research paper topics on decision

• Browse All Articles
• Newsletter Sign-Up

DecisionMaking →

No results found in working knowledge.

• Were any results found in one of the other content buckets on the left?
• Try removing some search filters.
• Use different search filters.

• Skip to main content
• Skip to primary sidebar

IResearchNet

Decision Making Research Topics

Decision making definition.

Decision making refers to the act of evaluating (i.e., forming opinions of) several alternatives and choosing the one most likely to achieve one or more goals. Common examples include deciding for whom to vote, what to eat or buy, and which college to attend. Decision making plays a key role in many professions, such as public policy, medicine, and management. The related concept of judgment refers to the use of information, often from a variety of sources, to form an evaluation or expectation. One might imagine that people’s judgment determines their choices, though it is not always the case. Read more about Decision Making .

Decision Making Research Topics:

• Behavioral Economics
• Fast and Frugal Heuristics
• Grim Necessities
• Hindsight Bias
• Hot Hand Effect
• Hyperbolic Discounting
• Illusion of Transparency
• Illusory Correlation
• Integrative Complexity
• Law of Small Numbers
• Loss Aversion
• Mental Accounting
• Mere Ownership Effect
• Naive Cynicism
• Naive Realism
• Omission Neglect
• Overconfidence
• Planning Fallacy
• Pluralistic Ignorance
• Preference Reversals
• Prisoner’s Dilemma
• Public Goods Dilemma
• Recency Effect
• Representativeness Heuristic
• Risk Taking
• Risky Shift
• Satisficing
• Simulation Heuristic
• Simultaneous Choice
• Social Dilemmas
• Spreading of Alternatives
• Visceral Factors

Decision Making Research in Applied Fields

Return to Social Psychology Topics list.

Advertisement

Decision-making: from neuroscience to neuroeconomics—an overview

• Published: 28 June 2021
• Volume 91 , pages 1–80, ( 2021 )

Cite this article

• Daniel Serra   ORCID: orcid.org/0000-0002-1907-4638 1  

4033 Accesses

8 Citations

4 Altmetric

Explore all metrics

By the late 1990s, several converging trends in economics, psychology, and neuroscience had set the stage for the birth of a new scientific field known as “neuroeconomics”. Without the availability of an extensive variety of experimental designs for dealing with individual and social decision-making provided by experimental economics and psychology, many neuroeconomics studies could not have been developed. At the same time, without the significant progress made in neuroscience for grasping and understanding brain functioning, neuroeconomics would have never seen the light of day. The paper is an overview of the main significant advances in the knowledge of brain functioning by neuroscience that have contributed to the emergence of neuroeconomics and its rise over the past two decades. These advances are grouped over three non-independent topics referred to as the “emo-rational” brain, “social” brain, and “computational” brain. For each topic, it emphasizes findings considered as critical to the birth and development of neuroeconomics while highlighting some of prominent questions about which knowledge should be improved by future research. In parallel, it shows that the boundaries between neuroeconomics and several recent sub-fields of cognitive neuroscience, such as affective, social, and more generally, decision neuroscience, are rather porous.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

The Neural Correlates of Decision-Making: Review and Research Agenda

Mind/Brain and Economic Behaviour: For a Naturalised Economics

• Neuroeconomics

It is commonly admitted today that the birth of neuroeconomics coincides with the publication by the neurobiologist Michael Platt and the neurophysiologist Paul Glimcher in Nature of a study on behavior of monkey linked to anticipated “rewards” (in this case, food rewards) (Platt & Glimcher 1999 ). For the first time, an electrophysiological experiment on a monkey proved that the brain “value” stimuli independently of sensory or motor processes. Thanks to cerebral imaging, this finding was extended to humans in the early 2000s (Berns et al., 2001 ; Breiter et al., 2001 ; Delgado et al., 2000 ; Elliot et al., 2000 ; Knutson et al., 2000 ; Knutson et al., 2001 ). For a first brief history of neuroeconomics, refer to Glimcher & Fehr ( 2014b ) and Serra ( 2022 ), chap. 3.

The most basic element of nervous system function is the “action potential” (or “spike”) that arises when a voltage of a neuron’s cell body rises above a particular threshold. Neurophysiologists use changes in firing rate of a neuron as an index of whether a stimulus changes the ongoing information processing with which that neuron is associated. Single-unit recording is a direct measurement of action potentials requiring the insertion of very fine electrodes into the neural tissue immediately adjacent to the neurons of interest. The invasive nature of this technique limits its use to non-human animals (except in the rare cases of human patients with clinically indicated electrodes).

EEG and MEG are non-invasive neurophysiologic techniques. Input to a neuron changes the electrical potential of its cell membrane. If many neurons evince similar changes in their membrane potential, the collective electrical current they generate can be detected by electrodes positioned on the scalp. EEG provides high-temporal-resolution access to the electrical activity of the brain. However, electrical currents, like those generated by dendritic activity of neurons, also give rise to magnetic fields that MEG is able to measure thanks to external sensors.

PET was the first functional imaging technique to gain wide-spread acceptance. It allows measuring brain metabolic activity thanks to emissions made by positrons coming from a radioactive isotope that is injected before or during scanning, depending on the isotope being used. The most salient disadvantage of PET is its invasiveness: safety guidelines restrict how that radioactive material can be created, handled, and administrated. This technique also has very limited temporal resolution.

Since its development in the early 1990s, fMRI has grown to become the dominant functional imaging technique in cognitive neuroscience. Its success comes from the intertwining of the image creation process from MRI with new insights into the metabolic changes associated with brain activity. It is based on magnetic properties of hemoglobin: neural activity in a particular zone induces a stronger demand for oxygenated hemoglobin, and then generates a higher BOLD (blood oxygenation-level-dependent) signal. This technique is a good combination of spatial and temporal resolution. Much of the growth of fMRI in research has been facilitated by the prevalence of high-field scanners for clinical applications. Structural MRI (morphometry), which is effective in discriminating between gray and white matter in the brain, and diffusion tension imaging (DTI), which measures the direction and magnitude of water diffusion in brain tissue, are also used in a few neuroeconomic experiments. Near-infrared spectography is another method recently introduced in experiments.

TMS stimulates neurons by means of electromagnetic induction. It uses a magnetic field which can pass easily through the skull, to generate an electrical current inside the brain. This electric current acts on the underlying neurons and triggers action potentials in axons that cross the field at appropriate orientations (e.g., perpendicular). This means that some locations in the cortex are easier to stimulate than others using this technique. The artificial and temporary lesion of the target zone allows identifying the behavioral effect. TMS is often applied repeatedly for changing induced neuronal excitability beyond the moment of stimulation (rTMS).

tDCS is a more recent non-invasive electrostimulation tool able to change cortical excitability thanks to electrodes that are wrapped in sponges soaked in saline solution and mounted to the head. It can be used in two modes: anodal tDCS to upregulate and cathodal tDCS to downregulate neural processing in a brain region. tDCS has an additional advantage: it helps to avoid a problem that may arise when using rTMS in social neuroeconomic experiments; e.g., to study “social preferences”. The issue is that each player must face a series of one-shot stranger-matching games sequentially with the behavioral study focusing on the participant playing second. This poses an implementation problem, because each participant will be faced with a high number of protagonists and there is a great temptation to deceive the participants and to confront them with prefabricated options. Yet, in experimental economics, it is well known that it is strongly recommended not to deceive participants to keep their trust in the experimentalist. As tDCS is inexpensive, it can be administered simultaneously too many interacting subjects. Deep brain stimulation, microstimulation, and optogenetic are invasive stimulation methods reserved for animal experiments or for patients with chronic and severe neurological disorders (Parkinson’s disease, epilepsy, and obsessive compulsive disorder).

At least 60 different neurotransmitters have been identified. Some of them increase the probability that the postsynaptic cell will transmit an action potential (“excitatory” neurotransmitters), while others decrease this probability (“inhibitory” neurotransmitters). The main excitatory neurotransmitter is glutamate and the main inhibitory one is GABA. Some neurotransmitters, known as neuromodulators, act mainly by modulating the activity of glutamate and GABA releasing neurons. Examples of neuromodulators include dopamine, serotonin, and noradrenaline/norepinephrine.

Charles Darwin was one of the first scholars to study emotions through facial expressions (Darwin 1872 ).

Using these relatively simple and inexpensive tools in neuroeconomic experiments rather than the complex and very expensive neuroimaging is actively encouraged par Axel Rubinstein, an economist rather skeptical about usefulness of neuroeconomics for economists without totaling rejecting this approach (Rubinstein 2008 ). Reuter & Montag ( 2016 , Part VII) give a scholarly introduction into the constellation of methods and techniques relevant to neuroeconomics.

In short, the argument is that if a phenomenon is already well known in psychological and behavioral terms, knowledge of neural correlates and mechanisms would be useless for economists (e.g., Harrison 2008a , 2008b ; Rubinstein 2008 ; Smith 2008 ). In addition to this issue of interest for economists and beyond the philosophical issue of the “mindless economics” argument (Gul & Pesendorfer 2008), controversial debates about neuroeconomics bear on reliability of findings, in relation to the non-trivial statistical analysis of fMRI data and particularly with the so-called reverse inference “fallacy”. The reverse inference problem, which questions the validity of the rationale underpinning neuroimaging methods—namely inferring thought processes from brain activity—is a practical issue also found in cognitive psychology experiments that rely on neuroimaging to infer particular cognitive functions (memory, attentiveness, language…). On this topic, see Poldrack (2006, 2011, 2018); Harrison ( 2008b ); Harrison & Ross ( 2010 ); Ross ( 2010 ); Bourgeois-Gironde ( 2010 ); Poldrack et al. ( 2017 ); Serra ( 2021 ). Remark that recent progress in the development of methods for decoding human neural activity as measured with fMRI should lead to bypassing the reverse inference problem. We know that fMRI studies focused on associating brain zones with mental functions. The introduction of decoding using the so-called “multivariate pattern analysis” (MVPA) has revolutionized fMRI research by changing the questions that are asked. Instead of asking what a zone’s function is, in terms of a single brain state associated with global activity, we can now ask what information is represented in a zone, in terms of brain states associated with distinct patterns of activity, and how that information is encoded and organized (see, e.g., Normann, Polyn, & Haxby 2006 ; Haxby, Connoly, & Guntupalli 2014; Efron & Hastie 2016 ).

In the same time, neuroeconomics results are viewed as useful in psychiatry for analyzing a constellation of mental and neurological disorders including frontotemporal dementia, obsessive–compulsive disorder, and drug addiction (see, e.g., Millan, 2013 ; Schutt et al., 2015 ; Conn 2016; Lis & Kirsch 2016 ; Dreher & Tremblay, 2017 ; Alos-Ferrer 2018 ).

However, there is a significant difference between neuroeconomic choice models and random utility models. While the latter posit that preferences are in essence stochastic and that choices always reflect these underlying preferences, neuroscience research suggests that the choice process itself might be systematically biased and sub-optimal (we shall return to this point in Sect.  5 ).

In this respect, as suggested by Huettel ( 2010 ), neuroeconomics may be viewed as a subfield of decision neuroscience which deals with both perceptual and VBD decisions. Yet, some scholars do not distinguish between neuroeconomics and decision neuroscience by opposing them to molecular neuroscience (e.g., Montague 2007 ).

Today, neuro-imagery studies use more frequently the Montreal Neurological Institute (MNI) space, which slightly differs from Talairach–Tournoux normalization by relying on a highly number of fMRI images (see, e.g., Poldrack et al., 2011 , 2017 ).

Notice that the different neural regions referred to in the text often include only a part of the BAs mentioned in bracket.

The anterior cortex (or frontopolar cortex) (BA 10) is the most rostral zone of the frontal lobe. It performs a function of cognitive control in the most complex situations; it is involved to monitor completely unknown situations or forcing the subject to think about one’s own thoughts (i.e., metacognition). The dorsolateral PFC (BA 8, 9, 46) corresponds to the superior part of the frontal lobe exterior. It is seen as the most “rational” part of the brain.

The cingulate cortex is an internal zone located along the interhemispheric fissure above the corpus callosum. It is divided into an anterior (ACC) (BA 24, 32, 25) and a posterior (PCC) (BA 23, 31) parts. The ACC has long been known to play a role in decision-making, especially when subjects made errors in simple decision-making tasks and detected those errors. It is traditionally known as mainly implicated in the monitoring of internal conflicts, namely when conflicting signals are sent by several neural areas and that selection of an action may be tricky. The rostral ACC is known as the paracingulate cortex. The PCC (BA 7, 40) is typically known as devoted to several high-level cognitive functions, including attention, working memory, and more broadly, “external consciousness”, but its ventral part seems to show a functional integration with the whole areas belonging to the cerebral “default mode” (i.e., the brain’s intrinsic activity when it is undertaking no task whatsoever); this network is supposed to accommodate what some authors called “internal subjective consciousness”. The TPJ (BA 22, 40) is a part of the temporal cortex at the edge of the parietal cortex. It is implicated both in reorienting of attention and social cognition.

All vertebrates (fish, amphibians, reptiles, birds, and mammals) possess such a neural structure, of one form or another. It consists of a set of functionally diversified nuclei embedded in cerebral hemispheres depth, behind the frontal lobes and encircling the thalamus, including the striatum. The striatum includes itself three structures connected to different neural regions: the caudate nucleus, the putamen, and the nucleus accumbens (NAcc). They receive extensive inputs from the frontal cortex and send almost all of their outputs to two other nuclei in the basal ganglia (the globus pallidus and the substantia nigra pars reticula). Today, many researchers simply divide the striatum into two sections: the ventral striatum (the NAcc and lower parts of the caudate and putamen), interacting with regions engaged mainly in emotion and motivation, and the dorsal striatum (the upper parts of the caudate and putamen), interacting with regions implicated in movement and memory.

The amygdala corresponds to a group of nuclei in the medial temporal lobe in front of the hippocampus. This structure plays a central place in emotion and motivational processing, and is implied both in the emotional component of sensorial stimuli and emotional stimuli memorization. The hippocampus, with near structures with whom it is closely connected, is related to memory in general and spatial memory and is crucial for complex spatial representations; it is part of a “human navigation network”.

