relationship between groups of points in a network. This turned out to have deep implications, as a “network” can be a collection of all manner of things, from computers in an network, people at a dinner party, or stars in the night sky. In essence, pattern-free randomness is impossible. Every random collection of things will contain patterns: mysterious order emerges from apparent randomness (indeed, recall Benford’s law from Sect.  6.4.2 ). Ramsey theory says that this order is not only likely, but becomes inevitable as the number of nodes in the network increases. Adding insult to injury, our minds suffer from apophenia, a cognitive bias describing the tendency to perceive connections and meaning between unrelated things (see Sect.  11.3.2 for more on cognitive biases) . We are truly exposed to a profound randomness/pattern dichotomy: from the fundamental randomness in the theories the human mind devises, to the pattern-formation emerging out of randomness, to the mind’s propensity to see patterns everywhere—the illusion of order in a random universe.

A final example of the failings of mathematics relates to politics. Specifically, clear and justified rules for apportionments in a political system produce results which are unexpected and appear to violate common sense. Such deficiencies are summarized in the apportionment paradoxes and question rational decision-making (Arrow 1950 ; Balinski and Young 1975 , 2001 ). Indeed (Deutsch 2011 , p. 333):

Sometimes politicians have been so perplexed by the sheer perverseness of apportionment paradoxes that they have been reduced to denouncing mathematics itself. Representative Roger Q. Mills of Texas complained in 1882, ’I thought ...that mathematics was a divine science. I thought that mathematics was the only science that spoke to inspiration and was infallible in its utterances [but] here is a new system of mathematics that demonstrates the truth to be false.’

4.2 Losing Meaning

In Sect.  9.1.4 above, the following question was asked:

Has modern physics itself really transformed into a postmodern narrative that defies meaning, clarity, and understanding?

The same question can be posed for mathematics. As the discipline becomes ever more technical, detailed, and abstract, fewer and fewer people can understand its subtlety and profundity.

Consider Fermat’s Last Theorem of 1637 (Wiles 1995 ):

Theorem 9.1

There are no non-zero solutions to the equation \(x^n+y^n=z^n\) , where x ,  y ,  x ,  and n are integers for \(n > 2\) .

It took over 350 years before, in 1995, Andrew Wiles presented a proof. It ran to over hundred pages and employed novel, previously unrelated, mathematical methods (Wiles 1995 ). What could be more demanding than a 100-page proof? Perhaps a proof which a computer carried out. The four-color theorem (Sect.  5.4.1 ) was proved in such a way. This raises the question “about whether a ‘proof’ that no one understand is a proof” (Colyvan 2012 ).

Then there is the tale of Shinichi Mochizuki (Castelvecchi 2015 ):

Sometime on the morning of 30 August 2012, Shinichi Mochizuki quietly posted four papers on his website. The papers were huge—more than 500 pages in all—packed densely with symbols, and the culmination of more than a decade of solitary work. They also had the potential to be an academic bombshell. In them, Mochizuki claimed to have solved the abc conjecture, a 27-year-old problem in number theory that no other mathematician had even come close to solving. If his proof was correct, it would be one of the most astounding achievements of mathematics this century and would completely revolutionize the study of equations with whole numbers. Everyone—even those whose area of expertise was closest to Mochizuki’s—was just as flummoxed by the papers [...]. To complete the proof, Mochizuki had invented a new branch of his discipline, one that is astonishingly abstract even by the standards of pure maths. “Looking at it, you feel a bit like you might be reading a paper from the future, or from outer space,” number theorist Jordan Ellenberg, of the University of Wisconsin-Madison, wrote on his blog a few days after the paper appeared.

Then, in 2016 (Castelvecchi 2009 ):

Nearly four years after Shinichi Mochizuki unveiled an imposing set of papers that could revolutionize the theory of numbers, other mathematicians have yet to understand his work or agree on its validity—although they have made modest progress.

Finally, in 2017 (Revell 2017 ):

“A small number of those close to Mochizuki claim to understand the proof, but they have had little success in explaining their understanding to others,” wrote Peter Woit at Columbia University in a blog post.

It does not help that mathematicians can be strange creatures (Castelvecchi 2015 ):

Mochizuki, however, did not make a fuss about his proof. The respected mathematician [...] did not even announce his work to peers around the world. He simply posted the papers, and waited for the world to find out. Adding to the enigma is Mochizuki himself. He has so far lectured about his work only in Japan, in Japanese, and despite being fluent in English, he has declined invitations to talk about it elsewhere. He does not speak to journalists; several requests for an interview for this story went unanswered.

This tendency work in isolation and avoid interactions with the world has similarities to another ingenious mathematician’s conduct which also alienated the community. Grigori Perelman shot to fame in 2003 after solving the century-old Poincaré conjecture (Singer 2004 ). For this achievement he was awarded the prestigious Fields Medal, considered to be the greatest accolade in mathematics. In addition, he was awarded the $1 million Millennium Prize Footnote 17 in recognition of his proof. Perelman, unprecedentedly, declined both prizes and noted (quoted in BBC News 2010 ):

I’m not interested in money or fame. I don’t want to be on display like an animal in a zoo. I’m not a hero of mathematics. I’m not even that successful; that is why I don’t want to have everybody looking at me.

Mathematics not only defies meaning when we don’t understand what is going on, but, more seriously, also when we do understand. For instance, the deeply counterintuitive Tarski-Banach theorem (Banach and Tarski 1924 ) states that (Colyvan 2012 , p. 152):

[...] a solid sphere can be decomposed into a finite number of pieces, the pieces moved around via rigid rotations and translations, and recombined into two spheres, each equal in volume to the first.

As a further counterintuitive example, consider the following equation

Theorem 9.2

How can the sum of all positive integers be a negative fraction?

Let \(S_1 = 1 -1 +1-1+1-1+ \cdots \) Then

Let \(S_2 = 1-2+3-4+5-6+ \cdots \) Then

Now consider

Or, equivalently

Substituting ( 9.5 ) yields

or \(S = -\frac{1}{12}.\)

\(\square \)

This bizarre proof highlights the failure of human intuition when faced with infinite sums. A rigorous proof of Theorem 9.2 , looking less like arithmetic sleight-of-hand, can be found using the Riemann zeta function (Stopple 2003 )

However, even more puzzlingly, Theorem 9.2 is relevant in modern theoretical physics, as it constrains bosonic string theory to 26-dimensional space-time (Polchinski 2005 , p. 22).

A further example of a deep and eerie connection between mathematics and reality is the number \(\pi \) . It is defined as the ratio of the circumference of a circle to its diameter. It is an irrational number (i.e., it cannot be expressed as a fraction) and it is also a transcendental number (i.e., it is not the solution of any polynomial with rational numbers as coefficients). \(\pi \) has an infinite number of digits in its decimal representation and no repeating pattern ever occurs. Magically, the formula for \(\pi \) appears in a basic calculation in the physics of the hydrogen atom (Friedmann and Hagen 2015 ). Then there is the claim that the distribution of the prime numbers follow the energy levels of a quantum system (Bender et al. 2017 ).

Initially, mathematics gave structure and order to human thinking. In the realm of the abstract, clear rules inexorably dictated its inner workings. The domain of relevance of mathematics exploded with the discovery of Volume I of the Book of Nature (Chaps.  2 , 3 , 4 , and 5 ). This unprecedented and extraordinary success is overshadowed by the discovery of the irreparable incompleteness and randomness in the foundations of mathematics. Moreover, how legitimate is a discipline, which can only be comprehended by a hand-full of initiated people?

See also Sect.  2.2 .

Which themselves could change in time.

A reference to the fictional character Baron Münchhausen, who once famously pulled himself (and the horse on which he was sitting) out of a swamp by his own hair.

See Kay’s 2011 TED talk.

This splitting of the intellectual life into the cultures of sciences and the humanities was already diagnosed in 1959 (Snow 1993 ). An attempt to reconcile the two fronts can be found in Labinger and Collins ( 2001 ).

Based on what is known as context free grammar.

The inconsistent theoretical calculations of black-body radiation, discussed in Sect.  4.3.4 , and the constant speed of light, seen in Sect.  3.2.1 .

It is tempting to add the “unreasonable simplicity of complexity” to this line of musing, which miraculously makes the complex systems surrounding us accessible and comprehensible, as described in Sect.  5.2.1 .

Recall the initial opposition to quasicrystals in Sect.  5.1.3 .

For actual scaling laws in finance, see Sect.  6.4.3.4 .

See http://www.er.ethz.ch/media/essays/PNAS.html .

Naturally, some exceptions exist. For instance, Springer’s The Frontiers Collection , where this book is published in, “is intended to encourage active scientists in all areas to ponder over important and perhaps controversial issues beyond their own speciality.”

See: http://www.edge.org .

See: http://www.edge.org/annual-questions .

Note that it can be considered an honor to be asked to answer an annual question.

Recall that axioms within any logical system can, by definition, not be proved by that system. Axioms are just a given, supporting the mathematical structure built upon them—they themselves are floating in the abstract abyss.

See http://www.claymath.org/millennium-problems/poincar%C3%A9-conjecture .

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We are faced with a monumental, inexplicable, and insurmountable paradox. The human mind’s journey into the realm of abstractions explains the rise of Homo sapiens (Harari 2015 ). Over millennia, we, as a species, have constructed layer after layer of fictional reality on top of objective reality. Religion, money, and nations are prime examples of abstract structures fostering human cooperation and evolution. Finally, laws of nature, elementary particles, and fundamental forces joined the mosaic of abstractions. However, nothing was more momentous than the human mind’s conquering of the abstract plane of mathematics. Now, there was nothing stopping Homo sapiens conquering the universe (Harari 2015 , p. 294):

Science, industry and military technology intertwined only with the advent of the capitalist system and the Industrial Revolution. Once this relationship was established, however, it quickly transformed the world.

This miraculous rise to dominance, aided by the mind’s capacity to decipher the workings of the universe, is, perplexingly, accompanied by the loss of certainty and meaning. Today, the edifice of science has a distinct postmodern veneer. It appears to be a Byzantine patchwork of knowledge fragments, lacking an overarching and unifying framework—isolated islands of knowledge with no common structure. Science has become like a static mosaic which is forever expanded by adding ever smaller and smaller pieces to it. Worst of all, the whole edifice of science appears to be floating in empty space, lacking any foundation. After millennia of human knowledge generation, today, the inherent limitations to knowledge seem inescapable. Even the realm of beautiful and timeless abstractions is affected by this decline in certainty and meaning. We appear to be lost in mathematical translation, where hyper-abstraction and obscurity reign. Are mathematics and science simply true by accident?

Admitting the limitations of knowledge would seem like a sober and pragmatic way to bypass such predicaments. After all, exposing the limits of human knowledge does not affect the nature of reality. So, can we find comfort and clarity in examining reality itself—in effect, moving from epistemology to ontology?

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Glattfelder, J.B. (2019). Philosophy and Science: What Can I Know?. In: Information—Consciousness—Reality. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-030-03633-1_9

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Philosophy of science is a thriving and interdisciplinary field of research, bringing together philosophy and cutting-edge science.

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Research Philosophy

Research philosophy is a vast topic and here we will not be discussing this topic in great details. Research philosophy is associated with assumption, knowledge and nature of the study. It deals with the specific way of developing knowledge. This matter needs to be addressed because researchers may have different assumptions about the nature of truth and knowledge and philosophy helps us to understand their assumptions.

In business and economics dissertations at Bachelor’s level, you are not expected to discuss research philosophy in a great level of depth, and about one page in methodology chapter devoted to research philosophy usually suffices. For a business dissertation at Master’s level, on the other hand, you may need to provide more discussion of the philosophy of your study. But even there, about two pages of discussions are usually accepted as sufficient by supervisors.

Discussion of research philosophy in your dissertation should include the following:

• You need to specify the research philosophy of your study. Your research philosophy can be pragmatism , positivism , realism or interpretivism as discussed below in more details.
• The reasons behind philosophical classifications of the study need to be provided.
• You need to discuss the implications of your research philosophy on the research strategy in general and the choice of primary data collection methods in particular.

The Essence of Research Philosophy

Research philosophy deals with the source, nature and development of knowledge [1] . In simple terms, research philosophy is belief about the ways in which data about a phenomenon should be collected, analysed and used.

Although the idea of knowledge creation may appear to be profound, you are engaged in knowledge creation as part of completing your dissertation. You will collect secondary and primary data and engage in data analysis to answer the research question and this answer marks the creation of new knowledge.

In respect to business and economics philosophy has the following important three functions [2] :

• Demystifying : Exposing, criticising and explaining the unsustainable assumptions, inconsistencies and confusions these may contain.
• Informing : Helping researchers to understand where they stand in the wider field of knowledge-producing activities, and helping to make them aware of potentialities they might explore.
• Method-facilitating : Dissecting and better understanding the methods which economists or, more generally, scientists do, or could, use, and thereby to refine the methods on offer and/or to clarify their conditions of usage.

In essence, addressing research philosophy in your dissertation involves being aware and formulating your beliefs and assumptions.  As illustrated in figure below, the identification of research philosophy is positioned at the outer layer of the ‘research onion’. Accordingly it is the first topic to be clarified in research methodology chapter of your dissertation.

Research philosophy in the ‘research onion’ [2]

Each stage of the research process is based on assumptions about the sources and the nature of knowledge. Research philosophy will reflect the author’s important assumptions and these assumptions serve as base for the research strategy. Generally, research philosophy has many branches related to a wide range of disciplines. Within the scope of business studies in particular there are four main research philosophies:

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The choice of a specific research philosophy is impacted by practical implications. There are important philosophical differences between studies that focus on facts and numbers such as an analysis of the impact of foreign direct investment on the level of GDP growth and qualitative studies such as an analysis of leadership style on employee motivation in organizations.