In the wide orbitomedial region of the PFC (the region encompassing all internal and orbital neural areas), several specific zones are identified, but not all researchers agree on their boundaries. By moving up from the zone located just above the orbits to the top of the skull, are typically defined the orbitofrontal cortex (OFC) (whose medial/caudal/lateral parts are differentiated) (BA 11, 14 / 13 / 47/12), ventromedial PFC (BA 10, 11, 14, 32), and dorsomedial PFC (BA 9, 8, 32) (sometimes named globally medial PFC). The ventromedial PFC very often is defined as including the medial OFC.

The insula (or insular cortex) is a part of the cortex moved in depth of the lateral sulcus, at the junction between the frontal and temporal lobes. The insula is sometimes called the “paralimbic structure”. Its anterior part is strongly involved in emotion expressing: it is acting as a monitoring system that informs the brain about high-risk or unpleasant situations that may be a source of danger, harm, or pain. Some authors call this structure the “interoceptive” cortex, because it is implicated in the processing of internal representations signals of body states.

Psychologists distinguish another notion, “mood”, considered as an affective state more diffuse, less intense but more durable than emotion. The term “affect” often is used as a generic term that involves both emotion and mood (e.g., Scherer 2005 ).

The locus coerulus, located in the cerebral pons, is in close contact with the amygdala. It is associated with noradrenaline/norepinephrine, a chemical substance related to adrenaline considered as neurotransmitter; it is seen as active in waking, sleeping, and feeding behavior, but it also interplays with cortical regions for modulating attention.

However, several meta-analyses showed that often there are differences in response intensity of a same structure depending on the emotion: e.g., both fear and happiness active the amygdala, but the activation level is significantly stronger with fear than with happiness, or both disgust and anger actives insula, but the activation level is significantly stronger with disgust than with anger. Hemispheric lateral effects also were observed, e.g., the right amygdala is more involved in negative emotions and the left in positive.

Consider Plato’s famous metaphor where the mind is seen as a chariot pulled by two horses. The rational brain is the charioteer who guides the horses. One of the horses is well bred and well behaved, while even the best charioteer has difficulty controlling the other horse; this obstinate horse represents negative, destructive emotions. The charioteer’s task is to keep both horses moving forward. Through that simple metaphor, the mind was seen as conflicted, torn between reason and emotion. This dual division of the mind is one of the most enshrined ideas in Western culture. A large set of influential philosophers, from René Descartes to Sigmund Freud, and including Francis Bacon, Auguste Comte, and Emmanuel Kant, all embraced various forms of this duality, which continues through to the modern brain–computer metaphor proposed by cognitive psychology that sees emotions as antagonists of rationality. Aristotle in The Nicomachean Ethics is seen as an exception by claiming that rationality is not always in conflict with emotion. Another widely known exception is Spinoza, a contemporary of Descartes, Antonio Damasio highlights this opposition between Descartes and Spinoza in the titles of two of his books. Descartes’ Error: Emotion, Reason, and the Human Brain (Damasio 1994 ) and Looking for Spinoza: Joy, Sorrow, and the Feeling Brain (Damasio 2003 ).

We know that in economics, the experience of regret in decision-making was initially introduced by Bell ( 1982 ) and Loomes & Sugden (1982). In this theory, we suppose that, for each decision, the agent is taking account her/his utility and the potential degree of regret/satisfaction, i.e., the comparison with what she/he could have obtained.

It was back in 1994 that Damasio depicts for the first time the now famous history of this young American railway worker named Phineas Gage who, in 1948, was suffering a serious injury in the brain (a crowbar of 6 kg was going through his brain), an accident whose consequences, against all odds, were not physical but behavioral (for further detail see Macmillan 2000 ). Interested in pathological consequences of patients with frontal lobe lesions, Damasio had the opportunity to observe subjects like Gage: Elliot history, a patient suffering from a benign brain tumor, is now as famous as Gage history (Damasio 1994 ).

Of course, this is not to say that emotions are only beneficial effects for subjects. Damasio himself acknowledges that the participation of emotion to reasoning process may be advantageous or detrimental according to both the decision circumstances and the decision-maker’s past history. There is compelling evidence that the perception of emotionally salient stimuli and the experience of emotional states can profoundly alter cognition and promote specific harmful behavioral tendencies (see, e.g., Okon-Singer et al., 2015 ; Engelman & Hare 2018).

Over the years, several studies have questioned the somatic marker hypothesis (e.g., Dunn et al., 2006 ). Nevertheless, this hypothesis has played a central role in affective neuroscience in that it was one of the first which links emotional responses and brain systems to behavioral decision patterns.

A lot of neuroscientific studies show that the emotion of regret also is implicated in several clinical disorders such as schizophrenia, depression, obsessive–compulsive disorder, and “chasing” behavior in pathological gambling.

This example indirectly refers to understanding consumer behavior in terms of “mental accounting” as proposed in behavioral economics (Thaler 1985 , 1999 ). This very general mental process is analyzed by distinguishing two often simultaneous phases: a “framing” phase, which is concerned with the external description of events that is given to an agent, and an “editing” phase, which is concerned with the internal process whereby the agent analyses the information. These neuroeconomic experiments focus on the editing phase.

Furthermore, the work of the American financial journalist Jason Zweig (Zweig 2007 ) aimed at the general public uses a broad range of examples from the history of finance to show the potential of neuroeconomics to elucidate and guide financial choices.

See Frederick, Loewenstein & O’Donoghue ( 2002 ) and Camerer & Loewenstein (2004) who distinguish this “choice tasks” method from other popular experimental methods such as the “matching tasks” method.

The experiment was repeated with food rewards in McClure et al. ( 2007 ) with the consumption of a fruit juice being either immediate or delayed (offset by 10 min or several minutes more). Unlike financial rewards, the emotional mechanism was activated only in the immediate consumption option, suggesting that time scales are perceived differently by the brain according to the nature of the reward.

The Laibson model (Laibson 1997 ) that uses quasi-hyperbolic discounting is however criticized, because it is incompatible with the notion of self-control. Thus, Ainslie ( 2012 ) prefers the original hyperbolic approach (Ainslie 1975 , 1991 ), but introduces a recursive process of self-prediction by the subjects themselves at the different expected timeframes, which may imply stronger commitment from the subjects towards themselves or, on the contrary, a progressive disengagement.

In the beginning, much research in social neuroscience has been driven by mental illnesses, because many of them often involve a breakdown of the “social” brain (in particular, schizophrenia). Remember that, likewise, the study of brain lesions has been a starting point for much of the early progress in neuroscience. Yet, in the last 15 years, research in social neuroscience has increasingly focused on the social behavior of mentally healthy decision-makers, encompassing many social phenomena as social interactions.

It was recognized that ability to mentalize is severely delayed in autism. That could explain observed failure in communication and social interaction by most autistic children. Today, the autistic brain is at the heart of social neuroscience, because it helps to clarify the missing links between brain and social behavior (Frith 2001 ). Temple Grandin (an American professor in animal science) was one of the first high-functioning autistic woman (people with Asperger syndrome) whose brain was scanned by fMRI toward the end of the 1980s. Like Gage and Elliot cases, mentioned by Damasio ( 1994 ), Grandin case is become paradigmatic in cognitive neuroscience (Sacks 1995 ).

The ability to mentalize is absent in monkeys, but is not an exclusively human trait. It is likely to be present, in varying degrees, in all species of apes (Call & Tomasello 2008 ; Krupenye et al., 2016 ).

For a systematic confrontation between theory of mind and game theory, see Schmidt & Livet ( 2014 ). It would also be interesting to parallel the mentalizing approach with the various informational requirements posit by normative economic in which ethical principles are conditioned by the existence of either inter personal comparisons of utility (i.e., ability to put yourself in others’ shoes, with their preferences)—e.g., utilitarianism, welfarist social choice—or only intra personal comparisons of utility (ability to put yourself in others’ place, with our own preferences)—e.g., theories of equity and fairness, non-welfarist social choice (on this literature on theory of utility and ethics, see, e.g., Roemer 1996 ; Mongin & d’Aspremont 1998 ).

Some authors introduce additional distinctions. For example, Blomm (2017) adds to cognitive and affective empathy two other senses of empathy: “emotional contagion”, understood as sharing the feelings of those in your immediate vicinity while for affective empathy others does not have to be present or even exist, and “compassion”, “kindness”, or “sympathy”, that would replace affective empathy as a moral motivation. When one empathizes with another person, there does not have to be a prosocial motivation attached to it; when one sympathizes or shows compassion for another person, there is. However, in general, empathy is viewed as a first necessary step in the process that begins with affect sharing, which motivates other-related concern and finally engagement in helping behavior. Empathy and prosocial behavior are closely linked (Singer & Tusche, 2014).

Although the unique features of human social cognition are often emphasized, there is now evidence that they may depend on more basic social cognitive processes present in other primates and sometimes even in other mammals, including monitoring the actions of others, assigning importance to others, and orienting behavior toward or away from others (for a survey, see Rushworth, Mars, & Sallet 2013 ).

Two participants are randomly and anonymously matched, one as investor (player I) and one as trustee (player T), and play a one-shot game. Both participants are endowed with an amount of money. Player I can send some, all or none of her endowment to player T. Every amount sent by player I is tripled. Player T observed the tripled amount send, and can send some, all or none of the tripled amount back to player I. The amount send by the investor is view as a measure of trust; the amount returned by the trustee is view as a measure of trustworthiness.

As is well known, Prisoner’s Dilemma (PD) games are used to study “social dilemmas” that arise when the welfare of a group conflicts with the narrow self-interest of each individual group member. In a typical two-player PD, each player can choose either to cooperate or defect . Payoffs are symmetric, and chosen, so that the sum of the payoffs is greatest when both choose to defect. However, each player earns the most if she chooses to defect when the other cooperate.

In the simplest variant of the game, each player simultaneously chooses a number P between 0 and 100. The person whose number is closest to 2/3 times the average of all chosen numbers wins a fixed amount of money; others receive noting; ties are broken randomly.

This game, originally discussed as “guessing game” by Moulin ( 1986 ), is an ideal tool for assessing where the chain of iterated dominance reasoning breaks down in a strategic-form game. It was studied experimentally by Nagel ( 1995 ). This game is also called a “beauty contest” (Camerer 1997 ), because it captures the importance of iterated reasoning that John Maynard Keynes ( 1936 ) described in his famous analogy for stock market investment. Keynes speaks about a newspaper contest in which people guess what faces others will guess are most beautiful, and compares that contest with the stock market investment. Like people selecting the prettiest picture, each subject in the beauty contest game must guess what average number other subjects will prefer, then pick the fraction P of that average, knowing that everybody is doing the same as her/him. The P-beauty contest game is a workhorse example for the cognitive hierarchy approach in strategic thinking, such that developed by several models of bounded rationality in behavioral game theory, including rationalizability , level-K, or cognitive hierarchy models (Camerer, Ho, & Chong 2004a , 2004b ). In these models, players use various levels of strategic thinking, and high-level thinkers distinguish themselves by correctly anticipating what players using fewer levels of thinking will do. It seems that limits of strategic thinking arise in particular from limits on working memory. For an overview of these models, see Cartwright ( 2016 ); Serra ( 2017 ).

Other games with very different logical structures are also concerned by this specificity of subjects’ behavior when they know (or believe to know) that they are interacting with humans and not with computers. For instance, in one of the first PET experiments, Gallagher et al. ( 2002 ) showed that in the well-known rock-paper-scissors game , the paracingulate cortex (rostral ACC) was strongly more activated when subjects thought they were playing against another human player rather than against a computer (in reality, they always were faced with random choices). For a review of neuroeconomic works dealing with strategic thinking, see Camerer & Hare (2014).

The structure of public good (PG) games is similar to that of prisoner’s dilemma (PD) games, but they are typically played in larger groups. In a typical PG game, each member of a group of four people is allocated an amount of money, say 10 dollars. Group members simultaneously decide how to allocate their endowment between two “accounts”, one private and one public. The private account returns one dollar to the subject for each dollar allocated to that account. In contrast, every dollar invested in the public account doubles, but is then split equally among the four group members (0.50 dollar each). Thus, like the PD game, group earnings are maximized at 80 dollars if everybody cooperates and contributes everything to the public account, in which case each of the four participants will earn 20 dollars. However, if three subjects contribute 10 dollars each, and the fourth free-rides and contribute nothing, then the free-rider will earn 25 dollars. Like the PD game, each group member has the private incentive to contribute nothing (free-riding). In on another side, we know that the funding of public goods is a balancing act, both voluntary and involuntary mechanisms. In general, modern societies rely much more on taxation than on voluntary giving to provide public goods. However, for specific goods (e.g., the arts or some kinds of medical research), voluntary giving can be quite important. The goal of charitable donations games is to experimentally study altruistic giving in a PG framework.

PG games with punishment are sequential PG games where players have the option to punish non-contributors and to reward the highest contributors after a round of the game.

Two participants are randomly and anonymously matched, one as proposer (player P) and one as r esponder (player R), and told that they will play a one-shot game. Player P is endowed with an amount of money, and suggests a division of that amount between herself and player R. Player R observes the suggestion and then decides whether to accept or reject. If the division is accepted, then both earn the amount implied by the player P’s suggestion. If rejected, then both players earn nothing for the experiment. It is a simple take-it-or-leave-it bargaining environment. Remark that in ultimatum games, the act of rejection of the Proposer’s offer by the Responder represents an act of costly punishment, because both players suffer a cost.

Several forms of social punishment are identified, including second-party or third-party punishment. “Parochial “altruism, namely a preference for altruistic behavior towards in-group members and mistrust or even hostility towards out-group members (e.g., one’s ethnic, racial, or any other social group), is a pervasive feature in human society. Parochial altruism involves a third-party punishment behavior. Recent evidence from fMRI studies suggested that areas involved in social cognition (including dorsomedial PFC and bilateral TPJ) must play a role in differentiating in-group and out-group members in behavior (Baumgartner et al., 2012 ), while Baumgartner et al. ( 2014 ) showed that the transient disruption of the right (but not the left) TPJ reduces parochial punishment with real social group.

For a brief presentation of these tools, refer to paragraph 2.1.1.

These studies complete the rare experiments that study in a game-theoretic framework the social behavior of patients with prefrontal damage. Krajbich et al. ( 2009 ), in particular, found that patients with damage to the ventromedial PFC show a specific insensibility to guilt.

We know that reputation was broadly studied in repeated game theory with private information. Several fMRI experiments directly or indirectly tap into aspects of reputation (e.g., Delgado, Franck & Phelps, 2005 ; Singer et al., 2004 ).

However, it turns out that oxytocin inhalation does not affect the loyalty of the trustees. To explain this asymmetry between investors and trustees, the authors highlight the difference between “pure” trust found in investors (that can only be generated by a certain empathy) and the “calculated” trust of trustees (as it is a function of their experience during the game).

A more complete panorama of this neuropharmacology literature, that also includes the effects of chemical substances on time and risk preference, can be found in Crockett & Fehr ( 2014 ).

It should be noted that social neuroscience literature covers a much broader thematic domain than questions of social cognition. A lot of studies concern in particular what is called “moral dilemmas”, which differ from “social dilemmas” by the fact that all solutions of a given problem generate a not morally desirable outcome (e.g., the famous “trolley problem”) (Christensen & Gomila 2012 ).

These experiments revealing the role of dopamine in reward system were carried out in non-human primates. However, a similar mechanism was shown to also exist in honeybees, which employ a close chemical homologue of dopamine called octopamine (Real 1991; Montague et al., 1995 ). As Glimcher points out, “the fact that the same basic system occurs in species separated by something like 500 million years of evolution suggests how strongly evolution has conserved this mechanism” (Glimcher 2011a , p. 302).

Attention allows for the voluntary processing of relevant over irrelevant inputs in line with the current behavioral goal of the organism. Working memory can be conceived as an active process whereby stimulus or internal representations are stored “on-line” to prevent temporal decay or intrusion from competing or distracting stimuli that are outside the current focus of attention. Therefore, dissociating effects of attention from those of working memory is difficult, and in practice, the two processes are interactive (Awh & Jonides 2001 ). The dopaminergic system is a primary pharmacological target for psychiatric disorders which are associated with attention deficits such as attention deficit, hyperactivity disorder, schizophrenia, and Parkinson’s disease (e.g., Arnsten & Rubia 2012 ). Note that dopamine is not the only neuromodulator implicated in attention; acetylcholine, noradrenaline, and serotonin also play a role in top–down attentional control (for a recent review, see Thiele & Bellgrove 2018 ).

Rolls ( 2014 ), particularly, agrees that there is evidence for DNs action in encoding of RPE signals and that this could present a problem; according to Rolls, the alternative hypothesis that DNs reflect the effects of many stimuli salience (i.e., a property less dependent to reward) is more consistent with experimental data. This is also explicit in the survey written by Berridge & O’Doherty ( 2014 ), in which each co-author has a slightly different point of view: for O’Doherty, dopamine is a prediction-error mechanism of reward learning, while for Berridge, dopamine mediates incentive salience. Indeed, there has been considerable debate over the role of dopamine activity in processing non-rewarding events (i.e., signals related to salient, surprising, and novel events). A lot of studies provide evidence that DNs are more diverse than previously thought. Rather than encoding a single homogeneous motivational signal, they come in multiple types that encode both reward and non-reward events in different manners. Thus, these results pose a problem for general theories that identify dopamine with a single neural signal or motivational mechanism.

Broadly, serotonin is implicated in a variety of motor, cognitive, and affective functions, such as locomotion, sleep–wake cycles, and mood disorders. It was argued that this neurotransmitter would play a role in impulsive behaviors: reduced levels of serotonin would promote impulsive actions (i.e., the failure to suppress inappropriate actions) and choices (i.e., the choice of small immediate rewards over larger delayed rewards) (Dalley et al., 2011 ).

The fact that the subjective impact of a loss is greater than that of an equivalent gain is one of the general principles underlying the famous prospect theory. This theory has been tested in recent years by numerous neuroeconomic experiments that have corroborated its main hypotheses such as loss aversion and the non-linearity of the probability-weighting function, but reference-dependence in decision-making and framing effects remain unclear (refer to Fox & Poldrack 2014 ; Louie & De Martino 2014 ). Glimcher ( 2011a , 2011b ) established a parallel between the idea of reference point introduced by Kahneman and Tversky and a similar concept in neurobiology. It is interesting to note that Kahneman himself was involved in one of the first experiments in neuroeconomics (Breiter et al., 2001 ). However, the status of the neural data in this experiment is ambiguous. As with all pioneering experiments in the early 2000s, it is claimed that the experiment is set within reward learning theory, yet it is clear that the prospect theory also plays the role of experimental paradigm. Neural data are alternately considered as parameters of the Kahneman–Tversky model ( exogenous variables that must be estimated to “calibrate” the model) or explanatory variables ( endogenous variables that are progressively corrected by the neural-learning process). This experiment shows clearly the difficulty that must be faced when transposing the “anomalies”, namely the disparities between “ideal” economic rational and observed behavior, into the theoretical framework of reward learning. In neurobiology, irrational behavior is appraised against learning dynamics (Fox & Poldrack 2014 ).

Today, the “common currency” hypothesis is widely accepted in the neuroscientific community. Yet, there are some rare researchers who do not fully agree with it. They argue that different specific rewards must be represented “on the same scale” but not necessarily converted into a “common currency”. The key difference between the two concepts of common scaling and common currency lies in the specificity with which rewards are represented at the level of single neurons. While a common currency view implies convergence of different types of reward onto the same neurons, a common scaling view implies that different rewards are represented by different neurons with the activity of the different neurons scaled to be in the same value range. Due to the limited resolution of the tool, fMRI studies cannot answer whether the same or different neurons are encoding the value of different rewards; only single neuron recording studies may provide such evidence (Grabenhorst & Rolls 2011 ; Rolls 2014 ).

Of course, this evolutionary advantage may become a disadvantage in some environments where the structure emphasizes likely utilities and rewards in the very short term. However, the flaw lies in the environment and not in the individual (Ainslie 1992 ).

For example, where reward is concerned, eat any food within reach in a buffet regardless of how hungry you are; where punishment is concerned, cross the road at the sight of a suspicious-looking individual to avoid a possible attack.

Pavlovian learning is known to be present in vertebrates, including humans, as well as many invertebrates, including insects such as drosophila.

For example, where reward is concerned, drink a cup of coffee every morning as a stimulant regardless of the specific need felt on that particular day; where punishment is concerned, select the same route every day to drive to work regardless of any foreseeable traffic jam on that particular day.

For example, where reward is concerned, select the film at the cinema according to your taste to make it the most pleasurable experience possible; where punishment is concerned, decide to jog regularly to minimize the risk of obesity.

Lengyel & Dayan ( 2007 ) advance the hypothesis of a fourth “episodic” system managed by the hippocampus. More recently, O’Doherty et al ( 2017 ) review evidence that an additional system would guide inference concerning the hidden states of other agents, such as their beliefs, preference, and intentions, in a social context.

For Pavlovian systems, Dayan et al. ( 2006 ) have proposed some hypothesis. More recently, Clark et al. ( 2012 ) review first evidence of the existence of multiple parallel Pavlovian valuation systems. Interaction between habitual and goal-directed systems, and particularly the situation when habits come to dominate behavior, has become a topic of great interest in neuropsychology of addiction and others psychiatric disorders involving compulsive behaviors, such as obsessive compulsive disorder (Daw & O’Doherty 2014 ).

Glimcher’s model is more widely dealing with VBD (i.e., it is supposed to also include habitual decisions), but the switch among the two neural systems is not explicitly mentioned.

Other distinctions are developed in the neuroeconomic literature. Bossaerts, Preuschoff & Hsu, ( 2009 ), in particular, mention “true” preferences (what individuals want) and “revealed” preferences (what individuals do), while Berridge & O’Doherty ( 2014 ) separate what is “wanting” and “liking” for an outcome: “it is possible to want what is not expected to be liked, not remembered to be liked, as well as what is not actually liked when obtained” (Berridge & O’Doherty 2014 , p. 242).

For instance, the PCC is more active in response to a reward of 100 cents than 1 dollar, while the ventromedial PFC and striatal responses to these rewards are indistinguishable.

The costs’ nature issue in encoding of decision is addressed somewhat differently by Grabenhorst & Rolls ( 2011 ) (see also Rolls 2014 ). These authors draw a distinction between “extrinsic” costs (such as action costs, time delay, and risk in getting reward) and “intrinsic” costs (such as motivation state, impulsiveness, risk, and ambiguity attitude of the subject).

The most obvious evidence provides from a decision system with which neurophysiologists are familiar, the monkey visio-saccadic system, which for widely technic reasons was above all studied since the 1980s for understanding the sensorimotor control in general. The core of this frontoparietal network, that is playing a critical role for oculomotor tasks, involves areas known as the lateral intraparietal area (LIP) (in the intraparietal sulcus), the frontal eye field (FEF) (in the PFC), and the superior colliculus (in the midbrain). These findings were generalized later to body movements; it has been shown that the primary motor cortex, some anterior areas of the parietal cortex, and supplementary motor area, are playing an equivalent role.

It is only fair to recognize that the declared ambition of the researchers in the “behavioral economics in the scanner” program was quickly limited to “simply improving the understanding of the decision-making process” (see in particular the review by Sanfey et al., 2006 , only a year after the survey of Camerer, Loewenstein, & Prelec 2005 ).

Adolphs, R. (2017). How should neuroscience study emotions? By distinguishing emotion states, concepts, and experiences. Social Cognitive and Affective Neuroscience, 12 , 24–31.

Article   Google Scholar  

Adolphs, R., & Anderson, D. (2018). The neuroscience of emotion: A new synthesis . Princeton University Press.

Book   Google Scholar  

Ainslie, G. W. (1975). Specious reward: A behavioral theory of impulsiveness and impulsive control. Psychological Bulletin, 82 (4), 463–496.

Ainslie, G. W. (1991). Derivation of ‘rational’ economic behavior from hyperbolic discount curves. American Economic Review, 81 (2), 334–340.

Google Scholar  

Ainslie, G. W. (1992). Picoeconomics: The strategic interaction of successive motivational states within the person . Cambridge University Press.

Ainslie, G. W. (2012). Pure hyperbolic discount curves predict ‘eyes open’ self-control. Theory and Decision, 73 , 3–34.

Alos-Ferrer, C. (2018). A review essay on Social neuroscience : Can research on the social brain and economics inform each other? Journal of Economic Literature, 56 (1), 234–264.

Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: The medial frontal cortex and social cognition. Nature Review Neuroscience, 7 (4), 268–277.

Arnsten, A. F., & Rubia, K. (2012). Neurobiological circuits regulating attention, cognitive control, motivation, and emotion: Disruptions in neurodevelopmental psychiatric disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 51 , 356–367.

Arnsten, A. F., Wang, M. J., & Paspalas, C. D. (2012). Neuromulation of thought: Flexibilities and vulnerabilities in prefrontal cortical network synapses. Neuron, 76 , 223–239.

Awh, E., & Jonides, J. (2001). Overlapping mechanisms of attention and spatial working memory. Trends in Cognitive Sciences, 5 , 119–126.

Balleine, B.W, Daw, N.D., & O’Doherty, J.P. (2009). Multiple forms of value learning and the function of dopamine. In P.W. Glimcher, C.F., Camerer, E. Fe hr, & R.A. Polack (Eds.), Macroeconomic. Decision making and the brain (pp. 367–388). Amsterdam: Elsevier.

Balleine, B. W., & O’Doherty, J. P. (2010). Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology, 35 , 48–69.

Barrett, L.F. (2017). How emotions are made: The secret life of the brain . Hougton-Mifflin-Harcourt.

Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. NeuroImage, 76 , 412–427.

Basten, U., Bille, G., Heekeren, H. R., & Fiebach, C. (2010). How the brain integrates costs and benefits during decision making. Proceedings of the National Academy of Science USA, 107 , 21767–21772.

Baumgartner, T., Gotte, L., Gugler, R., & Fehr, E. (2012). The mentalizing network orchestrates the impact of parochial altruism on social norm enforcement. Human Brain Mapping, 33 (6), 1452–1469.

Baumgartner, T., Heinrichs, M., Vonlanthen, A., Fischbacher, U., & Fehr, E. (2008). Oxytocin shapes the neural circuitry of trust and trust adaptation in humans. Neuron, 58 , 639–650.

Baumgartner, T., Knoch, T., Hotz, D., Eisenegger, P., & Fehr, C. (2011). Dorsolateral and ventromedial prefrontal cortex orchestrate normative choice. Nature Neuroscience, 14 (11), 1468–1474.

Baumgartner, T., Schiller, B., Rieskamp, J., Gianotti, L. R. R., & Knoch, D. (2014). Diminishing parochialism in intergroup conflict by disrupting the right temporo-parietal junction. Social Cognitive and Affective Neuroscience, 9 , 653–660.

Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Decision, 52 , 336–372.

Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50 , 7–15.

Bechara, A., Damasio, H., Damasio, A. R., & Lee, G. P. (1999). Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. Journal of Neuroscience, 19 , 5473–5481.

Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275 , 1293–1295.

Bechara, A., Tranel, D., Damasio, H., & Damasio, A. R. (1996). Failure to respond automatically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6 , 215–225.

Bell, D. (1982). Regret in decision making under uncertainty. Operations Research, 30 , 961–981.

Belluci, G., Camilleri, J. A., Iyengar, V., & Gruger, F. (2020). The emerging neuroscience of social punishment: Meta-analytic evidence. Neuroscience & Behavioral Reviews, 113 , 426–439.

Benhabib, J., & Bisin, A. (2005). Modeling internal commitment mechanisms and self-control: A neuroeconomic approach to consumption-saving decisions. Games and Economic Behavior, 52 (2), 460–492.

Bernheim, B. D., & Rangel, A. (2005). From neuroscience to public policy: A new economic view of addiction. Swedish Economic Policy Review , 12 , 11–46.

Berns, G. S., McClure, S. M., Pagnoni, G., & Montague, P. R. (2001). Predictability modulates human brain responses to reward. Journal of Neuroscience, 21 , 2793–2789.

Berridge, K.C. & O’Doherty, J.P. (2014). From experienced utility to decision utility. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 335–351). Amsterdam: Elsevier.

Bjorklund, A., & Dunnett, S. B. (2007). Dopamine neuron stems in the brain: An update. Trend Neuroscience, 30 , 194–202.