The choice between positivist and interpretivist research philosophies or between quantitative and qualitative research methods has traditionally represented a major point of debate. However, the latest developments in the practice of conducting studies have increased the popularity of pragmatism and realism philosophies as well.

Moreover, as it is illustrated in table below, there are popular data collection methods associated with each research philosophy.

 Research philosophies and data collection methods [3]

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research philosophy. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis  are explained in this e-book in simple words.

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[1] Bajpai, N. (2011) “Business Research Methods” Pearson Education India

[2] Tsung, E.W.K. (2016) “The Philosophy of Management Research” Routledge

[3] Table adapted from Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

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Scientific Objectivity

Scientific objectivity is a property of various aspects of science. It expresses the idea that scientific claims, methods, results—and scientists themselves—are not, or should not be, influenced by particular perspectives, value judgments, community bias or personal interests, to name a few relevant factors. Objectivity is often considered to be an ideal for scientific inquiry, a good reason for valuing scientific knowledge, and the basis of the authority of science in society.

Many central debates in the philosophy of science have, in one way or another, to do with objectivity: confirmation and the problem of induction; theory choice and scientific change; realism; scientific explanation; experimentation; measurement and quantification; statistical evidence; reproducibility; evidence-based science; feminism and values in science. Understanding the role of objectivity in science is therefore integral to a full appreciation of these debates. As this article testifies, the reverse is true too: it is impossible to fully appreciate the notion of scientific objectivity without touching upon many of these debates.

The ideal of objectivity has been criticized repeatedly in philosophy of science, questioning both its desirability and its attainability. This article focuses on the question of how scientific objectivity should be defined , whether the ideal of objectivity is desirable , and to what extent scientists can achieve it.

1. Introduction

2.1 the view from nowhere, 2.2 theory-ladenness and incommensurability, 2.3 underdetermination, values, and the experimenters’ regress, 3.1 epistemic and contextual values, 3.2 acceptance of scientific hypotheses and value neutrality, 3.3 science, policy and the value-free ideal, 4.1 measurement and quantification, 4.2.1 bayesian inference, 4.2.2 frequentist inference, 4.3 feyerabend: the tyranny of the rational method, 5.1 reproducibility and the meta-analytic perspective, 5.2 feminist and standpoint epistemology, 6.1 max weber and objectivity in the social sciences, 6.2 contemporary rational choice theory, 6.3 evidence-based medicine and social policy, 7. the unity and disunity of scientific objectivity, 8. conclusions, other internet resources, related entries.

Objectivity is a value. To call a thing objective implies that it has a certain importance to us and that we approve of it. Objectivity comes in degrees. Claims, methods, results, and scientists can be more or less objective, and, other things being equal, the more objective, the better. Using the term “objective” to describe something often carries a special rhetorical force with it. The admiration of science among the general public and the authority science enjoys in public life stems to a large extent from the view that science is objective or at least more objective than other modes of inquiry. Understanding scientific objectivity is therefore central to understanding the nature of science and the role it plays in society.

If what is so great about science is its objectivity, then objectivity should be worth defending. The close examinations of scientific practice that philosophers of science have undertaken in the past fifty years have shown, however, that several conceptions of the ideal of objectivity are either questionable or unattainable. The prospects for a science providing a non-perspectival “view from nowhere” or for proceeding in a way uninformed by human goals and values are fairly slim, for example.

This article discusses several proposals to characterize the idea and ideal of objectivity in such a way that it is both strong enough to be valuable, and weak enough to be attainable and workable in practice. We begin with a natural conception of objectivity: faithfulness to facts . We motivate the intuitive appeal of this conception, discuss its relation to scientific method and discuss arguments challenging both its attainability as well as its desirability. We then move on to a second conception of objectivity as absence of normative commitments and value-freedom , and once more we contrast arguments in favor of such a conception with the challenges it faces. A third conception of objectivity which we discuss at length is the idea of absence of personal bias .

Finally there is the idea that objectivity is anchored in scientific communities and their practices . After discussing three case studies from economics, social science and medicine, we address the conceptual unity of scientific objectivity : Do the various conceptions have a common valid core, such as promoting trust in science or minimizing relevant epistemic risks? Or are they rivaling and only loosely related accounts? Finally we present some conjectures about what aspects of objectivity remain defensible and desirable in the light of the difficulties we have encountered.

2. Objectivity as Faithfulness to Facts

The basic idea of this first conception of objectivity is that scientific claims are objective in so far as they faithfully describe facts about the world. The philosophical rationale underlying this conception of objectivity is the view that there are facts “out there” in the world and that it is the task of scientists to discover, analyze, and systematize these facts. “Objective” then becomes a success word: if a claim is objective, it correctly describes some aspect of the world.

In this view, science is objective to the degree that it succeeds at discovering and generalizing facts, abstracting from the perspective of the individual scientist. Although few philosophers have fully endorsed such a conception of scientific objectivity, the idea figures recurrently in the work of prominent twentieth-century philosophers of science such as Carnap, Hempel, Popper, and Reichenbach.

Humans experience the world from a perspective. The contents of an individual’s experiences vary greatly with his perspective, which is affected by his personal situation, and the details of his perceptual apparatus, language and culture. While the experiences vary, there seems to be something that remains constant. The appearance of a tree will change as one approaches it but—according to common sense and most philosophers—the tree itself doesn’t. A room may feel hot or cold for different persons, but its temperature is independent of their experiences. The object in front of me does not disappear just because the lights are turned off.

These examples motivate a distinction between qualities that vary with one’s perspective, and qualities that remain constant through changes of perspective. The latter are the objective qualities. Thomas Nagel explains that we arrive at the idea of objective qualities in three steps (Nagel 1986: 14). The first step is to realize (or postulate) that our perceptions are caused by the actions of things around us, through their effects on our bodies. The second step is to realize (or postulate) that since the same qualities that cause perceptions in us also have effects on other things and can exist without causing any perceptions at all, their true nature must be detachable from their perspectival appearance and need not resemble it. The final step is to form a conception of that “true nature” independently of any perspective. Nagel calls that conception the “view from nowhere”, Bernard Williams the “absolute conception” (Williams 1985 [2011]). It represents the world as it is, unmediated by human minds and other “distortions”.

This absolute conception lies at the basis of scientific realism (for a detailed discussion, see the entry on scientific realism ) and it is attractive in so far as it provides a basis for arbitrating between conflicting viewpoints (e.g., two different observations). Moreover, the absolute conception provides a simple and unified account of the world. Theories of trees will be very hard to come by if they use predicates such as “height as seen by an observer” and a hodgepodge if their predicates track the habits of ordinary language users rather than the properties of the world. To the extent, then, that science aims to provide explanations for natural phenomena, casting them in terms of the absolute conception would help to realize this aim. A scientific account cast in the language of the absolute conception may not only be able to explain why a tree is as tall as it is but also why we see it in one way when viewed from one standpoint and in a different way when viewed from another. As Williams (1985 [2011: 139]) puts it,

[the absolute conception] nonvacuously explain[s] how it itself, and the various perspectival views of the world, are possible.

A third reason to find the view from nowhere attractive is that if the world came in structures as characterized by it and we did have access to it, we could use our knowledge of it to ground predictions (which, to the extent that our theories do track the absolute structures, will be borne out). A fourth and related reason is that attempts to manipulate and control phenomena can similarly be grounded in our knowledge of these structures. To attain any of the four purposes—settling disagreements, explaining the world, predicting phenomena, and manipulation and control—the absolute conception is at best sufficient but not necessary. We can, for instance, settle disagreements by imposing the rule that the person with higher social rank or greater experience is always right. We can explain the world and our image of it by means of theories that do not represent absolute structures and properties, and there is no need to get things (absolutely) right in order to predict successfully. Nevertheless, there is something appealing in the idea that factual disagreements can be settled by the very facts themselves, that explanations and predictions grounded in what’s really there rather than in a distorted image of it.

No matter how desirable, our ability to use scientific claims to represent facts about the world depends on whether these claims can unambiguously be established on the basis of evidence, and of evidence alone. Alas, the relation between evidence and scientific hypothesis is not straightforward. Subsection 2.2 and subsection 2.3 will look at two challenges of the idea that even the best scientific method will yield claims that describe an aperspectival view from nowhere. Section 5.2 will deal with socially motivated criticisms of the view from nowhere.

According to a popular picture, all scientific theories are false and imperfect. Yet, as we add true and eliminate false beliefs, our best scientific theories become more truthlike (e.g., Popper 1963, 1972). If this picture is correct, then scientific knowledge grows by gradually approaching the truth and it will become more objective over time, that is, more faithful to facts. However, scientific theories often change, and sometimes several theories compete for the place of the best scientific account of the world.

It is inherent in the above picture of scientific objectivity that observations can, at least in principle, decide between competing theories. If they did not, the conception of objectivity as faithfulness would be pointless to have as we would not be in a position to verify it. This position has been adopted by Karl R. Popper, Rudolf Carnap and other leading figures in (broadly) empiricist philosophy of science. Many philosophers have argued that the relation between observation and theory is way more complex and that influences can actually run both ways (e.g., Duhem 1906 [1954]; Wittgenstein 1953 [2001]). The most lasting criticism, however, was delivered by Thomas S. Kuhn (1962 [1970]) in his book “The Structure of Scientific Revolutions”.

Kuhn’s analysis is built on the assumption that scientists always view research problems through the lens of a paradigm, defined by set of relevant problems, axioms, methodological presuppositions, techniques, and so forth. Kuhn provided several historical examples in favor of this claim. Scientific progress—and the practice of normal, everyday science—happens within a paradigm that guides the individual scientists’ puzzle-solving work and that sets the community standards.

Can observations undermine such a paradigm, and speak for a different one? Here, Kuhn famously stresses that observations are “theory-laden” (cf. also Hanson 1958): they depend on a body of theoretical assumptions through which they are perceived and conceptualized. This hypothesis has two important aspects.

First, the meaning of observational concepts is influenced by theoretical assumptions and presuppositions. For example, the concepts “mass” and “length” have different meanings in Newtonian and relativistic mechanics; so does the concept “temperature” in thermodynamics and statistical mechanics (cf. Feyerabend 1962). In other words, Kuhn denies that there is a theory-independent observation language. The “faithfulness to reality” of an observation report is always mediated by a theoretical überbau , disabling the role of observation reports as an impartial, merely fact-dependent arbiter between different theories.

Second, not only the observational concepts, but also the perception of a scientist depends on the paradigm she is working in.

Practicing in different worlds, the two groups of scientists [who work in different paradigms, J.R./J.S.] see different things when they look from the same point in the same direction. (Kuhn 1962 [1970: 150])

That is, our own sense data are shaped and structured by a theoretical framework, and may be fundamentally distinct from the sense data of scientists working in another one. Where a Ptolemaic astronomer like Tycho Brahe sees a sun setting behind the horizon, a Copernican astronomer like Johannes Kepler sees the horizon moving up to a stationary sun. If this picture is correct, then it is hard to assess which theory or paradigm is more faithful to the facts, that is, more objective.

The thesis of the theory-ladenness of observation has also been extended to the incommensurability of different paradigms or scientific theories , problematized independently by Thomas S. Kuhn (1962 [1970]) and Paul Feyerabend (1962). Literally, this concept means “having no measure in common”, and it figures prominently in arguments against a linear and standpoint-independent picture of scientific progress. For instance, the Special Theory of Relativity appears to be more faithful to the facts and therefore more objective than Newtonian mechanics because it reduces, for low speeds, to the latter, and it accounts for some additional facts that are not predicted correctly by Newtonian mechanics. This picture is undermined, however, by two central aspects of incommensurability. First, not only do the observational concepts in both theories differ, but the principles for specifying their meaning may be inconsistent with each other (Feyerabend 1975: 269–270). Second, scientific research methods and standards of evaluation change with the theories or paradigms. Not all puzzles that could be tackled in the old paradigm will be solved by the new one—this is the phenomenon of “Kuhn loss”.

A meaningful use of objectivity presupposes, according to Feyerabend, to perceive and to describe the world from a specific perspective, e.g., when we try to verify the referential claims of a scientific theory. Only within a peculiar scientific worldview, the concept of objectivity may be applied meaningfully. That is, scientific method cannot free itself from the particular scientific theory to which it is applied; the door to standpoint-independence is locked. As Feyerabend puts it:

our epistemic activities may have a decisive influence even upon the most solid piece of cosmological furniture—they make gods disappear and replace them by heaps of atoms in empty space. (1978: 70)

Kuhn and Feyerabend’s theses about theory-ladenness of observation, and their implications for the objectivity of scientific inquiry have been much debated afterwards, and have often been misunderstood in a social constructivist sense. Therefore Kuhn later returned to the topic of scientific objectivity, of which he gives his own characterization in terms of the shared cognitive values of a scientific community. We discuss Kuhn’s later view in section 3.1 . For a more thorough coverage, see the entries on theory and observation in science , the incommensurability of scientific theories and Thomas S. Kuhn .