Blair, R. J. R. (2005). Responding to the emotions of others: Dissociating forms of empathy through the study of typical and psychiatric populations. Consciousness and Cognitions, 14 (4), 698–718.

Blair, R. J. R. (2008). Fine cuts of empathy and the amygdala: Dissociable deficits in psychopathy and autism. Quarterly Journal of Experimental Psychology, 61 (1), 157–170.

Bloom, P. (2017). Empathy and its discontents. Trends in Cognitive Sciences, 21 (1), 24–31.

Bossaerts; P., Preuschoff, K., & Hsu, M. (2009). The neurobiological foundations of valuation in human decision making under uncertainty. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 353–366). Amsterdam: Elsevier.

Bourgeois-Gironde, S. (2010). Is neuroeconomics doomed by the reverse inference fallacy? Mind & Society, 9 (2), 229–249.

Bouton, M. E. (2007). Learning and behaviour: A contemporary synthesis . Sinauer Associates Inc.

Bowles, S., & Gintis, H. (2011). A cooperative species: Human reciprocity and its evolution . Princeton University Press.

Breiter, H. C., Aharon, I., Kahneman, D., Dale, A., & Shizgal, P. (2001). Functional imaging of neural responses to expectancy and experience of monetary gains and losses. Neuron, 30 , 619–639.

Brodmann, K. (1909). Vergleichhende lokalisationslehre der grosshimrinde . Leipzig: Verlag von Johann Ambrosius Barth / English translation: Garey, L.J. (1994). Brodmann’s “Localisation in the cerebral cortex” . London: Smith-Gordon.

Brown, (1951). Iterative solution of games by fictitious play. In T.C. Koopmans (ed.), Activity analysis of production and allocation . New York: John Wiley

Bush, R., & Mosteller, R. (1955). Stochastic models of learning . John Wiley.

Bzdok, D., Schibach, L., Vognely, K., Schneider, K., Laird, A. R., Langner, R., & Eickhoff, S. B. (2012). Parsing the neural correlates of moral cognition: ALE meta-analysis on morality, theory of mind, and empathy. Brain Structure and Function, 217 , 783–796.

Cacioppo, J. T., Visser, P. S., & Pickett, C. L. (Eds.). (2006). Social neuroscience: People thinking about thinking people . MIT Press.

Call, J., & Tomasello, M. (2008). Does the chimpanzee have a theory of mind? 30 years later. Trends in Cognitive Sciences, 12 (5), 187–192.

Camerer, C. F. (1997). Progress in behavioral game theory. Journal of Economic Perspectives, 11 , 167–188.

Camerer, C. F. (2007). Neuroeconomics: Using neuroscience to make economic predictions. Economic Journal, 117 , C26–C42.

Camerer, C.F. (2008a). The case of mindful economics. In A. Caplin & A. Schotter (Eds.). Foundation of positive and normative economics : A handbook , (pp. 43–69), New York: Oxford University Press, 2008.

Camerer, C. F. (2008b). The potential of neuroeconomics. Economics and Philosophy, 24 , 369–379.

Camerer, C. F. (2013). Goals, methods, and progress in neuroeconomics. Annual Review of Economics, 5 (1), 425–455.

Camerer, C.F. & Hare. T.A. (2014). The neural basis of strategic choice. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 479–511). Amsterdam: Elsevier.

Camerer, C. F., & Ho, T.-H. (1999). Experienced-weight attraction learning in normal form games. Econometrica, 67 , 827–874.

Camerer, C. F., & Ho, T.-H. (2004a). A cognitive hierarchy model of games. The Quarterly Journal of Economics, 119 (3), 961–898.

Camerer, C. F., Loewenstein, G., & Prelec, D. (2004). Neuroeconomics: Why economics needs brains. Scandinavian Journal of Economics, 106 (3), 555–579.

Camerer, C. F., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics”. Journal of Economic Literature, 43 , 9–64.

Camerer, C. F., & Weber, M. (1992). Recent developments in modeling preferences: Uncertainty and ambiguity. Journal of Risk and Uncertainty, 5 (4), 325–370.

Camille, N., Coricelli, G., Sallet, J., Pradat-Diehl, P., Duhamel, J.-R., & Sirigu, A. (2004). The involvement of the orbitofrontal cortex in the experience of regret. Science, 304 , 1167–1170.

Caplin, A., & Dean, M. (2008). Dopamine, reward prediction error, and economics. Quarterly Journal of Economics, 123 (2), 663–701.

Caplin, A. & Dean, M. (2009). Axiomatic neuroeconomics. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 21–32). Amsterdam: Elsevier.

Caplin, A., Dean, M., Glimcher, P. W., & Rutledge, R. (2010). Testing the reward prediction error hypothesis with an axiomatic model. Journal of Neuroscience, 30 , 13525–13536.

Carr, L., Iacoboni, M., Dubeau, M.-C., et al. (2003). Neural mechanisms of empathy in humans: A relay from neural systems for imitation to limbic areas. Proceedings of the National Academia of Science USA, 100 , 5497–5502.

Carter, R. M., Bowling, D. L., Reech, C., & Huettel, S. A. (2012). A distinct role of the temporo-parietal junction in predicting socially guided decisions. Science, 337 , 109–111.

Cartwright, E. (2016). Behavioral economics (3rd ed.). Routledge.

Chase, H. W., Kumar, P., Eickhoff, S. R., & Dombrovski, A. Y. (2015). Reinforcement learning model and their neural correlates: An activation likelihood estimation meta-analysis. Cognitive, Affective, and Behavioral Science, 15 , 435–459.

Chen, M. K., Lakshminarayanan, V., & Santos, L. R. (2006). How basic are behavioral biases? Evidence from capuchin monkey trading behavior. Journal of Political Economy, 111 (3), 517–537.

Cheung, Y. W., & Friedman, D. (1997). Individual learning in normal form games: Some laboratory results. Games and Economic Behavior, 19 , 46–76.

Chib, V. S., Rangel, A., Shimojo, S., & O’Doherty, J. P. (2009). Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex. Journal of Neuroscience, 29 , 12315–12320.

Christensen, J. F., & Gomila, A. (2012). Moral dilemma in cognitive neuroscience of moral decision-making: A principle review. Neuroscience and Biobehavioral Reviews, 36 (4), 1249–1264.

Chua, H. F., Gonzalez, R., Taylor, S. F., Welsh, R. C., & Liberzon, I. (2009). Decision-related loss: Regret and disappointment. NeuroImage, 47 , 2031–2040.

Clark, J. J., Hollon, N. G., & Phillips, P. E. M. (2012). Pavlovian valuation systems in learning and decision making. Current Opinion in Neurobiology, 22 , 1054–1061.

Clark-Polner, E., Johnson, T. D., & Barrett, L. F. (2017). Multi-voxel pattern analysis does not provide evidence to support the existence of basic emotions. Cerebral Cortex, 27 , 1844–1948.

Clithero, J. A., & Rangel, A. (2014). Informatic parcellation of the network involved in the computation of subjective value. Social Cognitive and Affective Neuroscience, 9 (9), 1289–1302.

Clithero, J. A., Tankersley, D., & Huettel, S. A. (2008). Foundation of neuroeconomics: From philosophy to practice. PLOS Biology, 6 (11), e298.

Cochi, L., Zalesky, A., Fornito, A., & Mattingley, J. B. (2013). Dynamic cooperation and competition between brain systems during cognitive control. Trends in Cognitive Sciences, 17 (10), 493–501.

Cools, R. (2011). Dopaminergic control of the striatum for high-level cognition. Current Opinion in Neurobiology, 21 , 402–407.

Commons, M. L. (2001). A short history of the Society for the quantitative analysis of behaviour. Behavior Analyst Today, 2 (3), 275–279.

Conn P.M. (Ed.). (2016). Conn’s translational neuroscience , Elsevier.

Coricelli, G. (2005). Two-levels of mental states attribution: From automaticity to voluntariness. Neuropsychologia, 43 (2), 294–300.

Coricelli, G., Critchley, H. D., Joffily, M., O’Doherty, J. P., Sirigu, A., & Dolan, R. J. (2005). Regret and its avoidance: A neuroimaging study of choice behaviour. Nature Neuroscience, 8 (9), 1255–1262.

Coricelli, G., Dolan, R. J., & Sirigu, A. (2007). Brain, emotion and decision making: The paradigmatic example of regret. Trends in Cognitive Science, 11 (6), 258–265.

Coricelli, G., & Nagel, R. (2009). Neural correlates of depth of strategic reasoning in medial prefrontal cortex. Proceedings of the National Academia of Science USA, 106 , 9162–9168.

Cournot, A.A. (1838). Recherches sur les principes mathématiques de la théorie des richesses . Paris: Librairie des sciences politiques et sociales (English: N. Bacon (ed). Researchs into the mathematical principles of the theory of wealth . London: Macmillan, 1987).

Cowen, A. S., & Keltner, D. (2017). Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proceedings of the National Academy of Science USA, 114 , 7900–7909.

Cox, C. L., Uddin, L. Q., Castellanos, F. X., Milham, M. P., & Kelly, C. (2011). The balance between feeling and knowing: Affective and cognitive empathy are reflected in the brain’s intrinsic functional dynamics. Social Cognitive and Affective Neuroscience, 7 (6), 727–737.

Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience . Oxford University Press.

Crockett, M.J. & Fehr, E. (2014). Pharmacology of economic and social decision making. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 259–279). Amsterdam: Elsevier.

Dalgleich, T. (2004). The emotional brain. Nature Reviews Neuroscience, 5 , 583–589.

Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron, 69 (4), 680–694.

Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and the human brain . A. Grosset/Putnam Books.

Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London B, 351 , 1413–1420.

Damasio, A. R. (2003). Looking for Spinoza: Joy, sorrow, and the feeling brain . Harcourt Inc.

Damasio, A.R. (2017). The stranger order of things. Life, feeling, and the making of cultures . New York: Pantheon Books.

D’Ardenne, K., McClure, S. M., Nystrom, L. E., & Cohen, J. D. (2008). BOLD responses reflecting dopaminergic signals in the human ventral tegmental area. Science, 319 , 1264–1267.

Darwin, C. (1859). On the origin of species by means of natural selection . New York: D. Appleton and Company (6th edition: 1872).

Darwin, C. (1872). The expression of the emotions in man and animals . London: John Murray (Chicago University Press, 1965).

Daw, N.D. (2014). Advanced reinforcement learning. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 299–320). Amsterdam: Elsevier.

Daw, N. D., Kakade, S., & Dayan, P. (2002). Opponent interactions between serotonin and dopamine. Neural Networks, 15 , 603–616.

Daw, N. D., Niv, Y., & Dayan, P. (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience, 8 (12), 1704–1711.

Daw, N.D. & O’Doherty, J.P (2014). Multiple systems for value learning. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 393–410). Amsterdam: Elsevier.

Daw, N.D. & Tobler, P.N. (2014). Value learning through reinforcement: The basics of dopamine and reinforcement learning. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 283–298). Amsterdam: Elsevier.

Dayan, P. (2008). The role of value systems in decision making. In C. Engel & W. Singer (Eds.), Better than conscious? Implications for performance and institutional analysis (pp. 51–70). MIT Press.

Dayan, E., Niv, Y., Seymour, B., & Daw, N. D. (2006). The misbehavior of value and the discipline of will. Neural Network, 19 , 1153–1160.

Dean, M. (2013). What can neuroeconomics tell us about economics (and vice versa). In P. H. Crowley & T. R. Zentall (Eds.), Comparative decision making (pp. 163–203). Oxford University Press.

Chapter   Google Scholar  

Decety, J., & Cacioppo, J. T. (Eds.). (2011). The Oxford handbook of social neuroscience . Oxford University Press.

Decety, J. (2014). The complex relation between morality and empathy. Trends in Cognitive Sciences, 18 (7), 337–339.

Decety, J., & Grèzes, J. (2006). The power of simulation: Imaging one’s own and other’s behaviour. Brain Research, 1079 (1), 4–14.

Declerck, C. & Boone, C. (2016). The neuroanatomy of prosocial decision making. In C. Declerck & C. Boone (Eds.), Neuroeconomics of prosocial behaviour. The compassionate egoist (Chap. 2). London: Elsevier.

Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013). Brain mechanisms for perceptual and reward-related decision-making. Progress in Neurobiology, 103 , 194–213.

Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70 (2), 200–227.

Dehaene, S., Changeux, J. P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: A testable taxonomy. Trends in Cognitive Sciences, 10 (5), 204–211.

Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56 (2), 384–398.

Dehaene, S., Duhamel, J.-R., Hauser, M. D., & Rizzolatti, G. (2005). From monkey brain to human brain . MIT Press.

Delgado, M. R., Franck, R. H., & Phelps, E. A. (2005). Perceptions of moral character modulate the neural systems of reward during the trust game. Nature Neuroscience, 8 , 1611–1618.

Delgado, M. R., Nystrom, L. E., Fissel, C., Noll, D. C., & Fiez, J. A. (2000). Tracking the hemodynamic responses to reward and punishment in the striatum. Journal of Neurophysiology, 84 , 3072–3077.

de Quervain, D. J., Fishbacher, U., Treyer, V., Schellhaller, M., Schnyder, U., Buck, A., & Fehr, E. (2004). The neural basis of altruistic punishment. Science, 305 , 1254–1258.

Di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: A neurophysiological study. Experimental Brain Research, 91 , 176–180.

Dolan, R. J., & Dayan, P. (2013). Goals and habits. Neuron, 80 (2), 312–325.

Doya, K. (2008). Modulators of decision making. Nature Neuroscience, 11 (4), 410–416.

Doya, K. & Kimura, M. (2014). The basal ganglia, reinforcement learning, and the encoding of value. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 321–334). Amsterdam: Elsevier.

Dreher, J.-C., & Tremblay, L. (2017). Decision neuroscience . Academic Press.

Dunn, B. D., Dalgleish, T., & Lawrence, A. D. (2006). The somatic marker hypothesis: A critical evaluation. Neuroscience and Biobehavioral Review, 30 , 239–271.

Efron, B., & Hastie, T. (2016). Computer-age statistical inference . Cambridge University Press.

Ekmann, P. (1982). Emotion in the human face. Studies in emotion and social interaction . Cambridge University Press.

Ekmann, P. (2003). Emotions revealed: Understanding faces and feeling . Weidenfeld and Nicolson.