Scientific theories are tested by comparing their implications with the results of observations and experiments. Unfortunately, neither positive results (when the theory’s predictions are borne out in the data) nor negative results (when they are not) allow unambiguous inferences about the theory. A positive result can obtain even though the theory is false, due to some alternative that makes the same predictions. Finding suspect Jones’ fingerprints on the murder weapon is consistent with his innocence because he might have used it as a kitchen knife. A negative result might be due not to the falsehood of the theory under test but due to the failing of one or more auxiliary assumptions needed to derive a prediction from the theory. Testing, let us say, the implications of Newton’s laws for movements in our planetary system against observations requires assumptions about the number of planets, the sun’s and the planets’ masses, the extent to which the earth’s atmosphere refracts light beams, how telescopes affect the results and so on. Any of these may be false, explaining an inconsistency. The locus classicus for these observations is Pierre Duhem’s The Aim and Structure of Physical Theory (Duhem 1906 [1954]). Duhem concluded that there was no “crucial experiment”, an experiment that conclusively decides between two alternative theories, in physics (1906 [1954: 188ff.]), and that physicists had to employ their expert judgment or what Duhem called “good sense” to determine what an experimental result means for the truth or falsehood of a theory (1906 [1954: 216ff.]).

In other words, there is a gap between the evidence and the theory supported by it. It is important to note that the alleged gap is more profound than the gap between the premisses of any inductive argument and its conclusion, say, the gap between “All hitherto observed ravens have been black” and “All ravens are black”. The latter gap could be bridged by an agreed upon rule of inductive reasoning. Alas, all attempts to find an analogous rule for theory choice have failed (e.g., Norton 2003). Various philosophers, historians, and sociologists of science have responded that theory appraisal is “a complex form of value judgment” (McMullin 1982: 701; see also Kuhn 1977; Hesse 1980; Bloor 1982).

In section 3.1 below we will discuss the nature of the value judgments in more detail. For now the important lesson is that if these philosophers, historians, and sociologists are correct, the “faithfulness to facts” ideal is untenable. As the scientific image of the world is a joint product of the facts and scientists’ value judgments, that image cannot be said to be aperspectival. Science does not eschew the human perspective. There are of course ways to escape this conclusion. If, as John Norton (2003; ms.—see Other Internet Resources) has argued, it is material facts that power and justify inductive inferences, and not value judgments, we can avoid the negative conclusion regarding the view from nowhere. Unsurprisingly, Norton is also critical of the idea that evidence generally underdetermines theory (Norton 2008). However, there are good reasons to mistrust Norton’s optimism regarding the ineliminability of values and other non-factual elements in inductive inferences (Reiss 2020).

There is another, closely related concern. Most of the earlier critics of “objective” verification or falsification focused on the relation between evidence and scientific theories. There is a sense in which the claim that this relation is problematic is not so surprising. Scientific theories contain highly abstract claims that describe states of affairs far removed from the immediacy of sense experience. This is for a good reason: sense experience is necessarily perspectival, so to the extent to which scientific theories are to track the absolute conception, they must describe a world different from that of sense experience. But surely, one might think, the evidence itself is objective. So even if we do have reasons to doubt that abstract theories faithfully represent the world, we should stand on firmer grounds when it comes to the evidence against which we test abstract theories.

Theories are seldom tested against brute observations, however. Simple generalizations such as “all swans are white” are directly learned from observations (say, of the color of swans) but they do not represent the view from nowhere (for one thing, the view from nowhere doesn’t have colors). Genuine scientific theories are tested against experimental facts or phenomena, which are themselves unobservable to the unaided senses. Experimental facts or phenomena are instead established using intricate procedures of measurement and experimentation.

We therefore need to ask whether the results of scientific measurements and experiments can be aperspectival. In an important debate in the 1980s and 1990s some commentators answered that question with a resounding “no”, which was then rebutted by others. The debate concerns the so-called “experimenter’s regress” (Collins 1985). Collins, a prominent sociologist of science, claims that in order to know whether an experimental result is correct, one first needs to know whether the apparatus producing the result is reliable. But one doesn’t know whether the apparatus is reliable unless one knows that it produces correct results in the first place and so on and so on ad infinitum . Collins’ main case concerns attempts to detect gravitational waves, which were very controversially discussed among physicists in the 1970s.

Collins argues that the circle is eventually broken not by the “facts” themselves but rather by factors having to do with the scientist’s career, the social and cognitive interests of his community, and the expected fruitfulness for future work. It is important to note that in Collins’s view these factors do not necessarily make scientific results arbitrary. But what he does argue is that the experimental results do not represent the world according to the absolute conception. Rather, they are produced jointly by the world, scientific apparatuses, and the psychological and sociological factors mentioned above. The facts and phenomena of science are therefore necessarily perspectival.

In a series of contributions, Allan Franklin, a physicist-turned-philosopher of science, has tried to show that while there are indeed no algorithmic procedures for establishing experimental facts, disagreements can nevertheless be settled by reasoned judgment on the basis of bona fide epistemological criteria such as experimental checks and calibration, elimination of possible sources of error, using apparatuses based on well-corroborated theory and so on (Franklin 1994, 1997). Collins responds that “reasonableness” is a social category that is not drawn from physics (Collins 1994).

The main issue for us in this debate is whether there are any reasons to believe that experimental results provide an aperspectival view on the world. According to Collins, experimental results are co-determined by the facts as well as social and psychological factors. According to Franklin, whatever else influences experimental results other than facts is not arbitrary but instead based on reasoned judgment. What he has not shown is that reasoned judgment guarantees that experimental results reflect the facts alone and are therefore aperspectival in any interesting sense. Another important challenge for the aperspectival account comes from feminist epistemology and other accounts that stress the importance of the construction of scientific knowledge through epistemic communities. These accounts are reviewed in section 5 .

3. Objectivity as Absence of Normative Commitments and the Value-Free Ideal

In the previous section we have presented arguments against the view of objectivity as faithfulness to facts and an impersonal “view from nowhere”. An alternative view is that science is objective to the extent that it is value-free . Why would we identify objectivity with value-freedom or regard the latter as a prerequisite for the former? Part of the answer is empiricism. If science is in the business of producing empirical knowledge, and if differences about value judgments cannot be settled by empirical means, values should have no place in science. In the following we will try to make this intuition more precise.

Before addressing what we will call the “value-free ideal”, it will be helpful to distinguish four stages at which values may affect science. They are: (i) the choice of a scientific research problem; (ii) the gathering of evidence in relation to the problem; (iii) the acceptance of a scientific hypothesis or theory as an adequate answer to the problem on the basis of the evidence; (iv) the proliferation and application of scientific research results (Weber 1917 [1949]).

Most philosophers of science would agree that the role of values in science is contentious only with respect to dimensions (ii) and (iii): the gathering of evidence and the acceptance of scientific theories . It is almost universally accepted that the choice of a research problem is often influenced by interests of individual scientists, funding parties, and society as a whole. This influence may make science more shallow and slow down its long-run progress, but it has benefits, too: scientists will focus on providing solutions to those intellectual problems that are considered urgent by society and they may actually improve people’s lives. Similarly, the proliferation and application of scientific research results is evidently affected by the personal values of journal editors and end users, and little can be done about this. The real debate is about whether or not the “core” of scientific reasoning—the gathering of evidence and the assessment and acceptance scientific theories—is, and should be, value-free.

We have introduced the problem of the underdetermination of theory by evidence above. The problem does not stop, however, at values being required for filling the gap between theory and evidence. A further complication is that these values can conflict with each other. Consider the classical problem of fitting a mathematical function to a data set. The researcher often has the choice between using a complex function, which makes the relationship between the variables less simple but fits the data more accurately , or postulating a simpler relationship that is less accurate . Simplicity and accuracy are both important cognitive values, and trading them off requires a careful value judgment. However, philosophers of science tend to regard value-ladenness in this sense as benign. Cognitive values (sometimes also called “epistemic” or “constitutive” values) such as predictive accuracy, scope, unification, explanatory power, simplicity and coherence with other accepted theories are taken to be indicative of the truth of a theory and therefore provide reasons for preferring one theory over another (McMullin 1982, 2009; Laudan 1984; Steel 2010). Kuhn (1977) even claims that cognitive values define the shared commitments of science, that is, the standards of theory assessment that characterize the scientific approach as a whole. Note that not every philosopher entertains the same list of cognitive values: subjective differences in ranking and applying cognitive values do not vanish, a point Kuhn made emphatically.

In most views, the objectivity and authority of science is not threatened by cognitive values, but only by non-cognitive or contextual values . Contextual values are moral, personal, social, political and cultural values such as pleasure, justice and equality, conservation of the natural environment and diversity. The most notorious cases of improper uses of such values involve travesties of scientific reasoning, where the intrusion of contextual values led to an intolerant and oppressive scientific agenda with devastating epistemic and social consequences. In the Third Reich, a large part of contemporary physics, such as the theory of relativity, was condemned because its inventors were Jewish; in the Soviet Union, biologist Nikolai Vavilov was sentenced to death (and died in prison) because his theories of genetic inheritance did not match Marxist-Leninist ideology. Both states tried to foster a science that was motivated by political convictions (“Deutsche Physik” in Nazi Germany, Lysenko’s Lamarckian theory of inheritance and denial of genetics), leading to disastrous epistemic and institutional effects.

Less spectacular, but arguably more frequent are cases where research is biased toward the interests of the sponsors, such as tobacco companies, food manufacturers and large pharmaceutic firms (e.g., Resnik 2007; Reiss 2010). This preference bias , defined by Wilholt (2009) as the infringement of conventional standards of the research community with the aim of arriving at a particular result, is clearly epistemically harmful. Especially for sensitive high-stakes issues such as the admission of medical drugs or the consequences of anthropogenic global warming, it seems desirable that research scientists assess theories without being influenced by such considerations. This is the core idea of the

Value-Free Ideal (VFI): Scientists should strive to minimize the influence of contextual values on scientific reasoning, e.g., in gathering evidence and assessing/accepting scientific theories.

According to the VFI, scientific objectivity is characterized by absence of contextual values and by exclusive commitment to cognitive values in stages (ii) and (iii) of the scientific process. See Dorato (2004: 53–54), Ruphy (2006: 190) or Biddle (2013: 125) for alternative formulations.

For value-freedom to be a reasonable ideal, it must not be a goal beyond reach and be attainable at least to some degree. This claim is expressed by the

Value-Neutrality Thesis (VNT): Scientists can—at least in principle—gather evidence and assess/accept theories without making contextual value judgments.

Unlike the VFI, the VNT is not normative: its subject is whether the judgments that scientists make are, or could possibly be, free of contextual values. Similarly, Hugh Lacey (1999) distinguishes three principal components or aspects of value-free science: impartiality, neutrality and autonomy. Impartiality means that theories are solely accepted or appraised in virtue of their contribution to the cognitive values of science, such as truth, accuracy or explanatory power. This excludes the influence of contextual values, as stated above. Neutrality means that scientific theories make no value statements about the world: they are concerned with what there is, not with what there should be. Finally, scientific autonomy means that the scientific agenda is shaped by the desire to increase scientific knowledge, and that contextual values have no place in scientific method.

These three interpretations of value-free science can be combined with each other, or used individually. All of them, however, are subject to criticisms that we examine below. Denying the VNT, or the attainability of Lacey’s three criteria for value-free science, poses a challenge for scientific objectivity: one can either conclude that the ideal of objectivity should be rejected, or develop a conception of objectivity that differs from the VFI.

Lacey’s characterization of value-free science and the VNT were once mainstream positions in philosophy of science. Their widespread acceptance was closely connected to Reichenbach’s famous distinction between context of discovery and context of justification . Reichenbach first made this distinction with respect to the epistemology of mathematics:

the objective relation from the given entities to the solution, and the subjective way of finding it, are clearly separated for problems of a deductive character […] we must learn to make the same distinction for the problem of the inductive relation from facts to theories. (Reichenbach 1938: 36–37)

The standard interpretation of this statement marks contextual values, which may have contributed to the discovery of a theory, as irrelevant for justifying the acceptance of a theory, and for assessing how evidence bears on theory—the relation that is crucial for the objectivity of science. Contextual values are restricted to a matter of individual psychology that may influence the discovery, development and proliferation of a scientific theory, but not its epistemic status.

This distinction played a crucial role in post-World War II philosophy of science. It presupposes, however, a clear-cut distinction between cognitive values on the one hand and contextual values on the other. While this may be prima facie plausible for disciplines such as physics, there is an abundance of contextual values in the social sciences, for instance, in the conceptualization and measurement of a nation’s wealth, or in different ways to measure the inflation rate (cf. Dupré 2007; Reiss 2008). More generally, three major lines of criticism can be identified.

First, Helen Longino (1996) has argued that traditional cognitive values such as consistency, simplicity, breadth of scope and fruitfulness are not purely cognitive or epistemic after all, and that their use imports political and social values into contexts of scientific judgment. According to her, the use of cognitive values in scientific judgments is not always, not even normally, politically neutral. She proposes to juxtapose these values with feminist values such as novelty, ontological heterogeneity, mutuality of interaction, applicability to human needs and diffusion of power, and argues that the use of the traditional value instead of its alternative (e.g., simplicity instead of ontological heterogeneity) can lead to biases and adverse research results. Longino’s argument here is different from the one discussed in section 3.1 . It casts the very distinction between cognitive and contextual values into doubt.

The second argument against the possibility of value-free science is semantic and attacks the neutrality of scientific theories: fact and value are frequently entangled because of the use of so-called “thick” ethical concepts in science (Putnam 2002)—i.e., ethical concepts that have mixed descriptive and normative content. For example, a description such as “dangerous technology” involves a value judgment about the technology and the risks it implies, but it also has a descriptive content: it is uncertain and hard to predict whether using that technology will really trigger those risks. If the use of such terms, where facts and values are inextricably entangled, is inevitable in scientific reasoning, it is impossible to describe hypotheses and results in a value-free manner, undermining the value-neutrality thesis.