Ellsberg, D. (1961). Risk, ambiguity and the Savage axioms. Quarterly Journal of Economics, 75 , 643–669.

Elliot, R., Agnew, Z., & Deakin, J. F. (2008). Medial orbitofrontal cortex codes relative rather than absolute value of financial rewards in human. European Journal of Neuroscience, 27 , 2213–2218.

Elliot, R., Friston, K. J., & Dolan, R. J. (2000). Dissociable neural responses in human reward system. Journal of Neuroscience, 20 , 6159–6165.

Engelmann, J.B. & Hare, T.A. (2018). Emotions can bias decision-making processes by promoting specific behavior. In A.S. Fox, R.C. Lapate, A.J. Shackman, & R.J. Davidson (Eds.), The nature of emotion. Fundamental questions (2nd ed.) (pp. 355–359). Oxford University Press.

Engelmann; J.B. & Fehr, E. . (2017). The neurobiology of trust and social decision: The important role of emotions. In P. A. M. Van Lange, B. Rockenbach, & T. Yamagishi (Eds.), Trust in social dilemmas (pp. 33–56). Oxford University Press.

Evans, J. (2010). Thinking twice: Two minds in one brain . Oxford University Press.

Evans, J. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science, 8 (3), 223–241.

Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995). Motor facilitation during action observation: A magnetic stimulation study. Journal of Neurophysiology, 73 , 2608–2611.

Farrer, C., & Frith, C. D. (2002). Experiencing oneself vs another person as being the cause of an action: The neural correlates of the experience of agency. NeuroImage, 15 (3), 586–603.

Fecteau, S., Pascual-Leone, A., Zald, D. H., Liguori, P., Théoret, H., Goggio, P. S., & Fregni, F. (2007). Activation of prefrontal cortex by transcranial direct current stimulation reduces appetite for risk during ambiguous decision making. The Journal of Neuroscience, 27 (23), 6212–6218.

Fehr, E. (2009). Social preferences and the brain. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.), Neuroeconomics. Decision making and the brain (pp. 215–232). Amsterdam: Elsevier.

Fehr, E., & Camerer, C. F. (2007). Social neuroeconomics: The neural circuitry of social preferences. Trend in Cognitive Sciences, 11 (10), 419–427.

Fehr, E. & Krajbich, I. (2014). Social preferences and the brain. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain . (2nd ed.) (pp. 193–218). Amsterdam: Elsevier.

Fehr, E., & Rangel, A. (2011). Neuroeconomic foundations of economic choices – Recent advances. Journal of Economic Perspectives, 25 (4), 3–30.

Fehr, E. & Schmidt, K.M. (2006). The economics of fairness, reciprocity and altruism. Experimental evidence and new theories. In S-C. Kolm & J. Mercier-Ythier (Eds.), Handbooks of the economics of giving, altruism and reciprocity , vol. 1: Foundations (Chap. 8). Amsterdam: Elsevier.

Fehr, T. (2013). A hybrid model for neural representation of complex mental processing in the human brain. Cognitive Neurodynamics, 7 (2), 89–103.

Fernandez-Dols, J.-M. & Russel, J.A. (Eds.) (2017). The science of facial expression . Oxford University Press.

Figner, B., Knoch, D., Jonhson, E. J., Krosch, A. R., Lisanby, S. H., Fehr, E., & Weber, E. U. (2010). Lateral prefrontal cortex and self-control in intertemporal choice. Nature Neuroscience, 13 (5), 538–539.

Fodor, J. A. (1983). The modularity of mind . MIT Press.

Fox, A., Lapate, R., Shackman, A., & Davidson, R. J. (Eds.). (2018). The nature of emotion. Fundamental questions (2nd ed.). Oxford University Press.

Fox, C.R. & Poldrack, R.A. (2014). Prospect theory and the brain. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain . (2nd ed.) (pp. 533–567). Amsterdam: Elsevier.

Frederick, S., Loewenstein, G., & O’Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40 (June), 351–401.

Frith, U. (2001). Mind blindness and the brain in autism. Neuron, 32 (6), 969–979.

Frith, U. (2012). The role of metacognition in human social interactions. Philosophical Transactions of the Royal Society of London, Series b, Biological Sciences, 367 (1599), 2213–2223.

Frith, U., & Frith, C. D. (2003). Development and neurophysiology of mentalizing. Philosophical Transactions of the Royal Society b: Biological Sciences, 358 (1431), 459–473.

Frith, U., & Frith, C. D. (2006). The neural basis of mentalizing. Neuron, 50 (4), 531–534.

Frith, U., & Frith, C. D. (2007). Social cognition in humans. Current Biology, 21 , R724–R732.

Frith, U., & Singer, T. (2008). The role of social cognition in decision making. Philosophical Transactions of the Royal Society b: Biological Sciences, 263 , 3875–3886.

Fudenberg, D., & Levine, D. K. (1998). Theory of learning in games . MIT Press.

Fudenberg, D., & Levine, D. K. (2006). A dual-self model of impulse control. American Economic Review, 96 (5), 1449–1476.

Fuster, J.M. (2008). The prefrontal cortex: Anatomy, physiology and neuropsychology of the frontal lobe (4th ed.). Philadelphia: Lippincott-Raven (1st ed.: 1980).

Fuster, J. M. (2009). Cortex and memory: Emergence of a new paradigm. Journal of Cognitive Neuroscience, 21 (11), 2047–2072.

Fuster, J. M., & Bressler, S. L. (2012). Cognitive activation: A mechanism enabling temporal integration in working memory. Trends in Cognitive Sciences, 16 (4), 207–218.

Gallagher, H. L., & Frith, C. D. (2003). Functional imaging of ‘the theory of mind.’ Trends in Cognitive Sciences, 7 (2), 77–83.

Gallagher, H. L., Jack, A. I., Roepstorff, A., & Frith, C. D. (2002). Imaging the intentional stance in a competitive game. NeuroImage, 16 , 814–821.

Gallese, V., Fadiga, L., Fogassi, L., & Rizzolatti, G. (1996). Action recognition in the premotor cortex. Brain, 119 , 593–609.

Gazzaniga, M. S., & Mangun, G. R. (Eds.). (2014). The cognitive neurosciences (5th ed.). MIT Press.

Genon, S., Andrew, R., Langner, R., Amunts, K., & Eickhoff, S. B. (2018). How to characterize the functions of brain region. Trends in Cognitive Sciences, 22 (4), 350–364.

Giorgetta, C., Grecucci, A., Bonini, N., Coricelli, G., Demarchi, G., Braun, C., & Sanfey, S. G. (2013). Waves of regret: A MEG study of emotion and decision making. Neuropsychologia, 51 , 38–51.

Gläscher, J., Adolphs, R., Damasio, H., Bechara, A., Rudrauf, D., Calamia, M., Paul, L. K., & Tranel, D. (2012). Lesion mapping of cognitive control and value-based decision making in the prefrontal cortex. Proceedings of the National Academy of Sciences, 109 (36), 14681–14686.

Gläscher, J., Daw, N., Dayan, P., & O’Doherty, J. P. (2010). States versus rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron, 66 (4), 585–595.

Glimcher, P. W. (2003). Decisions, uncertainty, and the brain: The science of neuroeconomics . MIT Press.

Glimcher, P. W. (2011a). Foundations of neuroeconomic analysis . Oxford University Press.

Glimcher, P. W. (2011b). Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis. Proceedings of the National Academy of Science USA, 108 (Suppl. 3), 15647–15654.

Glimcher, P.W. (2014a). Value-based decision making. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 373–391). Amsterdam: Elsevier.

Glimcher, P.W. (2014b). Introduction to neuroscience. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain . (2nd ed.) (pp. 63–76), Amsterdam: Elsevier.

Glimcher, P.W. (2014c). Understanding the hows and whys of decision-making: From expected utility to divisive normalization. Cold Springer Harbor Symposia on Quantitative Biology , LXXIX , 169–176.

Glimcher, P. W., Camerer, C. F., Fehr, E., & Poldrack, R. A. (Eds.). (2009). Neuroeconomics. Decision making and the brain . Elsevier.

Glimcher, P.W., Camerer, C.F., Fehr, E., & Poldrack, R.A. (Eds.) (2009). Introduction. A brief history of neuroeconomics. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 1–12). Amsterdam: Elsevier.

Glimcher, P. W., Dorris, M. C., & Bayer, H. M. (2005). Physiological utility theory and the neuroeconomics of choice. Games and Economic Behavior, 52 (2), 213–256.

Glimcher, P. W., & Fehr, E. (Eds.). (2014a). Neuroeconomics. Decision making and the brain (2nd ed.). Elsevier.

Glimcher, P.W. & Fehr, E. (2014b). Introduction: A brief history of neuroeconomics. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. xvii-xxviii). Amsterdam: Elsevier.

Glimcher, P. W., & Rustichini, A. (2004). Neuroeconomics: The consilience of brain and decision. Science, 206 , 447–452.

Gold, J.I. & Heekeren, H.R. (2014). Neural mechanism for perceptual decision making. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 355–372). Amsterdam: Elsevier.

Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of Neuroscience, 30 , 535–574.

Goldman, A. I. (2006). Simulating minds: The philosophy, psychology, and neuroscience of mind-reading . Oxford University Press.

Grabenhorst, F., & Rolls, E. T. (2011). Value, pleasure, and choice systems in the ventral prefrontal cortex. Trends in Cognitive Sciences, 15 , 56–67.

Green, D.M. & Sweets, J.A. (1966). Signal detection theory and psychophysics . New York: Wiley (reprinted: Los Altos, CA: Peninsula Publishing, 1988).

Gul, F., & Pesendorfer, W. (2006). Random expected utility. Econometrica, 74 , 121–146.

Gul, F. & Pesendorf, W. (2008). The case of mindless economics. In A. Caplin A. & A. Schotter (Eds.). The foundation of positive and normative economics: A handbook (pp. 3–39). New York: Oxford University Press.

Harbaugh, W. T., Mayr, U., & Burghart, D. R. (2007). Neural responses to taxation and voluntary giving reveal motives for charitable donations. Science, 316 , 1622–1625.

Hare, T. A., Camerer, C. F., Knoepfle, D. T., & Rangel, A. (2010). Value computations in ventral medial prefrontal cortex during charitable decision making incorporate input from regions involved in social cognition. Journal of Neuroscience, 30 , 583–590.

Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-control in decision-making involves modulation of the vmPFC valuation system. Science, 324 , 646–648.

Hare, T. A., O’Doherty, J. P., Camerer, C. F., Schultz, W., & Rangel, A. (2008). Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors. Journal of Neuroscience, 28 (22), 5623–5630.

Hare, T. A., Schultz, W., Camerer, C. F., O’Doherty, J. P., & Rangel, A. (2011). Transformation of stimulus value signals into motor commands during simple choice. Proceedings of the National Academy of Science USA, 107 , 18120–18125.

Harrison, G. W. (2008a). Neuroeconomics: A critical reconsideration. Economics and Philosophy, 24 , 303–344.

Harrison, G. W. (2008b). Neuroeconomics: Rejoinder. Economics and Philosophy, 24 , 533–544.

Harrison, G. W., & Ross, D. (2010). The methodology of neuroeconomics. Journal of Economic Methodology, 17 (2), 185–196.

Haxby, J. V., Connolly, A. C., & Guntupalli, J. S. (2014). Decoding neural representational spaces using multivariate pattern analysis. Annual Review of Neuroscience, 37 , 435–456.

Herrnstein, R. J. (1961). Relative and absolute strength of response as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behavior, 4 (3), 267–272.

Herrnstein, R. J., & Prelec, D. (1991). Melioration: A theory of distributed choice. Journal of Economic Perspectives, 5 (3), 137–156.

Heukelom, F. (2014). Behavioral economics. A history . Cambridge University Press.

Houk, J.C., Adams, J.L. and Barto, A.G. (1995). A model of how the basal ganglia generate and use neural signals that predict reinforcemement. In J.C. Houk J.C., Davis, J.L., & Beiser, D.G. (Eds.). Models of information processing in the basal ganglia (pp. 249–270). Boston: The MIT Press.

Houser, D. & McCabe, K. (2009). Experimental neuroeconomics and non-cooperative games. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 47–62). Amsterdam: Elsevier.

Houser, D. & McCabe, K. (2014). Experimental economics and experimental game theory. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 19–34). Amsterdam: Elsevier.

Howe, M. W., & Dombeck, D. A. (2016). Rapid signaling in distinct dopaminergic axons during locomotion and reward. Nature, 535 (7613), 505–510.

Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F. (2005). Neural systems responding to degrees of uncertainty in human decision-making. Science, 310 , 1680–1683.

Hsu, M., & Zhu, L. (2012). Learning in games: Neural computations underlying strategic learning. Recherches Économiques De Louvain, 78 (3), 47–72.

Huettel, S. A. (2010). Ten challenges for decision neuroscience. Frontiers in Neuroscience, 4 , 171–185.

Huettel, S. A., Stowe, C. J., Gordon, E. M., Warner, B. T., & Platt, M. L. (2006). Neural signatures of economic preferences for risk and ambiguity. Neuron, 49 , 765–775.

Hutcherson, C. A., Bushong, B., & Rangel, A. (2015). A neurocomputational model of altruistic choice and its implication. Neuron, 87 , 451–462.

Hyde, J. W., Simon, C. E., Ting, F., & Nikolaeva, J. I. (2018). Functional organization on the temporo-parietal junction for theory of mind in preverbal infants: A near-infrared spectroscopy study. Journal of Neuroscience, 38 (18), 4264–4274.

Iacoboni, M., Molnar-Szakacs, I., Gallese, V., Buccino, G., Mazziotta, J. C., & Rizzollatti, G. (2005). Grasping the intentions of others with one’s own mirror neuron system. PLoS Biology, 3 , e79.

James, W. (1890). Principles of psychology . Holt, Rinehart and Winston.

Joffily, M., Masclet, D., Noussair, C., & Villeval, M. C. (2014). Emotions, sanctions, and cooperation. Southern Economic Journal, 80 , 1002–1027.

Jung, W. H., Lee, S., Lerman, C., & Kable, J. W. (2016). Amygdala functional and structural connectivity predicts individual risk tolerance. Neuron, 98 , 394–404.