Indeed, John Dupré has argued that thick ethical terms are ineliminable from science, at least certain parts of it (Dupré 2007). Dupré’s point is essentially that scientific hypotheses and results concern us because they are relevant to human interests, and thus they will necessarily be couched in a language that uses thick ethical terms. While it will often be possible to translate ethically thick descriptions into neutral ones, the translation cannot be made without losses, and these losses obtain precisely because human interests are involved (see section 6.2 for a case study from social science). According to Dupré, then, many scientific statements are value-free only because their truth or falsity does not matter to us:

Whether electrons have a positive or a negative charge and whether there is a black hole in the middle of our galaxy are questions of absolutely no immediate importance to us. The only human interests they touch (and these they may indeed touch deeply) are cognitive ones, and so the only values that they implicate are cognitive values. (2007: 31)

A third challenge to the VNT, and perhaps the most influential one, was raised first by Richard Rudner in his influential article “The Scientist Qua Scientist Makes Value Judgments” (Rudner 1953). Rudner disputes the core of the VNT and the context of discovery/justification distinction: the idea that the acceptance of a scientific theory can in principle be value-free. First, Rudner argues that

no analysis of what constitutes the method of science would be satisfactory unless it comprised some assertion to the effect that the scientist as scientist accepts or rejects hypotheses . (1953: 2)

This assumption stems from industrial quality control and other application-oriented research. In such contexts, it is often necessary to accept or to reject a hypothesis (e.g., the efficacy of a drug) in order to make effective decisions.

Second, he notes that no scientific hypothesis is ever confirmed beyond reasonable doubt—some probability of error always remains. When we accept or reject a hypothesis, there is always a chance that our decision is mistaken. Hence, our decision is also “a function of the importance , in the typically ethical sense, of making a mistake in accepting or rejecting a hypothesis” (1953: 2): we are balancing the seriousness of two possible errors (erroneous acceptance/rejection of the hypothesis) against each other. This corresponds to type I and type II error in statistical inference.

The decision to accept or reject a hypothesis involves a value judgment (at least implicitly) because scientists have to judge which of the consequences of an erroneous decision they deem more palatable: (1) some individuals die of the side effects of a drug erroneously judged to be safe; or (2) other individuals die of a condition because they did not have access to a treatment that was erroneously judged to be unsafe. Hence, ethical judgments and contextual values necessarily enter the scientist’s core activity of accepting and rejecting hypotheses, and the VNT stands refuted. Closely related arguments can be found in Churchman (1948) and Braithwaite (1953). Hempel (1965: 91–92) gives a modified account of Rudner’s argument by distinguishing between judgments of confirmation , which are free of contextual values, and judgments of acceptance . Since even strongly confirming evidence cannot fully prove a universal scientific law, we have to live with a residual “inductive risk” in inferring that law. Contextual values influence scientific methods by determining the acceptable amount of inductive risk (see also Douglas 2000).

But how general are Rudner’s objections? Apparently, his result holds true of applied science, but not necessarily of fundamental research. For the latter domain, two major lines of rebuttals have been proposed. First, Richard Jeffrey (1956) notes that lawlike hypotheses in theoretical science (e.g., the gravitational law in Newtonian mechanics) are characterized by their general scope and not confined to a particular application. Obviously, a scientist cannot fine-tune her decisions to their possible consequences in a wide variety of different contexts. So she should just refrain from the essentially pragmatic decision to accept or reject hypotheses. By restricting scientific reasoning to gathering and interpreting evidence, possibly supplemented by assessing the probability of a hypothesis, Jeffrey tries to save the VNT in fundamental scientific research, and the objectivity of scientific reasoning.

Second, Isaac Levi (1960) observes that scientists commit themselves to certain standards of inference when they become a member of the profession. This may, for example, lead to the statistical rejection of a hypothesis when the observed significance level is smaller than 5%. These community standards may eliminate any room for contextual ethical judgment on behalf of the scientist: they determine when she should accept a hypothesis as established. Value judgments may be implicit in how a scientific community sets standards of inference (compare section 5.1 ), but not in the daily work of an individual scientist (cf. Wilholt 2013).

Both defenses of the VNT focus on the impact of values in theory choice, either by denying that scientists actually choose theories (Jeffrey), or by referring to community standards and restricting the VNT to the individual scientist (Levi). Douglas (2000: 563–565) points out, however, that the “acceptance” of scientific theories is only one of several places for values to enter scientific reasoning, albeit an especially prominent and explicit one. Many decisions in the process of scientific inquiry may conceal implicit value judgments: the design of an experiment, the methodology for conducting it, the characterization of the data, the choice of a statistical method for processing and analyzing data, the interpretational process findings, etc. None of these methodological decisions could be made without consideration of the possible consequences that could occur. Douglas gives, as a case study, a series of experiments where carcinogenic effects of dioxin exposure on rats were probed. Contextual values such as safety and risk aversion affected the conducted research at various stages: first, in the classification of pathological samples as benign or cancerous (over which a lot of expert disagreement occurred), second, in the extrapolation from the high-dose experimental conditions to the more realistic low-dose conditions. In both cases, the choice of a conservative classification or model had to be weighed against the adverse consequences for society that could result from underestimating the risks (see also Biddle 2013).

These diagnoses cast a gloomy light on attempts to divide scientific labor between gathering evidence and determining the degree of confirmation (value-free) on the one hand and accepting scientific theories (value-laden) on the other. The entire process of conceptualizing, gathering and interpreting evidence is so entangled with contextual values that no neat division, as Jeffrey envisions, will work outside the narrow realm of statistical inference—and even there, doubts may be raised ( see section 4.2 ).

Philip Kitcher (2011a: 31–40; see also Kitcher 2011b) gives an alternative argument, based on his idea of “significant truths”. There are simply too many truths that are of no interest whatsoever, such as the total number of offside positions in a low-level football competition. Science, then, doesn’t aim at truth simpliciter but rather at something more narrow: truth worth pursuing from the point of view of our cognitive, practical and social goals. Any truth that is worth pursuing in this sense is what he calls a “significant truth”. Clearly, it is value judgments that help us decide whether or not any given truth is significant.

Kitcher goes on to observing that the process of scientific investigation cannot neatly be divided into a stage in which the research question is chosen, one in which the evidence is gathered and one in which a judgment about the question is made on the basis of the evidence. Rather, the sequence is multiply iterated, and at each stage, the researcher has to decide whether previous results warrant pursuit of the current line of research, or whether she should switch to another avenue. Such choices are laden with contextual values.

Values in science also interact, according to Kitcher, in a non-trivial way. Assume we endorse predictive accuracy as an important goal of science. However, there may not be a convincing strategy to reach this goal in some domain of science, for instance because that domain is characterized by strong non-linear dependencies. In this case, predictive accuracy might have to yield to achieving other values, such as consistency with theories in neighbor domains. Conversely, changing social goals lead to re-evaluations of scientific knowledge and research methods.

Science, then, cannot be value-free because no scientist ever works exclusively in the supposedly value-free zone of assessing and accepting hypotheses. Evidence is gathered and hypotheses are assessed and accepted in the light of their potential for application and fruitful research avenues. Both cognitive and contextual value judgments guide these choices and are themselves influenced by their results.

The discussion so far has focused on the VNT, the practical attainability of the VFI, but little has been said about whether a value-free science is desirable in the first place. This subsection discusses this topic with special attention to informing and advising public policy from a scientific perspective. While the VFI, and many arguments for and against it, can be applied to science as a whole, the interface of science and public policy is the place where the intrusion of values into science is especially salient, and where it is surrounded by the greatest controversy. In the 2009 “Climategate” affair, leaked emails from climate scientists raised suspicions that they were pursuing a particular socio-political agenda that affected their research in an improper way. Later inquiries and reports absolved them from charges of misconduct, but the suspicions alone did much to damage the authority of science in the public arena.

Indeed, many debates at the interface of science and public policy are characterized by disagreements on propositions that combine a factual basis with specific goals and values. Take, for instance, the view that growing transgenic crops carries too much risk in terms of biosecurity, or addressing global warming by phasing out fossil energies immediately. The critical question in such debates is whether there are theses \(T\) such that one side in the debate endorses \(T\), the other side rejects it, the evidence is shared, and both sides have good reasons for their respective positions.

According to the VFI, scientists should uncover an epistemic, value-free basis for resolving such disagreements and restrict the dissent to the realm of value judgments. Even if the VNT should turn out to be untenable, and a strict separation to be impossible, the VFI may have an important function for guiding scientific research and for minimizing the impact of values on an objective science. In the philosophy of science, one camp of scholars defends the VFI as a necessary antidote to individual and institutional interests, such as Hugh Lacey (1999, 2002), Ernan McMullin (1982) and Sandra Mitchell (2004), while others adopt a critical attitude, such as Helen Longino (1990, 1996), Philip Kitcher (2011a) and Heather Douglas (2009). These criticisms we discuss mainly refer to the desirability or the conceptual (un)clarity of the VFI.

First, it has been argued that the VFI is not desirable at all. Feminist philosophers (e.g., Harding 1991; Okruhlik 1994; Lloyd 2005) have argued that science often carries a heavy androcentric values, for instance in biological theories about sex, gender and rape. The charge against these values is not so much that they are contextual rather than cognitive, but that they are unjustified. Moreover, if scientists did follow the VFI rigidly, policy-makers would pay even less attention to them, with a detrimental effect on the decisions they take (Cranor 1993). Given these shortcomings, the VFI has to be rethought if it is supposed to play a useful role for guiding scientific research and leading to better policy decisions. Section 4.3 and section 5.2 elaborate on this line of criticism in the context of scientific community practices, and a science in the service of society.

Second, the autonomy of science often fails in practice due to the presence of external stakeholders, such as funding agencies and industry lobbies. To save the epistemic authority of science, Douglas (2009: 7–8) proposes to detach it from its autonomy by reformulating the VFI and distinguishing between direct and indirect roles of values in science . Contextual values may legitimately affect the assessment of evidence by indicating the appropriate standard of evidence, the representation of complex processes, the severity of consequences of a decision, the interpretation of noisy datasets, and so on (see also Winsberg 2012). This concerns, above all, policy-related disciplines such as climate science or economics that routinely perform scientific risk analyses for real-world problems (cf. also Shrader-Frechette 1991). Values should, however, not be “reasons in themselves”, that is, evidence or defeaters for evidence (direct role, illegitimate) and as “helping to decide what should count as a sufficient reason for a choice” (indirect role, legitimate). This prohibition for values to replace or dismiss scientific evidence is called detached objectivity by Douglas, but it is complemented by various other aspects that relate to a reflective balancing of various perspectives and the procedural, social aspects of science (2009: ch. 6).

That said, Douglas’ proposal is not very concrete when it comes to implementation, e.g., regarding the way diverse values should be balanced. Compromising in the middle cannot be the solution (Weber 1917 [1949]). First, no standpoint is, just in virtue of being in the middle, evidentially supported vis-à-vis more extreme positions. Second, these middle positions are also, from a practical point of view, the least functional when it comes to advising policy-makers.

Moreover, the distinction between direct and indirect roles of values in science may not be sufficiently clear-cut to police the legitimate use of values in science, and to draw the necessary borderlines. Assume that a scientist considers, for whatever reason, the consequences of erroneously accepting hypothesis \(H\) undesirable. Therefore he uses a statistical model whose results are likely to favor ¬\(H\) over \(H\). Is this a matter of reasonable conservativeness? Or doesn’t it amount to reasoning to a foregone conclusion, and to treating values as evidence (cf. Elliott 2011: 320–321)?

The most recent literature on values and evidence in science presents us with a broad spectrum of opinions. Steele (2012) and Winsberg (2012) agree that probabilistic assessments of uncertainty involve contextual value judgments. While Steele defends this point by analyzing the role of scientists as policy advisors, Winsberg points to the influence of contextual values in the selection and representation of physical processes in climate modeling. Betz (2013) argues, by contrast, that scientists can largely avoid making contextual value judgments if they carefully express the uncertainty involved with their evidential judgments, e.g., by using a scale ranging from purely qualitative evidence (such as expert judgment) to precise probabilistic assessments. The issue of value judgments at earlier stages of inquiry is not addressed by this proposal; however, disentangling evidential judgments and judgments involving contextual values at the stage of theory assessment may be a good thing in itself.

Thus, should we or should we not worried about values in scientific reasoning? While the interplay of values and evidential considerations need not be pernicious, it is unclear why it adds to the success or the authority of science. How are we going to ensure that the permissive attitude towards values in setting evidential standards etc. is not abused? In the absence of a general theory about which contextual values are beneficial and which are pernicious, the VFI might as well be as a first-order approximation to a sound, transparent and objective science.

4. Objectivity as Freedom from Personal Biases

This section deals with scientific objectivity as a form of intersubjectivity—as freedom from personal biases. According to this view, science is objective to the extent that personal biases are absent from scientific reasoning, or that they can be eliminated in a social process. Perhaps all science is necessarily perspectival. Perhaps we cannot sensibly draw scientific inferences without a host of background assumptions, which may include assumptions about values. Perhaps all scientists are biased in some way. But objective scientific results do not, or so the argument goes, depend on researchers’ personal preferences or experiences—they are the result of a process where individual biases are gradually filtered out and replaced by agreed upon evidence. That, among other things, is what distinguishes science from the arts and other human activities, and scientific knowledge from a fact-independent social construction (e.g., Haack 2003).

Paradigmatic ways to achieve objectivity in this sense are measurement and quantification. What has been measured and quantified has been verified relative to a standard. The truth, say, that the Eiffel Tower is 324 meters tall is relative to a standard unit and conventions about how to use certain instruments, so it is neither aperspectival nor free from assumptions, but it is independent of the person making the measurement.

We will begin with a discussion of objectivity, so conceived, in measurement, discuss the ideal of “mechanical objectivity” and then investigate to what extent freedom from personal biases can be implemented in statistical evidence and inductive inference—arguably the core of scientific reasoning, especially in quantitatively oriented sciences. Finally, we discuss Feyerabend’s radical criticism of a rational scientific method that can be mechanically applied, and his defense of the epistemic and social benefits of personal “bias” and idiosyncrasy.

Measurement is often thought to epitomize scientific objectivity, most famously captured in Lord Kelvin’s dictum

when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science , whatever the matter may be. (Kelvin 1883, 73)

Measurement can certainly achieve some independence of perspective. Yesterday’s weather in Durham UK may have been “really hot” to the average North Eastern Brit and “very cold” to the average Mexican, but they’ll both accept that it was 21°C. Clearly, however, measurement does not result in a “view from nowhere”, nor are typical measurement results free from presuppositions. Measurement instruments interact with the environment, and so results will always be a product of both the properties of the environment we aim to measure as well as the properties of the instrument. Instruments, thus, provide a perspectival view on the world (cf. Giere 2006).

Moreover, making sense of measurement results requires interpretation. Consider temperature measurement. Thermometers function by relating an unobservable quantity, temperature, to an observable quantity, expansion (or length) of a fluid or gas in a glass tube; that is, thermometers measure temperature by assuming that length is a function of temperature: length = \(f\)(temperature). The function \(f\) is not known a priori , and it cannot be tested either (because it could in principle only be tested using a veridical thermometer, and the veridicality of the thermometer is just what is at stake here). Making a specific assumption, for instance that \(f\) is linear, solves that problem by fiat. But this “solution” does not take us very far because different thermometric substances (e.g., mercury, air or water) yield different results for the points intermediate between the two fixed points 0°C and 100°C, and so they can’t all expand linearly.

According to Hasok Chang’s account of early thermometry (Chang 2004), the problem was eventually solved by using a “principle of minimalist overdetermination”, the goal of which was to find a reliable thermometer while making as few substantial assumptions (e.g., about the form for \(f\)) as possible. It was argued that if a thermometer was to be reliable, different tokens of the same thermometer type should agree with each other, and the results of air thermometers agreed the most. “Minimal” doesn’t mean zero, however, and indeed this procedure makes an important presupposition (in this case a metaphysical assumption about the one-valuedness of a physical quantity). Moreover, the procedure yielded at best a reliable instrument, not necessarily one that was best at tracking the uniquely real temperature (if there is such a thing).

What Chang argues about early thermometry is true of measurements more generally: they are always made against a backdrop of metaphysical presuppositions, theoretical expectations and other kinds of belief. Whether or not any given procedure is regarded as adequate depends to a large extent on the purposes pursued by the individual scientist or group of scientists making the measurements. Especially in the social sciences, this often means that measurement procedures are laden with normative assumptions, i.e., values.

Julian Reiss (2008, 2013) has argued that economic indicators such as consumer price inflation, gross domestic product and the unemployment rate are value-laden in this sense. Consumer-price indices, for instance, assume that if a consumer prefers a bundle \(x\) over an alternative \(y\), then \(x\) is better for her than \(y\), which is as ethically charged as it is controversial. National income measures assume that nations that exchange a larger share of goods and services on markets are richer than nations where the same goods and services are provided by the government or within households, which too is ethically charged and controversial.

While not free of assumptions and values, the goal of many measurement procedures remains to reduce the influence of personal biases and idiosyncrasies. The Nixon administration, famously, indexed social security payments to the consumer-price index in order to eliminate the dependence of security recipients on the flimsiest of party politics: to make increases automatic instead of a result of political negotiations (Nixon 1969). Lorraine Daston and Peter Galison refer to this as mechanical objectivity . They write:

Finally, we come to the full-fledged establishment of mechanical objectivity as the ideal of scientific representation. What we find is that the image, as standard bearer of is objectivity is tied to a relentless search to replace individual volition and discretion in depiction by the invariable routines of mechanical reproduction. (Daston and Galison 1992: 98)

Mechanical objectivity reduces the importance of human contributions to scientific results to a minimum, and therefore enables science to proceed on a large scale where bonds of trust between individuals can no longer hold (Daston 1992). Trust in mechanical procedures thus replaces trust in individual scientists.

In his book Trust in Numbers , Theodore Porter pursues this line of thought in great detail. In particular, on the basis of case studies involving British actuaries in the mid-nineteenth century, of French state engineers throughout the century, and of the US Army Corps of Engineers from 1920 to 1960, he argues for two causal claims. First, measurement instruments and quantitative procedures originate in commercial and administrative needs and affect the ways in which the natural and social sciences are practiced, not the other way around. The mushrooming of instruments such as chemical balances, barometers, chronometers was largely a result of social pressures and the demands of democratic societies. Administering large territories or controlling diverse people and processes is not always possible on the basis of personal trust and thus “objective procedures” (which do not require trust in persons) took the place of “subjective judgments” (which do). Second, he argues that quantification is a technology of distrust and weakness, and not of strength. It is weak administrators who do not have the social status, political support or professional solidarity to defend their experts’ judgments. They therefore subject decisions to public scrutiny, which means that they must be made in a publicly accessible form.

This is the situation in which scientists who work in areas where the science/policy boundary is fluid find themselves:

The National Academy of Sciences has accepted the principle that scientists should declare their conflicts of interest and financial holdings before offering policy advice, or even information to the government. And while police inspections of notebooks remain exceptional, the personal and financial interests of scientists and engineers are often considered material, especially in legal and regulatory contexts. Strategies of impersonality must be understood partly as defenses against such suspicions […]. Objectivity means knowledge that does not depend too much on the particular individuals who author it. (Porter 1995: 229)

Measurement and quantification help to reduce the influence of personal biases and idiosyncrasies and they reduce the need to trust the scientist or government official, but often at a cost. Standardizing scientific procedures becomes difficult when their subject matters are not homogeneous, and few domains outside fundamental physics are. Attempts to quantify procedures for treatment and policy decisions that we find in evidence-based practices are currently transferred to a variety of sciences such as medicine, nursing, psychology, education and social policy. However, they often lack a certain degree of responsiveness to the peculiarities of their subjects and the local conditions to which they are applied (see also section 5.3 ).

Moreover, the measurement and quantification of characteristics of scientific interest is only half of the story. We also want to describe relations between the quantities and make inferences using statistical analysis. Statistics thus helps to quantify further aspects of scientific work. We will now examine whether or not statistical analysis can proceed in a way free from personal biases and idiosyncrasies—for more detail, see the entry on philosophy of statistics .

4.2 Statistical Evidence

The appraisal of scientific evidence is traditionally regarded as a domain of scientific reasoning where the ideal of scientific objectivity has strong normative force, and where it is also well-entrenched in scientific practice. Episodes such as Galilei’s observations of the Jupiter moons, Lavoisier’s calcination experiments, and Eddington’s observation of the 1919 eclipse are found in all philosophy of science textbooks because they exemplify how evidence can be persuasive and compelling to scientists with different backgrounds. The crucial question is therefore: can we identify an “objective” concept of scientific evidence that is independent of the personal biases of the experimenter and interpreter?

Inferential statistics—the field that investigates the validity of inferences from data to theory—tries to answer this question. It is extremely influential in modern science, pervading experimental research as well as the assessment and acceptance of our most fundamental theories. For instance, a statistical argument helped to establish the recent discovery of the Higgs Boson. We now compare the main theories of statistical evidence with respect to the objectivity of the claims they produce. They mainly differ with respect to the role of an explicitly subjective interpretation of probability.

Bayesian inference quantifies scientific evidence by means of probabilities that are interpreted as a scientist’s subjective degrees of belief. The Bayesian thus leaves behind Carnap’s (1950) idea that probability is determined by a logical relation between sentences. For example, the prior degree of belief in hypothesis \(H\), written \(p(H)\), can in principle take any value in the interval \([0,1]\). Simultaneously held degrees of belief in different hypotheses are, however, constrained by the laws of probability. After learning evidence E, the degree of belief in \(H\) is changed from its prior probability \(p(H)\) to the conditional degree of belief \(p(H \mid E)\), commonly called the posterior probability of \(H\). Both quantities can be related to each other by means of Bayes’ Theorem .

These days, the Bayesian approach is extremely influential in philosophy and rapidly gaining ground across all scientific disciplines. For quantifying evidence for a hypothesis, Bayesian statisticians almost uniformly use the Bayes factor , that is, the ratio of prior to posterior odds in favor of a hypothesis. The Bayes factor in favor of hypothesis \(H\) against its negation \(\neg\)\(H\) in the light of evidence \(E\) can be written as

or in other words, as the likelihood ratio between \(H\) and \(\neg\)\(H\). The Bayes factor reduces to the likelihoodist conception of evidence (Royall 1997) for the case of two competing point hypotheses. For further discussion of Bayesian measures of evidence, see Good (1950), Sprenger and Hartmann (2019: ch. 1) and the entry on confirmation and evidential support .

Unsurprisingly, the idea to measure scientific evidence in terms of subjective probability has met resistance. For example, the statistician Ronald A. Fisher (1935: 6–7) has argued that measuring psychological tendencies cannot be relevant for scientific inquiry and sustain claims to objectivity. Indeed, how should scientific objectivity square with subjective degree of belief? Bayesians have responded to this challenge in various ways:

Howson (2000) and Howson and Urbach (2006) consider the objection misplaced. In the same way that deductive logic does not judge the correctness of the premises but just advises you what to infer from them, Bayesian inductive logic provides rational rules for representing uncertainty and making inductive inferences. Choosing the premises (e.g., the prior distributions) “objectively” falls outside the scope of Bayesian analysis.

Convergence or merging-of-opinion theorems guarantee that under certain circumstances, agents with very different initial attitudes who observe the same evidence will obtain similar posterior degrees of belief in the long run. However, they are asymptotic results without direct implications for inference with real-life datasets (see also Earman 1992: ch. 6). In such cases, the choice of the prior matters, and it may be beset with idiosyncratic bias and manifest social values.

Adopting a more modest stance, Sprenger (2018) accepts that Bayesian inference does not achieve the goal of objectivity in the sense of intersubjective agreement (concordant objectivity), or being free of personal values, bias and subjective judgment. However, he argues that competing schools of inference such as frequentist inference face this problem to the same degree, perhaps even worse. Moreover, some features of Bayesian inference (e.g., the transparency about prior assumptions) fit recent, socially oriented conceptions of objectivity that we discuss in section 5 .

A radical Bayesian solution to the problem of personal bias is to adopt a principle that radically constrains an agent’s rational degrees of belief, such as the Principle of Maximum Entropy (MaxEnt—Jaynes 1968; Williamson 2010). According to MaxEnt, degrees of belief must be probabilistic and in sync with empirical constraints, but conditional on these constraints, they must be equivocal, that is, as middling as possible. This latter constraint amounts to maximizing the entropy of the probability distribution in question. The MaxEnt approach eliminates various sources of subjective bias at the expense of narrowing down the range of rational degrees of belief. An alternative objective Bayesian solution consists in so-called “objective priors” : prior probabilities that do not represent an agent’s factual attitudes, but are determined by principles of symmetry, mathematical convenience or maximizing the influence of the data on the posterior (e.g., Jeffreys 1939 [1980]; Bernardo 2012).

Thus, Bayesian inference, which analyzes statistical evidence from the vantage point of rational belief, provides only a partial answer to securing scientific objectivity from personal idiosyncrasy.

The frequentist conception of evidence is based on the idea of the statistical test of a hypothesis . Under the influence of the statisticians Jerzy Neyman and Egon Pearson, tests were often regarded as rational decision procedures that minimize the relative frequency of wrong decisions in a hypothetical series of repetitions of a test (hence the name “frequentism”). Rudner’s argument in section 3.2 has pointed out the limits of this conception of hypothesis tests: the choice of thresholds for acceptance and rejection (i.e., the acceptable type I and II error rates) may reflect contextual value judgments and personal bias. Moreover, the losses associated with erroneously accepting or rejecting that hypothesis depend on the context of application which may be unbeknownst to the experimenter.

Alternatively, scientists can restrict themselves to a purely evidential interpretation of hypothesis tests and leave decisions to policy-makers and regulatory agencies. The statistician and biologist R.A. Fisher (1935, 1956) proposed what later became the orthodox quantification of evidence in frequentist statistics. Suppose a “null” or default hypothesis \(H_0\) denotes that an intervention has zero effect. If the observed data are “extreme” under \(H_0\)—i.e., if it was highly likely to observe a result that agrees better with \(H_0\)—the data provide evidence against the null hypothesis and for the efficacy of the intervention. The epistemological rationale is connected to the idea of severe testing (Mayo 1996): if the intervention were ineffective, we would, in all likelihood, have found data that agree better with the null hypothesis. The strength of evidence against \(H_0\) is equal to the \(p\)-value : the lower it is, the stronger evidence \(E\) speaks against the null hypothesis \(H_0\).

Unlike Bayes factors, this concept of statistical evidence does not depend on personal degrees of belief. However, this does not necessarily mean that \(p\)-values are more objective. First, \(p\)-values are usually classified as “non-significant” (\(p > .05\)), “significant” (\(p < .05\)), “highly significant”, and so on. Not only that these thresholds and labels are largely arbitrary, they also promote publication bias : non-significant findings are often classified as “failed studies” (i.e., the efficacy of the intervention could not be shown), rarely published and end up in the proverbial “file drawer”. Much valuable research is suppressed. Conversely, significant findings may often occur when the null hypothesis is actually true, especially when researchers have been “hunting for significance”. In fact, researchers have an incentive to keep their \(p\)-values low: the stronger the evidence, the more convincing the narrative, the greater the impact—and the higher the chance for a good publication and career-relevant rewards. Moving the goalpost by “p-hacking” outcomes—for example by eliminating outliers, selective reporting or restricting the analysis to a subgroup—evidently biases the research results and compromises the objectivity of experimental research.