Kable, J.W. (2014). Valuation, intertemporal choice, and self-control. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 173–192). Amsterdam: Elsevier.

Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature Neuroscience, 10 (12), 1625–1633.

Kable, J. W., & Glimcher, P. W. (2009). The neurobiology of decision: Consensus and controversy. Neuron, 63 , 733–745.

Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93 (5), 1449–1475.

Kahneman, D. (2011). Thinking, fast and slow . FSG.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An anakysis of decision under risk. Econometrica , 47 , 263–291.

Kahneman, D., Wakker, P., & Sarin, R. (1997). Back to Bentham? Explorations of experienced utility. Quarterly Journal of Economics, 112 , 375–405.

Kanayet, F. J., Opfer, J. E., & Cunningham, W. A. (2014). The value of numbers in economic rewards. Psychological Science, 25 , 1534–1545.

Karton, I., & Bachmann, T. (2011). Effect of prefrontal transcranial magnetic stimulation on spontaneous truth-telling. Behavioral Brain Research, 225 (1), 209–214.

Kelso, J. A. S., & Engstrom, D. A. (2006). The complementary nature . MIT Press.

Kenning, P., & Plassmann, H. (2005). Neuroeconomics: An overview from an economic perspective. Brain Research Bulletin, 67 , 343–354.

Keynes, J. M. (1936). The general theory of interest, employment, and money . Macmillan.

Kilner, J. M., Neal, A., Weiskopf, N., Friston, K. J., & Frith, C. D. (2009). Evidence of mirror neurons in human inferior frontal gyrus. Journal of Neuroscience, 29 , 10153–10159.

King-Casas, B., Tomlin, D., Anen, C., Camerer, C. F., Quartz, S. R., & Montague, P. R. (2005). Getting to know you: Reputation and trust in a two-person economic exchange. Science, 308 , 78–83.

Knoch, D., & Fehr, E. (2007). Resisting the power of temptations: The right prefrontal cortex and self-control. Annual New York Academy of Science, 1104 , 123–134.

Knoch, D., Gianotti, L. R., Pascual-Leone, A., Treyer, V., Regard, M., Hoffman, M., & Brugger, P. (2006a). Disruption of right prefrontal cortex by low-frequency repetitive transcranial magnetic stimulation induces risk-taking behavior. Journal of Neuroscience, 26 , 6469–6472.

Knoch, D., Nitsche, M. A., Fischbacher, U., Eisenegger, C., Pascual-Leone, A., & Fehr, E. (2008). Studying the neurobiology of social interaction with transcranial direct current stimulation – The example of punishing unfairness. Cerebral Cortex, 18 (9), 1987–1990.

Knoch, D., Pascual-Leone, A., Meyer, K., Treyer, V., & Fehr, E. (2006b). Diminishing reciprocal fairness by disrupting the right prefrontal cortex. Science, 314 , 829–832.

Knoch, D., Schneider, F., Schunk, D., Hohmann, M., & Fehr, E. (2009). Disrupting the prefrontal cortex diminishing the human ability to build a good reputation. Proceedings of the National Academy of Science of the USA, 106 , 20895–20899.

Knutson, B.K., Delgado, M.R., & Phillips, P.E.M. (2009). Representation of subjective value in the striatum. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 389–406). Amsterdam: Elsevier.

Knutson, B. K., Fong, G. W., Adams, C. M., Varer, J. L., & Hommer, D. (2001). Dissociation of reward anticipation and outcome with event related fMRI. NeuroReport, 12 , 3883–3687.

Knutson, B. K., Fong, G. W., Bennett, S. M., Adams, C. M., & Homme, D. (2003). A region of mesial prefrontal cortex tracks monetary rewarding outcomes: Characterisation with rapid event-related fMRI. NeuroImage, 18 , 263–272.

Knutson, B. K., Ricks, S., Wimmer, G. S., Prelec, D., & Loewenstein, G. (2007). Neural predictors of purchases. Neuron, 53 , 147–156.

Knutson, B. K., Westdorp, A., Kaiser, E., & Hommer, D. (2000). FMRI visualization of brain activity during a monetary incentive delay task. NeuroImage, 12 , 20–27.

Konovalov, A., & Krajbich, I. (2019). Over a decade of neuroeconomics: What have we learned? Organizational Research Methods, 22 (1), 148–173.

Koppel, L., Andersson, D., Morrison, I., Västfjäll, D., & Tinghög, G. (2017). The (null) effect of affective touch on betrayal aversion, altruism, and risk taking. Frontiers in Behavorial Neurosciences, 11 (Art 251), 1–11.

Kosfeld, M., Heinrichs, M., Zak, P., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature, 435 , 673–676.

Kragel, P., & LaBar, K. S. (2016). Decoding the nature of emotion in the brain. Trends in Cognitive Sciences, 20 (6), 444–455.

Krajbich, I., Adolphs, R., Tranel, D., Denburg, N. L., & Camerer, C. F. (2009). Economic games quantify diminished sense of guilt in patients with damage to the prefrontal cortex. Journal of Neuroscience, 29 , 2188–2192.

Krajbich, I., Armel, K. C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neurosciences, 13 , 1292–1298.

Krajbich, I., & Dean, M. (2015). How can neuroscience inform economics? Current Opinion in Behavioral Sciences, 5 , 51–57.

Krajbich, I., Hare, T., Bartling, B., Morishima, Y., & Fehr, E. (2015). A common mechanism underlying food choice and social decision. PLos Computational Biology, 11 , e1004371.

Krajbich, I., Lu, D., Camerer, C. F., & Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3 , 1–18.

Krajbich, I., Oud, B., & Fehr, E. (2014). Benefits of neuroeconomic modeling: New policy interventions and predictors of preference. American Economic Review, 104 (5), 501–506.

Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Science USA, 108 , 13852–13857.

Krupenye, C., Kano, F., Hirata, S., Call, J., & Tomasello, M. (2016). Great apes anticipate that other individuals will act according to false belief. Science, 354 (6308), 110–114.

Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47 , 763–770.

Laibson, D. (1997). Golden Eggs and Hyperbolic Discounting. Quarterly Journal of Economics, 112 (2), 443–477.

Landreh, A., & Bickle, J. (2008). Neuroeconomics, neurophysiology and the common currency hypothesis. Economics and Philosophy, 24 , 419–429.

LeDoux, J. E. (1996). The emotional brain: Mysterious underpinnings of emotion life . Simon and Schuster.

Lempert, K.M. & Phelps, E.A. (2014). Neuroeconomics of emotion and decision making. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 219–236). Amsterdam: Elsevier.

Lengyel, M., & Dayan, P. (2007). Hippocampal contributions to control: The third way. Advances in Neural Information Processing Systems, 20 , 889–896.

Levy, D. J., & Glimcher, P. W. (2012). The root of all value: A neural common currency for choice. Current Opinion in Neurobiology, 22 , 1027–1038.

Levy, I., Snell, J., Nelson, A. J., Rustichini, A., & Glimcher, P. W. (2010). Neural representation of subjective value under risk and ambiguity. Journal of Neurophysiology, 103 , 1036–1047.

Lipps, T. (1903). Asthetic. Psychologie des Schönen und der Kunst , 2 vol., Hambourg und Leipzig: Leopold Voss.

Lieberman, M. D. (2012). A geographical history of social cognitive neuroscience. NeuroImage, 61 , 432–436.

Lindquist, K. A., & Barrett, L. F. (2012). A functional architecture of the human brain: Emerging insights from the science of emotion. Trends in Cognitive Sciences, 16 (11), 533–540.

Lindquist, K. A., Satpute, A. B., Wagner, T. D., Weber, J., & Barrett, L. F. (2016). The brain basis of positive and negative affect. Evidence from a meta-analysis of the human neuroimaging literature. Cerebral Cortex, 26 , 1910–1922.

Lis, S., & Kirsch, P. (2016). Neuroecnomic approaches in mental disorders. In M. Reuter & C. Montag (Eds.), Neuroeconomics (pp. 333–344). Springer-Verlag.

Lombardo, M. V., Chakravarti, B., Bullmore, E. T., Wheelwright, S. J., Sadeh, S. A., Suckling, J., MRC AIMS Consortium, & Baron-Cohen, S. (2010). Shared neural circuits for mentalizing about the self and others. Journal of Cognitive Neuroscience, 22 (7), 1623–1639.

Loewenstein, G., & Elster, J. (1992). Choice over time . Russel Sage.

Loewenstein, G. & O’Donoghue, T. (2004). Animal spirits: Affective and deliberative processes in economic behavior. Working paper 04–14, Center for Analytic Economics, Cornell University.

Loewenstein, G., & Prelec, D. (1992). Anomalies in intertemporal choice: Evidence and an interpretation. Quarterly Journal of Economics, 107 (2), 573–597.

Loewenstein, G., Rick, S., & Cohen, J. D. (2008). Neuroeconomics. Annual Review of Psychology., 59 , 647–672.

Lohrenz, T., McCabe, K., Camerer, C. F., & Montague, P. R. (2007). Neural signature of fictive learning signals in a sequential investment task. Proceedings of the National Academy of Sciences, 104 (22), 9493–9498.

Loomes, G., & Sudgen, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. Economic Journal, 92 , 805–824.

Louie, K.L. & De Martino, B. (2014). The neurobiology of context-dependent valuation and choice. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 455–476). Amsterdam: Elsevier.

Luce, R. D. (1959/2005). Individual Choice Behavior: A Theoretical Analysis . New York: Wiley (reprinted by Dover Publications).

MacDonald, K., & MacDonald, T. M. (2010). The peptide that binds: A systematic review of oxytocin and its prosocial effects in humans. Harvard Review of Psychiatry, 18 (1), 1–21.

Mackey, S., & Petrides, M. (2014). Architecture and morphology of the human ventromedial prefrontal cortex. European Journal of Neuroscience, 40 (5), 2777–2796.

MacLean, P. D. (1952). Some psychiatric implications of physiological studies on frontotemporal portion of limbic system (visceral brain). Electroencephalography: The basal and temporal region. Yale Journal of Biology and Medicine, 22 , 407–418.

MacLean, P. D. (1970). The triune brain in evolution: Role in paleocerebral function plenum . Plenum Press.

Macmillan, M. (2000). An odd kind of fame: Stories of Phineas Gage . MIT Press.

Macmillan, N. A., & Creelmann, C. D. (2004). Detection theory: A user’s guide . Lawrence Erlbaum.

Mahy, C. E. V., Moses, L. J., & Pfeifer, J. H. (2014). How and where: Theory-of-mind in the brain. Developmental Cognitive Neuroscience, 9 , 68–81.

Mäki, U. (2010). When economics meet neuroscience: Hype or hope. Journal of Economic Methodology, 17 , 107–117.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7 , 77–91.

Martin, D., Mehtra, M. A., & Prata, D. (2017). The «highs and lows» of the human brain on dopaminergics: Evidence from neuropharmacology. Neuroscience and Biobehavioral Reviews, 80 , 351–371.

Mayr, U., Harbaugh, W.T., & Tankersley, D. (2009). Neuroeconomics of charitable giving and philanthropy. In P.W. Glimcher, C.F. Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 303–320). Amsterdam: Elsevier.

McCabe, K. A. (2003). Neuroeconomics. In L. Nadel (Ed.), Encyclopedia of cognitive science (Vol. 3, pp. 294–298). Publishing Group, Macmillan Publishers Ltd.

McCabe, K. A. (2008). Neuroeconomics and the economic science. Economics and Philosophy, 24 (3), 345–368.

McCabe, K. A., Houser, R., Smith, V., & Trouard, T. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. Proceedings of the National Academy of Science USA, 98 , 11832–11835.

McClure, S. M., Berns, G. S., & Montague, P. R. (2003). Temporal prediction errors in a passive learning task activate human striatum. Neuron, 38 , 339–346.

McClure, S. M., Ericson, K. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2007). Time discounting for primary rewards. Journal of Neuroscience, 27 (21), 5796–5804.

McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004a). Separate neural systems value immediate and delayed monetary rewards. Science, 306 , 503–307.

McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., & Montague, P. R. (2004b). Neural correlates of behavioral preferences for culturally familiar drinks. Neuron, 44 , 379–387.

McCullogh, W. (1965). Embodiments of mind . MIT Press.

McCullogh, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5 (4), 115–133.

McFadden, D. (1974). Conditional logit analysis of qualitative choice behaviour. In P. Zarembka (Ed.), Frontier in econometrics (pp. 105–142). Academic Press.

McFadden, D. (2005). Revealed stochastic preference: A synthesis. Economic Theory, 26 , 245–264.

Melnikoff, D. E., & Bargh, J. A. (2018). The mythical number two. Trends in Cognitive Sciences, 22 , 280–293.

Mikolajczak, M., Gross, J. J., Lane, A., Corneille, O., De Timary, Ph., & Luminet, O. (2010). Oxytocin makes people trusting, not gullible. Psychological Science, 21 , 1072–1075.

Millan, M. J. (2013). An epigenetic framework for neurodevelopmental disorders: From pathogenesis to potential therapy. Neuropharmacology, 68 , 2–82.

Miller, E. K. (2000). The prefrontal cortex and cognitive control. Nature Review Neuroscience, 1 , 59–65.

Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24 , 167–202.

Moll, J., Krueger, F., Zahn, R., Pardini, M., Oliveira-Souza, R., & Grafman, J. (2006). Human fronto-mesolimbic networks guide decisions about charitable decisions. Proceedings of the National Academy of Science USA, 103 , 15623–15628.

Mollenberghs, P., Cunnington, R., & Mattingley, J. B. (2012). Brain region with mirror properties: A meta-analysis of 125 human fMRI studies. Neuroscience and Biobehavioral Reviews, 36 , 341–349.

Mollenberghs, P., Johnson, H., Henrey, J. D., & Mattingley, J. B. (2016). Understanding the mind of others: A neuroimagery meta-analysis. Neuroscience and Biobehavioral Reviews, 65 , 276–291.

Mongin, P. & d’Aspremont, C. (1998). Utility theory and ethics. In S. Barbera, P. Hammond, & C. Seidel (Eds.), Handbook of utility theory, vol. 1: Principles (chap. 10), Dordrecht: Kluwer Academic Pub.

Montague, P. R. (2007). Neuroeconomics: A view from neuroscience. Functional Neurology, 22 (4), 219–234.