In particular, such questionable research practices (QRP) increase the type I error rate, which measures the rate at which false hypotheses are accepted, substantially over its nominal 5% level and contribute to publication bias (Bakker et al. 2012). Ioannidis (2005) concludes that “most published research findings are false”—they are the combined result of a low base rate of effective causal interventions, the file drawer effect and the widespread presence of questionable research practices. The frequentist logic of hypothesis testing aggravates the problem because it provides a framework where all these biases can easily enter (Ziliak and McCloskey 2008; Sprenger 2016). These radical conclusions are also confirmed by empirical findings: in many disciplines researchers fail to replicate findings by other scientific teams. See section 5.1 for more detail.

Summing up our findings, neither of the two major frameworks of statistical inference manages to eliminate all sources of personal bias and idiosyncrasy. The Bayesian considers subjective assumptions to be an irreducible part of scientific reasoning and sees no harm in making them explicit. The frequentist conception of evidence based on \(p\)-values avoids these explicitly subjective elements, but at the price of a misleading impression of objectivity and frequent abuse in practice. A defense of frequentist inference should, in our opinion, stress that the relatively rigid rules for interpreting statistical evidence facilitate communication and assessment of research results in the scientific community—something that is harder to achieve for a Bayesian. We now turn from specific methods for stating and interpreting evidence to a radical criticism of the idea that there is a rational scientific method.

In his writings of the 1970s, Paul Feyerabend launched a profound attack on the rationality and objectivity of scientific method. His position is exceptional in the philosophical literature since traditionally, the threat for objective and successful science is located in contextual rather than epistemic values. Feyerabend turns this view upside down: it is the “tyranny” of rational method, and the emphasis on epistemic rather than contextual values that prevents us from having a science in the service of society. Moreover, he welcomes a diversity of different personal, also idiosyncratic perspectives, thus denying the idea that freedom from personal “bias” is epistemically and socially beneficial.

The starting point of Feyerabend’s criticism of rational method is the thesis that strict epistemic rules such as those expressed by the VFI only suppress an open exchange of ideas, extinguish scientific creativity and prevent a free and truly democratic science. In his classic “Against Method” (1975: chs. 8–13), Feyerabend elaborates on this criticism by examining a famous episode in the history of science. When the Catholic Church objected to Galilean mechanics, it had the better arguments by the standards of seventeenth-century science. Their conservatism in their position was scientifically backed: Galilei’s telescopes were unreliable for celestial observations, and many well-established phenomena (no fixed star parallax, invariance of laws of motion) could not yet be explained in the heliocentric system. With hindsight, Galilei managed to achieve groundbreaking scientific progress just because he deliberately violated rules of scientific reasoning. Hence Feyerabend’s dictum “Anything goes”: no methodology whatsoever is able to capture the creative and often irrational ways by which science deepens our understanding of the world. Good scientific reasoning cannot be captured by rational method, as Carnap, Hempel and Popper postulated.

The drawbacks of an objective, value-free and method-bound view on science and scientific method are not only epistemic. Such a view narrows down our perspective and makes us less free, open-minded, creative, and ultimately, less human in our thinking (Feyerabend 1975: 154). It is therefore neither possible nor desirable to have an objective, value-free science (cf. Feyerabend 1978: 78–79). As a consequence, Feyerabend sees traditional forms of inquiry about our world (e.g., Chinese medicine) on a par with their Western competitors. He denounces appeals to “objective” standards as rhetorical tools for bolstering the epistemic authority of a small intellectual elite (=Western scientists), and as barely disguised statements of preference for one’s own worldview:

there is hardly any difference between the members of a “primitive” tribe who defend their laws because they are the laws of the gods […] and a rationalist who appeals to “objective” standards, except that the former know what they are doing while the latter does not. (1978: 82)

In particular, when discussing other traditions, we often project our own worldview and value judgments into them instead of making an impartial comparison (1978: 80–83). There is no purely rational justification for dismissing other perspectives in favor of the Western scientific worldview—the insistence on our Western approach may be as justified as insisting on absolute space and time after the Theory of Relativity.

The Galilei example also illustrates that personal perspective and idiosyncratic “bias” need not be bad for science. Feyerabend argues further that scientific research is accountable to society and should be kept in check by democratic institutions, and laymen in particular. Their particular perspectives can help to determine the funding agenda and to set ethical standards for scientific inquiry, but also be useful for traditionally value-free tasks such as choosing an appropriate research method and assessing scientific evidence. Feyerabend’s writings on this issue were much influenced by witnessing the Civil Rights Movement in the U.S. and the increasing emancipation of minorities, such as Blacks, Asians and Hispanics.

All this is not meant to say that truth loses its function as a normative concept, nor that all scientific claims are equally acceptable. Rather, Feyerabend advocates an epistemic pluralism that accepts diverse approaches to acquiring knowledge. Rather than defending a narrow and misleading ideal of objectivity, science should respect the diversity of values and traditions that drive our inquiries about the world (1978: 106–107). This would put science back into the role it had during the scientific revolution or the Enlightenment: as a liberating force that fought intellectual and political oppression by the sovereign, the nobility or the clergy. Objections to this view are discussed at the end of section 5.2 .

5. Objectivity as a Feature of Scientific Communities and Their Practices

This section addresses various accounts that regard scientific objectivity essentially as a function of social practices in science and the social organization of the scientific community. All these accounts reject the characterization of scientific objectivity as a function of correspondence between theories and the world, as a feature of individual reasoning practices, or as pertaining to individual studies and experiments (see also Douglas 2011). Instead, they evaluate the objectivity of a collective of studies, as well as the methods and community practices that structure and guide scientific research. More precisely, they adopt a meta-analytic perspective for assessing the reliability of scientific results (section 5.1), and they construct objectivity from a feminist perspective: as an open interchange of mutual criticism, or as being anchored in the “situatedness” of our scientific practices and the knowledge we gain ( section 5.2 ).

The collectivist perspective is especially useful when an entire discipline enters a stage of crisis: its members become convinced that a significant proportion of findings are not trustworthy. A contemporary example of such a situation is the replication crisis , which was briefly mentioned in the previous section and concerns the reproducibility of scientific knowledge claims in a variety of different fields (most prominently: psychology, biology, medicine). Large-scale replication projects have noticed that many findings which we considered as an integral part of scientific knowledge failed to replicate in settings that were designed to mimic the original experiment as closely as possible (e.g., Open Science Collaboration 2015). Successful attempts at replicating an experimental result have long been argued to provide evidence of freedom from particular kinds of artefacts and thus the trustworthiness of the result. Compare the entry on experiment in physics . Likewise, failure to replicate indicates that either the original finding, the result of the replication attempt, or both, are biased—though see John Norton’s (ms., ch. 3—see Other Internet Resources) arguments that the evidential value of (failed) replications crucially depends on researchers’ material background assumptions.

When replication failures in a discipline are particularly significant, one may conclude that the published literature lacks objectivity—at a minimum the discipline fails to inspire trust that its findings are more than artefacts of the researchers’ efforts. Conversely, when observed effects can be replicated in follow-up experiments, a kind of objectivity is reached that goes beyond the ideas of freedom from personal bias, mechanical objectivity, and subject-independent measurement, discussed in section 4.1 .

Freese and Peterson (2018) call this idea statistical objectivity . It grounds in the view that even the most scrupulous and diligent researchers cannot achieve full objectivity all by themselves. The term “objectivity” instead applies to a collection or population of studies, with meta-analysis (a formal method for aggregating the results from ranges of studies) as the “apex of objectivity” (Freese and Peterson 2018, 304; see also Stegenga 2011, 2018). In particular, aggregating studies from different researchers may provide evidence of systematic bias and questionable research practices (QRP) in the published literature. This diagnostic function of meta-analysis for detecting violations of objectivity is enhanced by statistical techniques such as the funnel plot and the \(p\)-curve (Simonsohn et al. 2014).

Apart from this epistemic dimension, research on statistical objectivity also has an activist dimension: methodologists urge researchers to make publicly available essential parts of their research before the data analysis starts, and to make their methods and data sources more transparent. For example, it is conjectured that the replicability (and thus objectivity) of science will increase by making all data available online, by preregistering experiments, and by using the registered reports model for journal articles (i.e., the journal decides on publication before data collection on the basis of the significance of the proposed research as well as the experimental design). The idea is that transparency about the data set and the experimental design will make it easier to stage a replication of an experiment and to assess its methodological quality. Moreover, publicly committing to a data analysis plan beforehand will lower the rate of QRPs and of attempts to accommodate data to hypotheses rather than making proper predictions.

All in all, statistical objectivity moves the discussion of objectivity to the level of population of studies. There, it takes up and modifies several conceptions of objectivity that we have seen before: most prominently, freedom of subjective bias, which is replaced with collective bias and pernicious conventions, and the subject-independent measurement of a physical quantity, which is replaced by reproducibility of effects.

Traditional notions of objectivity as faithfulness to facts or freedom of contextual values have also been challenged from a feminist perspective. These critiques can be grouped in three major research programs: feminist epistemology, feminist standpoint theory and feminist postmodernism (Crasnow 2013). The program of feminist epistemology explores the impact of sex and gender on the production of scientific knowledge. More precisely, feminist epistemology highlights the epistemic risks resulting from the systematic exclusion of women from the ranks of scientists, and the neglect of women as objects of study. Prominent case studies are the neglect of female orgasm in biology, testing medical drugs on male participants only, focusing on male specimen when studying the social behavior of primates, and explaining human mating patterns by means of imaginary neolithic societies (e.g., Hrdy 1977; Lloyd 1993, 2005). See also the entry on feminist philosophy of biology .

Often but not always, feminist epistemologists go beyond pointing out what they regard as androcentric bias and reject the value-free ideal altogether—with an eye on the social and moral responsibility of scientific inquiry. They try to show that a value-laden science can also meet important criteria for being epistemically reliable and objective (e.g., Anderson 2004; Kourany 2010). A classical representative of such efforts is Longino’s (1990) contextual empiricism . She reinforces Popper’s insistence that “the objectivity of scientific statements lies in the fact that they can be inter-subjectively tested” (1934 [2002]: 22), but unlike Popper, she conceives scientific knowledge essentially as a social product. Thus, our conception of scientific objectivity must directly engage with the social process that generates knowledge. Longino assigns a crucial function to social systems of criticism in securing the epistemic success of science. Specifically, she develops an epistemology which regards a method of inquiry as “objective to the degree that it permits transformative criticism ” (Longino 1990: 76). For an epistemic community to achieve transformative criticism, there must be:

avenues for criticism : criticism is an essential part of scientific institutions (e.g., peer review);

shared standards : the community must share a set of cognitive values for assessing theories (more on this in section 3.1 );

uptake of criticism : criticism must be able to transform scientific practice in the long run;

equality of intellectual authority : intellectual authority must be shared equally among qualified practitioners.

Longino’s contextual empiricism can be understood as a development of John Stuart Mill’s view that beliefs should never be suppressed, independently of whether they are true or false. Even the most implausible beliefs might be true, and even if they are false, they might contain a grain of truth which is worth preserving or helps to better articulate true beliefs (Mill 1859 [2003: 72]). The underlying intuition is supported by recent empirical research on the epistemic benefits of a diversity of opinions and perspectives (Page 2007). By stressing the social nature of scientific knowledge, and the importance of criticism (e.g., with respect to potential androcentric bias and inclusive practice), Longino’s account fits into the broader project of feminist epistemology.

Standpoint theory undertakes a more radical attack on traditional scientific objectivity. This view develops Marxist ideas to the effect that epistemic position is related to, and a product of, social position. Feminist standpoint theory builds on these ideas but focuses on gender, racial and other social relations. Feminist standpoint theorists and proponents of “situated knowledge” such as Donna Haraway (1988), Sandra Harding (1991, 2015a, 2015b) and Alison Wylie (2003) deny the internal coherence of a view from nowhere: all human knowledge is at base human knowledge and therefore necessarily perspectival. But they argue more than that. Not only is perspectivality the human condition, it is also a good thing to have. This is because perspectives, especially the perspectives of underprivileged classes and groups in society, come along with epistemic benefits. These ideas are controversial but they draw attention to the possibility that attempts to rid science of perspectives might not only be futile but also costly: they prevent scientists from having the epistemic benefits certain standpoints afford and from developing knowledge for marginalized groups in society. The perspectival stance can also explain why criteria for objectivity often vary with context: the relative importance of epistemic virtues is a matter of goals and interests—in other words, standpoint.

By endorsing a perspectival stance, feminist standpoint theory rejects classical elements of scientific objectivity such as neutrality and impartiality (see section 3.1 above). This is a notable difference to feminist epistemology, which is in principle (though not always in practice) compatible with traditional views of objectivity. Feminist standpoint theory is also a political project. For example, Harding (1991, 1993) demands that scientists, their communities and their practices—in other words, the ways through which knowledge is gained—be investigated as rigorously as the object of knowledge itself. This idea she refers to as “strong objectivity” replaces the “weak” conception of objectivity in the empiricist tradition: value-freedom, impartiality, rigorous adherence to methods of testing and inference. Like Feyerabend, Harding integrates a transformation of epistemic standards in science into a broader political project of rendering science more democratic and inclusive. On the other hand, she is exposed to similar objections (see also Haack 2003). Isn’t it grossly exaggerated to identify class, race and gender as important factors in the construction of physical theories? Doesn’t the feminist approach—like social constructivist approaches—lose sight of the particular epistemic qualities of science? Should non-scientists really have as much authority as trained scientists? To whom does the condition of equally shared intellectual authority apply? Nor is it clear—especially in times of fake news and filter bubbles—whether it is always a good idea to subject scientific results to democratic approval. There is no guarantee (arguably there are few good reasons to believe) that democratized or standpoint-based science leads to more reliable theories, or better decisions for society as a whole.

6. Issues in the Special Sciences

So far everything we discussed was meant to apply across all or at least most of the sciences. In this section we will look at a number of specific issues that arise in the social sciences, in economics, and in evidence-based medicine.

There is a long tradition in the philosophy of social science maintaining that there is a gulf in terms of both goals as well as methods between the natural and the social sciences. This tradition, associated with thinkers such as the neo-Kantians Heinrich Rickert and Wilhelm Windelband, the hermeneuticist Wilhelm Dilthey, the sociologist-economist Max Weber, and the twentieth-century hermeneuticists Hans-Georg Gadamer and Michael Oakeshott, holds that unlike the natural sciences whose aim it is to establish natural laws and which proceed by experimentation and causal analysis, the social sciences seek understanding (“ Verstehen ”) of social phenomena, the interpretive examination of the meanings individuals attribute to their actions (Weber 1904 [1949]; Weber 1917 [1949]; Dilthey 1910 [1986]; Windelband 1915; Rickert 1929; Oakeshott 1933; Gadamer 1960 [1989]). See also the entries on hermeneutics and Max Weber .

Understood this way, social science lacks objectivity in more than one sense. One of the more important debates concerning objectivity in the social sciences concerns the role value judgments play and, importantly, whether value-laden research entails claims about the desirability of actions. Max Weber held that the social sciences are necessarily value laden. However, they can achieve some degree of objectivity by keeping out the social researcher’s views about whether agents’ goals are commendable. In a similar vein, contemporary economics can be said to be value laden because it predicts and explains social phenomena on the basis of agents’ preferences. Nevertheless, economists are adamant that economists are not in the business of telling people what they ought to value. Modern economics is thus said to be objective in the Weberian sense of “absence of researchers’ values” —a conception that we discussed in detail in section 3 .

In his widely cited essay “‘Objectivity’ in Social Science and Social Policy” (Weber 1904 [1949]), Weber argued that the idea of an aperspectival social science was meaningless:

There is no absolutely objective scientific analysis of […] “social phenomena” independent of special and “one-sided” viewpoints according to which expressly or tacitly, consciously or unconsciously they are selected, analyzed and organized for expository purposes. (1904 [1949: 72]) All knowledge of cultural reality, as may be seen, is always knowledge from particular points of view. (1904 [1949:. 81])

The reason for this is twofold. First, social reality is too complex to admit of full description and explanation. So we have to select. But, perhaps in contraposition to the natural sciences, we cannot just select those aspects of the phenomena that fall under universal natural laws and treat everything else as “unintegrated residues” (1904 [1949: 73]). This is because, second, in the social sciences we want to understand social phenomena in their individuality, that is, in their unique configurations that have significance for us.

Values solve a selection problem. They tell us what research questions we ought to address because they inform us about the cultural importance of social phenomena:

Only a small portion of existing concrete reality is colored by our value-conditioned interest and it alone is significant to us. It is significant because it reveals relationships which are important to use due to their connection with our values. (1904 [1949: 76])

It is important to note that Weber did not think that social and natural science were different in kind, as Dilthey and others did. Social science too examines the causes of phenomena of interest, and natural science too often seeks to explain natural phenomena in their individual constellations. The role of causal laws is different in the two fields, however. Whereas establishing a causal law is often an end in itself in the natural sciences, in the social sciences laws play an attenuated and accompanying role as mere means to explain cultural phenomena in their uniqueness.

Nevertheless, for Weber social science remains objective in at least two ways. First, once research questions of interest have been settled, answers about the causes of culturally significant phenomena do not depend on the idiosyncrasies of an individual researcher:

But it obviously does not follow from this that research in the cultural sciences can only have results which are “subjective” in the sense that they are valid for one person and not for others. […] For scientific truth is precisely what is valid for all who seek the truth. (Weber 1904 [1949: 84], emphasis original)

The claims of social science can therefore be objective in our third sense ( see section 4 ). Moreover, by determining that a given phenomenon is “culturally significant” a researcher reflects on whether or not a practice is “meaningful” or “important”, and not whether or not it is commendable: “Prostitution is a cultural phenomenon just as much as religion or money” (1904 [1949: 81]). An important implication of this view came to the fore in the so-called “ Werturteilsstreit ” (quarrel concerning value judgments) of the early 1900s. In this debate, Weber maintained against the “socialists of the lectern” around Gustav Schmoller the position that social scientists qua scientists should not be directly involved in policy debates because it was not the aim of science to examine the appropriateness of ends. Given a policy goal, a social scientist could make recommendations about effective strategies to reach the goal; but social science was to be value-free in the sense of not taking a stance on the desirability of the goals themselves. This leads us to our conception of objectivity as freedom from value judgments.

Contemporary mainstream economists hold a view concerning objectivity that mirrors Max Weber’s (see above). On the one hand, it is clear that value judgments are at the heart of economic theorizing. “Preferences” are a key concept of rational choice theory, the main theory in contemporary mainstream economics. Preferences are evaluations. If an individual prefers \(A\) to \(B\), she values \(A\) higher than \(B\) (Hausman 2012). Thus, to the extent that economists predict and explain market behavior in terms of rational choice theory, they predict and explain market behavior in a way laden with value judgments.

However, economists are not themselves supposed to take a stance about whether or not whatever individuals value is also “objectively” good in a stronger sense:

[…] that an agent is rational from [rational choice theory]’s point of view does not mean that the course of action she will choose is objectively optimal. Desires do not have to align with any objective measure of “goodness”: I may want to risk swimming in a crocodile-infested lake; I may desire to smoke or drink even though I know it harms me. Optimality is determined by the agent’s desires, not the converse. (Paternotte 2011: 307–8)

In a similar vein, Gul and Pesendorfer write:

However, standard economics has no therapeutic ambition, i.e., it does not try to evaluate or improve the individual’s objectives. Economics cannot distinguish between choices that maximize happiness, choices that reflect a sense of duty, or choices that are the response to some impulse. Moreover, standard economics takes no position on the question of which of those objectives the agent should pursue. (Gul and Pesendorfer 2008: 8)

According to the standard view, all that rational choice theory demands is that people’s preferences are (internally) consistent; it has no business in telling people what they ought to prefer, whether their preferences are consistent with external norms or values. Economics is thus value-laden, but laden with the values of the agents whose behavior it seeks to predict and explain and not with the values of those who seek to predict and explain this behavior.

Whether or not social science, and economics in particular, can be objective in this—Weber’s and the contemporary economists’—sense is controversial. On the one hand, there are some reasons to believe that rational choice theory (which is at work not only in economics but also in political science and other social sciences) cannot be applied to empirical phenomena without referring to external norms or values (Sen 1993; Reiss 2013).

On the other hand, it is not clear that economists and other social scientists qua social scientists shouldn’t participate in a debate about social goals. For one thing, trying to do welfare analysis in the standard Weberian way tends to obscure rather than to eliminate normative commitments (Putnam and Walsh 2007). Obscuring value judgments can be detrimental to the social scientist as policy adviser because it will hamper rather than promote trust in social science. For another, economists are in a prime position to contribute to ethical debates, for a variety of reasons, and should therefore take this responsibility seriously (Atkinson 2001).

The same demands calling for “mechanical objectivity” in the natural sciences and quantification in the social and policy sciences in the nineteenth century and mid-twentieth century are responsible for a recent movement in biomedical research, which, even more recently, have swept to contemporary social science and policy. Early proponents of so-called “evidence-based medicine” made their pursuit of a downplay of the “human element” in medicine plain:

Evidence-based medicine de-emphasizes intuition, unsystematic clinical experience, and pathophysiological rationale as sufficient grounds for clinical decision making and stresses the examination of evidence from clinical research. (Guyatt et al. 1992: 2420)

To call the new movement “evidence-based” is a misnomer strictly speaking, as intuition, clinical experience and pathophysiological rationale can certainly constitute evidence. But proponents of evidence-based practices have a much narrower concept of evidence in mind: analyses of the results of randomized controlled trials (RCTs). This movement is now very strong in biomedical research, development economics and a number of areas of social science, especially psychology, education and social policy, and especially in the English speaking world.

The goal is to replace subjective (biased, error-prone, idiosyncratic) judgments by mechanically objective methods. But, as in other areas, attempting to mechanize inquiry can lead to reduced accuracy and utility of the results.

Causal relations in the social and biomedical sciences hold on account of highly complex arrangements of factors and conditions. Whether for instance a substance is toxic depends on details of the metabolic system of the population ingesting it, and whether an educational policy is effective on the constellation of factors that affect the students’ learning progress. If an RCT was conducted successfully, the conclusion about the effectiveness of the treatment (or toxicity of a substance) under test is certain for the particular arrangement of factors and conditions of the trial (Cartwright 2007). But unlike the RCT itself, many of whose aspects can be (relatively) mechanically implemented, applying the result to a new setting (recommending a treatment to a patient, for instance) always involves subjective judgments of the kind proponents of evidence-based practices seek to avoid—such as judgments about the similarity of the test to the target or policy population.

On the other hand, RCTs can be regarded as “debiasing procedure” because they prevent researchers from allocating treatments to patients according to their personal interests, so that the healthiest (or smartest or…) subjects get the researcher’s favorite therapy. While unbalanced allocations can certainly happen by chance, randomization still provides some warrant that the allocation was not done on purpose with a view to promoting somebody’s interests. A priori , the experimental procedure is thus more impartial with respect to the interests at stake. It has thus been argued that RCTs in medicine, while no guarantor of the best outcomes, were adopted by the U.S. Food and Drugs Administration (FDA) to different degrees during the 1960s and 1970s in order to regain public trust in its decisions about treatments, which it had lost due to the thalidomide and other scandals (Teira and Reiss 2013; Teira 2010). It is important to notice, however, that randomization is at best effective with respect to one kind of bias, viz. selection bias. Important other epistemic concerns are not addressed by the procedure but should not be ignored (Worrall 2002).

In sections 2–5, we have encountered various concepts of scientific objectivity and their limitations. This prompts the question of how unified (or disunified) scientific objectivity is as a concept: Is there something substantive shared by all of these analyses? Or is objectivity, as Heather Douglas (2004) puts it, an “irreducibly complex” concept?

Douglas defends pluralism about scientific objectivity and distinguishes three areas of application of the concept: (1) interaction of humans with the world, (2) individual reasoning processes, (3) social processes in science. Within each area, there are various distinct senses which are again irreducible to each other and do not have a common core meaning. This does not mean that the senses are unrelated; they share a complex web of relationships and can also support each other—for example, eliminating values from reasoning may help to achieve procedural objectivity. For Douglas, reducing objectivity to a single core meaning would be a simplification without benefits; instead of a complex web of relations between different senses of objectivity we would obtain an impoverished concept out of touch with scientific practice. Similar arguments and pluralist accounts can be found in Megill (1994), Janack (2002) and Padovani et al. (2015)—see also Axtell (2016).

It has been argued, however, that pluralist approaches give up too quickly on the idea that the different senses of objectivity share one or several important common elements. As we have seen in section 4.1 and 5.1 , scientific objectivity and trust in science are closely connected. Scientific objectivity is desirable because to the extent that science is objective we have reasons trust scientists, their results and recommendations (cf. Fine 1998: 18). Thus, perhaps what is unifying among the difference senses of objectivity is that each sense describes a feature of scientific practice that is able to inspire trust in science.

Building on this idea, Inkeri Koskinen has recently argued that it is in fact not trust but reliance that we are after (Koskinen forthcoming). Trust is something that can be betrayed, but only individuals can betray whereas objectivity pertains to institutions, practices, results, etc. We call scientific institutions, practices, results, etc. objective to the extent that we have reasons to rely on them. The analysis does not stop here, however. There is a distinct view about objectivity that is behind Daston and Galison’s historical epistemology of the concept and has been defended by Ian Hacking: that objectivity is not a—positive—virtue but rather the absence of this or that vice (Hacking 2015: 26). Speaking of objectivity in imaging, for instance, Daston and Galison write that the goal is to

let the specimen appear without that distortion characteristic of the observer’s personal tastes, commitments, or ambitions. (Daston and Galison 2007: 121)

Koskinen picks up this idea of objectivity as absence of vice and argues that it is specifically the aversion of epistemic risks for which the term is reserved. Epistemic risks comprise “any risk of epistemic error that arises anywhere during knowledge practices’ (Biddle and Kukla 2017: 218) such as the risk of having mistaken beliefs, the risk of errors in reasoning and risks related to operationalization, concept formation, and model choice. Koskinen argues that only those epistemic risks that relate to failings of scientists as human beings are relevant to objectivity (Koskinen forthcoming: 13):

For instance, when the results of an experiment are incorrect because of malfunctioning equipment, we do not worry about objectivity—we just say that the results should not be taken into account. [...] So it is only when the epistemic risk is related to our own failings, and is hard to avert, that we start talking about objectivity. Illusions, subjectivity, idiosyncrasies, and collective biases are important epistemic risks arising from our imperfections as epistemic agents.