Montague, P. R., & Berns, G. S. (2002). Neural economics and the biological substrates of valuation. Neuron, 36 , 265–284.

Montague, P. R., Dayan, P., Person, C., & Sejnowski, T. J. (1995). Bee foraging in uncertain environments using predictive Hebbian learning. Nature, 377 , 725–728.

Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning”. Journal of Neuroscience, 16 , 1936–1947.

Moulin, H. (1986). Game theory for social sciences. 2nd rev. ed., New York: New York University Press.

Mukamel, R., Ekstrom, A. D., Kaplan, I., & M., & Fried, I. . (2010). Single-neuron responses in humans during execution and observation of actions. Current Biology, 20 (8), 750–756.

Nagel, R.-M. (1995). Unraveling in guessing games: An experimentally study. American Economic Review, 85 , 1313–1326.

Nave, G., Camerer, C. F., & McCullough, M. (2015). Does oxytocin increase trust in humans? A critical review of research. Perspectives on Psychological Science, 10 , 772–789.

Niv, Y. & Montague, P.R. (2009). Theoretical and empirical studies of learning. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 331–351). Amsterdam: Elsevier.

Normann, K. A., Polyn, S. M., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Science, 10 (9), 424–430.

O’Doherty, J. P., Cockburn, J., & Pauli, W. M. (2017). Learning, reward, and decision making. Annual Review of Psychology, 68 , 73–100.

O’Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003). Temporal difference models and reward-related learning in the human brain. Neuron, 38 , 329–337.

O’Doherty, J. P., Shiv, B., & Rangel, A. (2008). Marketing actions can modulate neural representations of experienced pleasantness. Proceedings of the National Academy of Sciences, 105 (3), 1050–1054.

Okon-Singer, H., Hendler, D., Pessoa, L., & Shackman, A. J. (2015). The neurobiology of emotion-cognition interactions: Fundamental questions and strategies for future research. Frontiers in Human Neuroscience, 9 , 1–14.

Okon-Singer, H., Stout, D.M., Stockbridge, M.D., Gamer, M.D., Fox, M., & Shackman, A.J. (2018). The interplay of emotion and cognition. In A. Fox, R. Lapate, A. Shackman, & R.J. Davidson (Eds.). The nature of emotion. Fundamental questions (2nd ed.) (181–185). Oxford: Oxford University Press.

Oliver, L. D., Vieira, J. B., Neufeld, R. W. J., Dziobek, I., & Mitchell, D. G. V. (2018). Greater involvement of action simulation mechanisms in emotional vs cognitive empathy. Social Cognitive and Affective Neuroscience, 13 (4), 367–380.

Olson, I. R., McCoy, D., Klobusicky, E., & Ross, L. A. (2013). Social cognition and the anterior temporal lobes: A review and theoretical framework. Social Cognitive and Affective Neuroscience, 8 , 123–133.

Ongür, D., Ferry, A. T., & Price, J. L. (2003). Architectonic subdivision of the human orbital and medial prefrontal cortex. Journal of Comparative Neurology, 460 , 425–449.

Ongür, D., & Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10 , 206–219.

Padoa-Schioppa, C. (2009). Range-adapting representation of economic value in the orbitofrontal cortex. Journal of Neuroscience, 29 , 14004–14014.

Padoa-Schioppa, C. (2011). Neurobiology of economic choice: A good-based model. Annual Review of Neuroscience, 34 , 333–359.

Padoa-Schioppa, C., & Assad, J. A. (2006). Neurons in the orbitofrontal cortex encode economic value. Nature, 441 , 223–226.

Padoa-Schioppa, C., & Assad, J. A. (2008). The representation of economic value in the orbitofrontal cortex is invariant for changes in menu. Nature Neuroscience, 11 , 95–102.

Padoa-Schioppa, C., & Conen, K. E. (2017). Orbitofrontal cortex: A neural circuit for economic decisions. Neuron, 96 (4), 736–754.

Palmer, J., Huk, A. C., & Shadlen, M. N. (2005). The effect of stimulus strength on the speed and accuracy of a perceptual decision. Journal of Vision, 5 (5), 376–404.

Panksepp, J. (1998). Affective neuroscience. The foundations of human and animal emotions . Oxford University Press.

Papez, J. W. (1937). A proposed mechanism of emotion. Archives of Neurology and Psychiatry, 38 (4), 725–743.

Passingham, R. E., & Wise, S. P. (2012). The neurobiology of prefrontal cortex: Anatomy, evolution, and the origin of insight . Oxford University Press.

Pavlov, I.P. (1927/1960). Conditioned reflexes: An investigation of the physiological activity of the cerebral cortex . New York: Dover Publications (unaltered republication of the 1927 translation in 1960 by Oxford University Press).

Pennycook, G., De Neys, W., Evans, St., & B.T., Stanovitch, K.E., & Thompson, V.A. . (2018). The mythical dual-process typology. Trends in Cognitive Sciences, 22 , 667–668.

Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006). Dopamine-dependent prediction errors underpin reward-seeking behavior in humans. Nature, 442 (7106), 1042–1045.

Pessoa, L. (2010). Emotion and cognition and the amygdala: From ‘what is it?’ to ‘what’s to be done?’ Neuropsychologia, 48 , 3416–3429.

Pessoa, L. (2013). The cognitive-emotional brain: From interactions to integration . MIT Press.

Pessoa, L. (2016). The emotional brain. In P.M. Conn (Ed.), Conn’s translational neuroscience (pp. 635–656). Elsevier.

Pessoa, L. (2017). A network model of the emotional brain. Trends in Cognitive Sciences, 21 (5), 357–371.

Phelps, E.A. (2009). The study of emotions in neuroeconomics. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 233–250). Amsterdam: Elsevier.

Phelps, E. A., & LeDoux, J. E. (2005). Contribution of the amygdala to emotion processing: From animal models to human behavior. Neuron, 48 , 175–187.

Plassman, H., O’Doherty, J. P., & Rangel, A. (2007). Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. Journal of Neuroscience, 27 , 9984–9988.

Platt, M. L., & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400 , 233–238.

Platt, M. L., & Huettel, S. A. (2008). Risky business: The neuroeconomics of decision making under uncertainty. Nature Neuroscience, 11 , 398–303.

Platt, M.L. & Plassman, H. (2014). Multistage valuation signals and common neural currencies. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 237–258). Amsterdam: Elsevier.

Plutchik, R. (1980). Emotion: A psychoevolutionary synthesis . Harper & Row.

Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trend in Cognitive Sciences, 10 (2), 59–63.

Poldrack, R. A. (2011). Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding. Neuron, 72 (5), 692–697.

Poldrack, R. A. (2018). The new mind reader . Princeton University Press.

Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munato, M. R., Nichols, T. E., Poline, J. B., Vul, E., & Yakoni, T. (2017). Scanning the horizon: Towards transparent and reproducible neuroimaging resource. Nature Review Neuroscience, 18 (2), 115–126.

Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of functional MRI data analysis . Cambridge University Press.

Premack, D., & Woodruff, G. (1978). Does the chimpanzee have a ‘theory of mind’? Behavioral and Brain Sciences, 4 , 515–526.

Preuschoff, K., Bossaerts, P., & Quartz, S. R. (2006). Neural differentiation of expected reward and risk in human subcortical structures. Neuron, 51 , 381–390.

Preuschoff, K., Quartz, S. R., & Bossaerts, P. (2008). Human insula activation reflects risk prediction errors as well as risk. Journal of Neuroscience, 28 , 2745–2752.

Pribram, K. H. (1973). The primate frontal cortex – Executive of the brain. In K. H. Pribam & A. R. Luria (Eds.), Psychophysiology of the frontal lobes (pp. 293–314). Academic Press.

Purves, D., Augustine, G.J., Fitzpatrick, D., Hall, W.C., LaMantia, A-S., McNamara, J.O., & White, L.E. (Eds.) (2011), Neuroscience (5th ed.). Sinauer Associates, Inc.

Quartz, S. R. (2008). From cognitive science to cognitive neuroscience to neuroeconomics. Economics & Philosophy, 24 , 459–471.

Ramnani, N., & Owen, A. M. (2004). Anterior prefrontal cortex: Insights into function from anatomy and neuroimaging. Nature Reviews Neuroscience, 5 , 185–194.

Rangel, A. (2009). The computation and comparison value in goal-directed choice. In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.). Neuroeconomics. Decision making and the brain (pp. 425–440). Amsterdam: Elsevier.

Rangel, A., Camerer, C. F., & Montague, P. R. (2008). A framework for studying the neurobiology of value-based decision making. Nature Reviews Neuroscience, 9 , 545–556.

Rangel, A & Clithero, J.A. (2014). The computation of stimulus values in simple choice. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2 nd ed.) (pp. 125–148). Amsterdam: Elsevier.

Rangel, A., & Hare, T. (2010). Neural computations associated with goal-directed choice. Current Opinion in Neurobiology, 20 , 262–270.

Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85 (2), 59–108.

Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in Cognitive Science, 20 (4), 260–281.

Reiman, M., & Bechara, A. (2010). The somatic marker framework as a neurological theory of decision-making: Review, conceptual comparisons, and future neuroeconomics research. Journal of Economic Psychology, 31 , 767–776.

Reuter, M., & Montag, C. (Eds.). (2016). Neuroeconomics . Springer-Verlag.

Richter, T., Shackman, A.J., & Okon-Singer, H. (2017). The neurobiology of emotion-cognition interactions. In B. Baune & C. Harmer (Eds.), Cognitive dimensions of major depressive disorder: Cognitive, emotional and social cognitive processes . New York: Oxford University Press.

Riedl, R., & Javor, A. (2012). The biology of trust: Integrating evidence from genetics, endocrinology and functional brain imaging. Journal of Neuroscience, Psychology, and Economics, 5 (2), 63–91.

Rilling, J. K., Gutman, D., Zeh, T., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002). A neural basis for social cooperation. Neuron, 35 , 395–405.

Rilling, J. K., King-Casas, B., & Sanfey, A. G. (2008). The neurobiology of social decision-making. Current Opinion in Neurobiology, 18 , 159–165.

Rilling, J. K., & Sanfey, A. G. (2011). The neurosciences of social decision-making. Annual Review of Psychology, 62 , 43–48.

Rilling, J. K., Sanfey, A. G., Aronso, J. A., Nystrom, L. E., & Dohen, J. D. (2004). The neural correlate of theory of mind within interpersonal interactions. NeuroImage, 22 (4), 1694–1703.

Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27 , 169–192.

Rizzolatti, G., Fadiga, L., Gallese, V., & Fogassi, L. (1996a). Premotor cortex and the recognition of motor actions. Cognitive Brain Research, 3 , 131–141.

Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience, 2 , 661–670.

Rizzolatti, G., Fogassi, L., & Gallese, V. (2009). The mirror neuron system: A motor-based mechanism for action and intention understanding. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (4th ed., pp. 635–640). MIT Press.

Rizzolatti, G., Fogassi, L., Matteli, M., Bettirnadi, V., Paulesu, E., Perani, D., & Fazio, F. (1996b). Localization of grasp representations in humans by PET: 1 Observation versus Execution. Experimental Brain Research, 111 , 246–252.

Rizzolatti, G., & Sinigaglia, C. (2006). So quel che fai. Il cervelloche agisce e i neuroni specchio . Raffaello Cortina Editore.

Roemer, J. E. (1996). Theories of distributive justice . Harvard University Press.

Roitman, J. D., & Shadlen, M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22 , 9475–9489.

Rolls, E. T. (2014). Emotion and decision-making explained . Oxford University Press.

Rose, N. & Abi-Rached, J.MM (2013). Neuro: The new brain sciences and the management of the mind . Harvard University Press.

Ross, D. (2005). Economic theory and cognitive science: Microexplanation . MIT Press.

Ross, D. (2008). Two styles of neuroeconomics. Economics and Philosophy, 24 (3), 473–483.

Ross, D. (2010). Neuroeconomics and economic methodology. In J. B. Davis & D. W. Hands (Eds.), Handbook of economic methodology (pp. 61–93). Edward Elgar.

Roth, A. E., & Erev, I. (1995). Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior, 8 (1), 164–212.

Rubinstein, A. (2008). Comments on neuroeconomics. Economics and Philosophy, 24 (3), 485–494.

Ruff, C. C., & Fehr, E. (2014). The neurobiology of rewards and values in social decision. Nature Review Neuroscience, 15 , 549–562.

Ruff, C.C. & Huettel, S.A. (2014). Experimental methods in cognitive neuroscience. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 77–108). Amsterdam: Elsevier.

Ruff, C. C., Ugazio, U., & Fehr, E. (2013). Changing social norm compliance with non-invasive brain stimulation. Science, 342 , 482–484.

Rushworth, M. F. S., Mars, R. B., & Sallet, J. (2013). Are there specialized circuits for social cognition and are they unique to humans? Current Opinion in Neurobiology, 23 , 436–442.

Rushworth, M. F. S., Noonan, N. P., Boorman, E. D., Walton, M. E., & Behren, T. E. (2011). Frontal cortex and reward-guided learning and decision-making. Neuron, 70 , 1054–1069.

Rustichini, A. (2005). Neuroeconomics: Present and future. Games and Economic Behavior, 52 , 201–212.

Rustichini, A. (2009). Neuroeconomics: What have we found, and what should we search for. Current Opinion in Neurobiology, 19 , 672–677.

Rustichini, A., Dickhaut, J., Ghirardato, P., Smith, K., & Pardo, J. V. (2005). A brain imaging study of the choice procedure. Games and Economic Behavior, 52 , 257–282.

Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2015). Dopaminergic modulation of decision-making and subjective well-being. Journal of Neurosciences, 35 , 9811–9822.

Saarimäki, H., Ejtehadian, L. F., Glerean, E., Jääkeläinen, L., Vuilleumier, P., Sams, M., & Nummenmea, L. (2018). Distributed affective space represents multiple emotion categories across the human brain. Social Cognitive and Affective Neuroscience, 13 (5), 471–482.

Sacks, O. (1995). An anthropologist on Mars. Seven paradoxical tales . Picador.

Sanfey, A. G. (2007). Social decision-making: Insights from game theory and neuroscience. Science, 318 , 598–602.