Koskinen understands her account as a response to Hacking’s (2015) criticism that we should stop talking about objectivity altogether. According to Hacking, “objectivity” is an “elevator” or second-level word, similar to “true” or “real”—“Instead of saying that the cat is on the mat, we move up one story and and say that it is true that the cat is on the mat” (2015: 20). He recommends to stick to ground-level questions and worry about whether specific sources of error have been controlled. (A similar elimination request with respect to the labels “objective” and “subjective” in statistical inference has been advanced by Gelman and Hennig (2017).) In focussing on averting specific epistemic risks, Koskinen’s account does precisely that. Koskinen argues that a unified account of objectivity as averting epistemic risks takes into account Hacking’s negative stance and explains at the same time important features of the concept—for example, why objectivity does not imply certainty and why it varies with context.

The strong point of this account is that none of the threats to a peculiar analysis puts scientific objectivity at risk. We can (and in fact, we do) rely on scientific practices that represent the world from a perspective and where non-epistemic values affect outcomes and decisions. What is left open by Koskinen’s account is the normative question of what a scientist who cares about her experiments and inferences being objective should actually do. That is, the philosophical ideas we have reviewed in this section stay mainly on the descriptive level and do not give an actual guideline for working scientists. Connecting the abstract philosophical analysis to day-to-day work in science remains an open problem.

So is scientific objectivity desirable? Is it attainable? That, as we have seen, depends crucially on how the term is understood. We have looked in detail at four different conceptions of scientific objectivity: faithfulness to facts, value-freedom, freedom from personal biases, and features of community practices. In each case, there are at least some reasons to believe that either science cannot deliver full objectivity in this sense, or that it would not be a good thing to try to do so, or both. Does this mean we should give up the idea of objectivity in science?

We have shown that it is hard to define scientific objectivity in terms of a view from nowhere, value freedom, or freedom from personal bias. It is a lot harder to say anything positive about the matter. Perhaps it is related to a thorough critical attitude concerning claims and findings, as Popper thought. Perhaps it is the fact that many voices are heard, equally respected and subjected to accepted standards, as Longino defends. Perhaps it is something else altogether, or a combination of several factors discussed in this article.

However, one should not (as yet) throw out the baby with the bathwater. Like those who defend a particular explication of scientific objectivity, the critics struggle to explain what makes science objective, trustworthy and special. For instance, our discussion of the value-free ideal (VFI) revealed that alternatives to the VFI are as least as problematic as the VFI itself, and that the VFI may, with all its inadequacies, still be a useful heuristic for fostering scientific integrity and objectivity. Similarly, although entirely “unbiased” scientific procedures may be impossible, there are many mechanisms scientists can adopt for protecting their reasoning against undesirable forms of bias, e.g., choosing an appropriate method of statistical inference, being transparent about different stages of the research process and avoiding certain questionable research practices.

Whatever it is, it should come as no surprise that finding a positive characterization of what makes science objective is hard. If we knew an answer, we would have done no less than solve the problem of induction (because we would know what procedures or forms of organization are responsible for the success of science). Work on this problem is an ongoing project, and so is the quest for understanding scientific objectivity.

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• Norton, John, manuscript, The Material Theory of Induction , retrieved on 9 January 2020.
• Objectivity , entry by Dwayne H. Mulder in the Internet Encyclopedia of Philosophy .

Bayes’ Theorem | confirmation | feminist philosophy, interventions: epistemology and philosophy of science | feminist philosophy, interventions: philosophy of biology | Feyerabend, Paul | hermeneutics | incommensurability: of scientific theories | Kuhn, Thomas | logic: inductive | physics: experiment in | science: theory and observation in | scientific realism | statistics, philosophy of | underdetermination, of scientific theories | Weber, Max

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The Stanford Encyclopedia of Philosophy is copyright © 2023 by The Metaphysics Research Lab , Department of Philosophy, Stanford University

Library of Congress Catalog Data: ISSN 1095-5054

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TWU philosophy professor awarded Templeton Religion Trust grant for joint research on beauty and truth in science and religion 

Exploring how art can speak the language of science

What insights arise when a philosopher and a chemist collaborate to answer questions about truth and beauty? How do concepts of beauty help us discover scientific knowledge and inform our understanding of reality? 

When scientists use aesthetic criteria in their scientific work, it can open new realms of discovery. 

TWU philosophy professor Dr. Myron A. Penner , together with co-investigator and chemistry professor Dr. Amanda J. Nichols (Oklahoma Christian University), are researching how properties like beauty, simplicity, and elegance can communicate information that increases scientific understanding. 

“Sometimes scientists will be struck by the aesthetic qualities of an object they are dealing with, whether that’s a theory, or an observation in the lab, or even a particular experimental process,” Dr. Penner, who teaches in TWU’s graduate MA in Interdisciplinary Humanities program and undergraduate philosophy courses, begins.  

Take symmetry, for example. Symmetry is found in many fields of scientific inquiry, from cosmology down to molecular design.  

“When you look at the properties of molecular structures,” Dr. Penner continues, “there’s an elegance and theoretical beauty in how molecular structures can be classified according to their symmetry properties and how those classifications explain how the molecule will behave in different conditions."  

In this way, he indicates, a molecular structure can be a sort of “scientific work of art.” 

“We are looking at how scientists engage different ‘scientific works of art’ in order to describe a science-informed model for how these ‘works of art’ communicate scientific information that can increase scientific understanding."

Molecular structure happens to be an area of expertise for Dr. Nichols, who has previously conducted research exploring the roles that aesthetic judgment played in the development of molecular models. 

Now Drs. Nichols and Penner are seeking to understand what role artistic sensibilities play in supporting scientific inquiry. 

“We are looking at how scientists engage different ‘scientific works of art’ in order to describe a science-informed model for how these ‘works of art’ communicate scientific information that can increase scientific understanding,” Dr. Penner describes.  

Drawings, shapes, and models are often used to depict molecular compounds. 

"One historical example we are investigating is the way chemists in the nineteenth century used geometric shapes as models for certain molecular compounds,” he illustrates. “Since at least the 6th century BC, mathematicians and philosophers were drawn to what came to be known as the five ‘platonic solids.’”  

The platonic solids are shapes possessing such balance and harmony that they have inspired wonder and curiosity in mathematicians, scientists, and artists for centuries. As Penner explains, “These shapes, which include cubes and pyramids, are aesthetically pleasing and mathematically interesting because each surface plane has an identical size and shape.” 

Could beauty, like that found in geometric shapes, help us to better understand scientific descriptions of the world that lies beyond what’s observed by the naked eye?  

“Fast forward to the 19th century when chemists were developing ideas of how atoms bonded to form molecules,” Penner continues, “Some thought that the elegance of platonic shapes accurately depicted how atoms were connected to each other.”  

“It turns out that in some cases, they were right." 

The way that beauty can reveal scientific truth is what motivates Drs. Nichols and Penner in their research.     “When I first learned about molecular symmetry theory as an undergraduate and saw how the aesthetic features of a molecule explain chemical behavior, I was in awe of the theory, and like many chemists, struck by how beautiful molecules are,” Drs. Nichols remarked. “I’m grateful to have had the opportunity to think more deeply about molecular symmetry along with historians and philosophers of science.” 

“We intend to find out if this science-informed model can provide new language and categories for describing how, in religious contexts, aesthetic judgments might communicate spiritually significant information that increases human understanding.”  

What’s more, their investigation into scientific practices might help us better understand aspects of religion and spirituality as well. Drs. Nichols and Penner are keen to uncover how beauty conveys truth in other ways:  

“Understanding how aesthetic qualities communicate information in one domain, such as science, might provide a model for describing how aesthetic qualities are perceived to communicate information in religious contexts,” they indicate. 

Their project, Beauty and Truth in Science and Religion, is a three-year (2023–2026) project funded by a Templeton Religion Trust grant ($250,910 USD) and involves a formal partnership with Oklahoma Christian University. They also have project collaborators at Point Loma Nazarene University, the University of Nottingham, Baylor University, and the University of British Columbia. The opportunity to apply for this funding was made possible by an earlier grant given by Bridging the Two Cultures of Science and the Humanities II, a project run by Scholarship and Christianity in Oxford, the UK subsidiary of the Council for Christian Colleges and Universities, with funding by Templeton Religion Trust and the Blankemeyer Foundation.  "Trinity Western University seeks to contribute a unique and valuable perspective that integrates faith and reason as part of high-quality research aimed at gaining a greater understanding of the world,"  Dr. Richard Chandra , associate provost of research, remarks "We are excited to see TWU faculty members engaging in interdisciplinary research that spans academic and international boundaries and are grateful to the support provided by the Templeton Religion Trust."    "Dr. Penner’s research is the type of work that addresses big questions and demonstrates the need to work across disciplines,"  Dr. Michael Wilkinson , dean of the Faculty of Humanities and Social Sciences, adds. "We congratulate Dr. Penner for receiving this grant and the many opportunities that will come for sharing the findings more broadly." 

 The Beauty and Truth in Science and Religion project builds on builds on Drs. Penner and Nichols' previous work in philosophy of science. Since 2019 they've co-authored three articles on topics in the philosophy of chemistry. They expect that this project will result in at least three more peer-reviewed publications.  Learn more about Dr. Penner's research here .   

About Research at TWU

TWU researchers are award-winning professors, nationally recognized educators, and intellectually engaged students. Together, they are enriching our programming, contributing to society, and revealing the necessity of faith integration in research. Learn more at  TWU Research .

About Trinity Western University

Founded in 1962, Trinity Western University is a global Christian liberal arts university. We are dedicated to equipping students to discover meaningful connections between career, life, and the needs of the world. Drawing upon the riches of the Christian tradition, seeking to unite faith and reason through teaching and scholarship, Trinity Western University is a degree-granting research institution offering liberal arts and sciences as well as professional schools in business, nursing, education, human kinetics, graduate studies, and arts, media, and culture. It has campuses in Canada in Langley, Richmond, and Ottawa. Learn more at  www.twu.ca  or follow us on Instagram  @trinitywestern , Twitter  @TrinityWestern , on  Facebook  and  LinkedIn .

For media inquiries, please contact:  media @twu.ca .

Guidance on energy and macronutrients across the lifespan

In the long history of recommendations for nutritional intake, current research is trending toward the concept of "food as medicine" -- a philosophy in which food and nutrition are positioned within interventions to support health and wellness. In the paper -- "Guidance on Energy and Macronutrients Across the Lifespan" -- by Pennington Biomedical Research Center's Dr. Steven Heymsfield, he shares the latest clarity and recommendations in the rich and storied history of energy and macronutrient intake.

The research paper by Dr. Heymsfield and colleague Dr. Sue Shapses, Professor of Nutritional Sciences at Rutgers University and Director of the Next Center at the New Jersey Institute for Food, Nutrition and Health, was recently published in the New England Journal of Medicine , showcasing recommendations with increased clarity for protein, fat, carbohydrates, fiber and water intake at various stages in the human lifespan.

"Couple with the amount and pattern of the foods people eat, the primary macronutrients of protein, carbohydrates and fat can shape the major determinates of health throughout the lifespan," said Dr. Heymsfield, who is a professor of Metabolism & Body Composition at Pennington Biomedical. "Even considering the incredible diversity of traits and nutritional needs across the global population, we can potentially provide effective care for all patients, including the growing number of patients with diet-related diseases, so long as we recognize the subtle effects of the key macronutrients."

Throughout the research document, the authors frequently reference the original, historic research for which they are providing the latest incarnation and related knowledge. Focusing primarily on energy and three macronutrients -- protein, carbohydrates and fat, and their subsequent substrates -- amino acids, glucose and free fatty acids, the paper shows how these can fuel growth and maintenance throughout life. For optimal health, the study provides dietary reference intakes for the three micronutrients at various stages: 0 to 6 months, 7 months to slightly less than a year old, one year to three, four to eight years, nine to 13 years, 14 to 18 years, over 19 years, and then additional recommendations for pregnancy and lactation.

The research goes on to provide recommendations to patients and caregivers on healthy eating patterns consistent with the energy and macronutrient guidelines and includes an online calculator. While the energy requirements and variable needs for the three main macronutrients and multiple micronutrients vary across the nine life stage groups, there are overarching nutritional goals for patients when choosing healthy food patterns. A variety of healthy meal pattern examples are available, but reoccurring components feature the inclusion of vegetables of all types, whole fruits, fat-free or low-fat dairy, lean meats, seafood, eggs, beans, and nuts, plant- and seafood-based oils, and grains, with at least half of those being whole grains.

The need to incorporate the three main macronutrient groups and micronutrients into the diets of the various life stage groups is a matrix that is further complicated as varying financial resources, personal preferences, cultural backgrounds and ethnic food traditions are accounted for. The paper structures a priority framework, offering better insights into those diets that can be tailored for specific diet-related chronic conditions, such as obesity or type 2 diabetes.

"The legacy of research into dietary nutrition continues to refine what we know about our bodies and the capacity for a tailored diet, featuring key macronutrients to support our long-term health," said Dr. John Kirwan, Executive Director of Pennington Biomedical Research Center. "Dr. Heymsfield's recent paper in the New England Journal of Medicine is the latest contribution to this research history of contributing to the knowledge base, and further promotes the notion of 'food as medicine' -- delivering the potential to improve health across the lifespan with bespoke, nutrient-rich diets."

• Diet and Weight Loss
• Staying Healthy
• Agriculture and Food
• Food and Agriculture
• Healthy diet
• Epidemiology
• Chinese food therapy
• Food groups

Story Source:

Materials provided by Pennington Biomedical Research Center . Note: Content may be edited for style and length.

Journal Reference :

• Steven B. Heymsfield, Sue A. Shapses. Guidance on Energy and Macronutrients across the Life Span . New England Journal of Medicine , 2024; 390 (14): 1299 DOI: 10.1056/NEJMra2214275

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