Sanfey, A. G., Loewenstein, G., McClure, S. M., & Cohen, J. D. (2006). Neuroeconomics: Cross-currents in research on decision-making. Trends in Cognitive Sciences, 10 (3), 108–116.

Sanfey, A. G., & Rilling, J. K. (2011). Neural bases of social decision making. In O. Vartorian & D. R. Mandel (Eds.), Neuroscience of decision making (pp. 223–242). Psychology Press.

Sanfey, A. G., Rilling, J. K., Aronso, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the ultimatum game. Science, 300 , 1755–1758.

Santos, L.R. & Platt, M.L. (2014). Evolutionary anthropological insights into neuroeconomics: What non-human primates can tell us about human decision-making strategies. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 109–122). Amsterdam: Elsevier.

Scherer, K. R. (2005). What are emotions? And how can they be measured?”. Social Science Information, 44 , 695–729.

Schmidt, C. (2008). What neuroeconomics does really mean? Revue D’economie Politique, 118 (1), 7–34.

Schmidt, C., & Livet, P. (2014). Comprendre nos interactions sociales. Une perspective neuroéconomique . Paris: Odile jacob.

Schnell, K., Bluschke, S., Konradt, B., & Walter, H. (2011). Functional relations of empathy and mentalizing: An fMRI study on the neural basis of cognitive empathy. NeuroImage, 54 (2), 1743–1754.

Schultz, W. (2008). Introduction. Neuroeconomics: The promise and the profit. Philosophical Transactions of the Royal Society b: Biological Sciences, 363 , 3767–3769.

Schultz, W. (2009). Midbrain dopamine neurons: A retina of the reward system? In P.W. Glimcher, C.F., Camerer, E. Fehr, & R.A. Poldrack (Eds.), Neuroeconomics. Decision making and the brain (pp. 323–329), Amsterdam: Elsevier.

Schultz, W. (2010). Dopamine signals for reward value and risk: Basic and recent data. Behavioral and Brain Function, 6 , 24.

Schultz, W. (2013). Updating dopamine reward signals. Current Opinion in Neurobiology, 23 , 229–238.

Schultz, W. (2016). Neural reward and decision signals: From theories to data. Physiological Review, 95 , 853–951.

Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275 (5306), 1593–1599.

Schultz, W., Preuschoff, K., Camerer, C. F., Hsu, M., Fiorillo, C. D., Tobler, N. P., & Bossaerts, P. (2008). Explicit neural signals reflecting reward uncertainty. Philosophical Transactions of the Royal Society b: Biological Sciences, 363 , 3801–3811.

Schurz, M., Radua, J., Aichhorn, M., Richlan, F., & Perner, J. (2014). Fractionating theory of mind: A meta-analysis of functional brain imaging studies. Neuroscience and Biobehavioral Reviews, 42C , 9–34.

Schutt, R. K., Seidman, L. J., & Keshavan, M. S. (Eds.). (2015). Social neuroscience: Brain, mind, and society . Harvard University Press.

Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K., & Hoesen, G. W. V. (2001). Prefrontal cortex in humans and apes: A comparative study of area 10. American Journal of Physical Anthropology, 114 , 224–241.

Serra, D. (2016). Neuroeconomie . Economica.

Serra, D. (2017). Economie comportementale . Economica.

Serra, D. (2021). Controversies around neuroeconomics: Empirical, methodological and philosophical issues, Review of Economic Philosophy/ Revue de philosophie économique (to be published).

Serra, D. (2022). La "révolution" expérimentale en économie. Une histoire des courants de recherche qui l'incarnent. Presses Universitaires de la Méditerranée (forthcoming).

Shamay-Tsoory, S. G. (2011a). The neural basis for empathy. The Neuroscientist, 17 (1), 18–24.

Shamay-Tsoory, S.G. (2011b). Empathic processing: Its cognitive and affective dimensions and neuroanatomical basis. In J. Decety J. & W. Ickes (Eds.). The social neuroscience of empathy (chap. 16), MIT Press.

Sherrington, C.S. (1906). The integrative action of the nervous system . New Haven: Yale University Press (re-issued in 1947 with a new foreword by Sherrington).

Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69 (1), 99–118.

Simon, H. A. (1979). Models of thought . Yale University Press.

Singer, T. (2012). The past, present and future of social neuroscience: A European perspective. NeuroImage, 61 , 437–449.

Singer, T., Kiebel, S. J., Winston, J. S., Dolan, R. J., & Frith, C. D. (2004). Brain responses to the acquired moral status of faces. Neuron, 41 , 653–662.

Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E., Dolan, R. J., & Frith, C. D. (2006). Empathic neural responses are modulated by the perceived fairness of others. Nature, 439 , 466–469.

Singer, T. & Tusche, A. (2014). Understanding others: Brain mechanisms of theory of mind and empathy. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 513–532), Amsterdam: Elsevier.

Skinner, B. F. (1953). Science and human behaviour . Macmillan.

Sloman, S. A. (2002). Two systems of reasoning. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 379–398). Cambridge University Press.

Smith, A., Bernheim, B. D., Camerer, C. F., & Rangel, A. (2014). Neural activity reveals preferences without choices. American Economic Journal: Microeconomics, 6 (2), 1–36.

Smith, V. L. (2008). Rationality in economics: Constructivist and ecological forms . Cambridge University Press.

Soltani, A., & Wang, X. J. (2008). From biophysics to cognition: Reward-dependant adaptive choice behaviour. Current Opinion in Neurobiology, 18 , 209–216.

Sperdutti, M., Guionnet, S., Fossati, P., Nadel, J., & J. . (2014). Mirror neuron system and mentalizing system connect during online social interaction. Cognitive Processing, 15 (3), 307–316.

Spunt, R. P., & Adolphs, R. (2014). Validating the why/how contrast for functional MRI studies of theory of mind. NeuroImage, 99 (1), 301–311.

Starmer, C. (2000). Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. Journal of Economic Literature, 39 , 332–338.

Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction . MIT Press.

Talairach, J., & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system – An approach to cerebral imaging . Thieme Medical Publishers.

Taya, F. (2012). Seeking ambiguity: A review on neuroimaging studies on decision making under ambiguity. Recherches Economiques De Louvain, 78 (3), 83–100.

Thaler, R. H. (1985). Mental accounting and consumer choice. Marketing Science, 4 , 199–214.

Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision Making, 12 , 183–206.

Thiele, A., & Bellgrove, M. A. (2018). Neuromodulation of attention. Neuron, 97 , 769–785.

Thorndike, E. L. (1911). Animal intelligence: Experimental studies . Macmillan.

Tobler, P.N. & Weber, E.U. (2014). Valuation for risky and uncertain choices. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 149–172), Amsterdam: Elsevier.

Tomasello, M. (2000). The cultural origin of human cognition . Harvard University Press.

Trope, Y., & Gaunt, R. (2000). Processing alternative explanations of behavior: Correction or integration? Journal of Personality and Social Psychology, 79 (3), 344.

Tversky, A., & Kahneman, D. (1981). The framing of decision and the psychology of choice. Science, 211 , 453–458.

Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. Journal of Business, 59 , 5251–5278.

Van Overwalle, F. (2009). Social cognition and the brain: A meta-analysis. Human Brain Mapping, 30 (3), 829–858.

Van Overwalle, F., & Baetens, K. (2009). Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis. NeuroImage, 48 (3), 564–584.

Vromen, J. (2007). Neuroeconomics as a natural extension of bioeconomics: The shifting scope of standard economic theory. Journal of Bioeconomics, 9 (2), 145–167.

Vromen, J. (2011). Neuroeconomics: Two camps gradually converging. What can economics gain from it? International Review of Economics, 58 , 267–285.

Wake, S. J., & Izuma, K. (2017). A common neural code for social and monetary rewards in the human striatum. Social Cognitive and Affective Sciences, 12 (10), 1558–1564.

Wallis, J.D. & Rushworth, M.E. (2014). Integrating benefits and costs in decision making. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 411–433). Amsterdam: Elsevier.

Wang, X-J. (2014). Neuronal circuit computation of choice. In P.W. Glimcher & E. Fehr (Eds.), Neuroeconomics. Decision making and the brain (2nd ed.) (pp. 435–454). Amsterdam: Elsevier.

Watson, J. (1913). Psychology as the behavioural views it. Psychological Review, 20 (2), 158–177.

Weber, E.H. (1834/1996). E.H. Weber: On the tactile senses (with translation of De Tactu), H.E. Ross & D.J. Murray (trans. and eds.), New York: Experimental Psychology Society.

Whalen, P. J., & Phelps, E. A. (Eds.). (2009). The human amygdala . Guilford.

Wise, R. A. (1982). Neuroleptics and operant behaviour: The anhedonia hypothesis. Behavioral and Brain Sciences, 5 , 39–53.

Young, L., Dodell-Feder, D., & Saxe, R. (2010). What gets the attention of the temporo-parietal junction? An fMRI investigation of attention and theory of mind. Neuropsychologia, 48 , 2658–2664.

Zaghloul, K. A., Blanco, J. A., Weidemann, C. T., McGill, K., Jaggi, J. L., Baltuch, G. H., & Kahana, M. J. (2009). Human substantia nigra neurons encode unexpected financial rewards. Science, 323 , 1496–1499.

Zak, P. J. (2004). Neuroeconomics. Philosophical Transactions of the Royal Society b: Biological Sciences, 359 (1451), 1737–1748.

Zak, P. J. (2011). The physiology of moral sentiments. Journal of Economic Behavior and Organization, 77 , 53–65.

Zaki, J., & Ochsner, K. (2012). The neuroscience of empathy: Progress, pitfalls and promise. Nature Neuroscience, 15 (5), 675–680.

Zhu, L., Mathewson, K. E., & Hsu, M. (2012). Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning. Proceedings of the National Academy of Sciences., 109 (5), 1419–1424.

Zweig, J. (2007). Your money and your brain . Simon & Schulster.

Download references

Acknowledgements

The author would like to thank Sacha Bourgeois-Gironde, Guillaume Herbet, Philippe Mongin, Pierre Livet, Olivier Oullier, Christian Schmidt, Nicolas Vallois, and Marc Willinger for useful comments and conversations over the years related to the topics of this paper. He would also like to thank Thierry Blayac and Guillaume Cheikbossian for extremely useful comments and suggestions on both earlier and current versions of the paper.

Author information

Authors and affiliations.

CEE-M, Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France

Daniel Serra

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Daniel Serra .

Ethics declarations

Conflict of interests.

The author declares that he has no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Serra, D. Decision-making: from neuroscience to neuroeconomics—an overview. Theory Decis 91 , 1–80 (2021). https://doi.org/10.1007/s11238-021-09830-3

Download citation

Accepted : 08 June 2021

Published : 28 June 2021

Issue Date : July 2021

DOI : https://doi.org/10.1007/s11238-021-09830-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Decision-making
• Neuroscience
• Experiments
• Behavioral economics

JEL Classifications

• Find a journal
• Publish with us
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

Decision making articles from across Nature Portfolio

Latest research and reviews.

The case for longtermism: concern for the far future as a catalyst for pro-climate action

• Stylianos Syropoulos
• Kyle Fiore Law
• Liane Young

Dynamics of collective cooperation under personalised strategy updates

Collective cooperation is found across many social and biological systems. Here, the authors find that infrequent hub updates promote the emergence of collective cooperation and develop an algorithm that optimises collective cooperation with update rates.

• Sean P. Cornelius

Experiencing nature leads to healthier food choices

Across five studies of 3,726 participants, walking in nature (study 1) and viewing images of nature (studies 2-5) led to significantly more healthy food choices and fewer unhealthy food choices compared to urban settings.

• Maria Langlois
• Pierre Chandon

Contributions of human cultures to biodiversity and ecosystem conservation

This Review identifies and describes interactions and feedbacks between biodiversity and diversity of Indigenous Peoples and Local Communities, and uses case studies from South America to illustrate the conservation and human benefits that can arise from protecting both biological and cultural diversity.

• Carolina Levis
• Bernardo M. Flores
• Charles R. Clement

Field testing the transferability of behavioural science knowledge on promoting vaccinations

The authors field test the transferability of behavioural science knowledge on promoting COVID-19 booster uptake. Interventions effective in past field work improve uptake, but those deemed effective in surveys measuring intentions or predictions do not.

• Silvia Saccardo
• Hengchen Dai
• Jeffrey Fujimoto

Aligning renewable energy expansion with climate-driven range shifts

The authors conduct a systematic literature review on renewable energy expansion and biodiversity. Comparing renewable energy siting maps with the ranges of two threatened species under future climates, they highlight the potential conflict and need for consideration of climate-change-driven range shifts.

• Uzma Ashraf
• Toni Lyn Morelli
• Rebecca R. Hernandez

News and Comment

Transformative localization to accelerate the 2030 Agenda

To get the Sustainable Development Goals (SDGs) back on track we need to reshape our approaches to implementation, including localization. Localization done differently involves progressing beyond symbolic piecemeal efforts, prioritizing the SDGs with the greatest gains, and pluralizing interpretations and pathways for actions.

• Shirin Malekpour
• Brett Bryan

FAO’s 1.5 °C roadmap for food systems falls short

The first instalment of the FAO food systems roadmap is a key step in identifying pathways to achieve zero hunger without breaching the 1.5 °C climate change threshold. But future instalments should be more methodologically transparent, emphasize the need to reduce animal-sourced food consumption and align with a holistic One Health approach.

• Cleo Verkuijl
• Jan Dutkiewicz
• Matthew Hayek

Judgements of bias vary with observers’ political ideology and targets’ characteristics

Whether conservatives or liberals have higher sensitivity towards underrepresentation depends on the target of the judgement: conservatives are shown to have higher thresholds than liberals for indicating bias against traditionally nondominant groups, whereas liberals have higher bias thresholds regarding dominant groups. However, such relationships weaken when the targets of bias are unknown or ideologically irrelevant to the observer, which emphasizes the context-dependency of such bias judgements.

Emerging local energy communities

• Silvana Lakeman

Adaptation requires attuning to shifting temporal patterns

Communal life is characterized by the shared timing of human and environmental events. Climate change is disrupting these timings, creating mismatches in these coordinated temporal patterns and requiring adaptive governance.

• Scott Bremer
• Nicole Klenk
• Danielle Kwan-Lafond

Unpacking symbolic policy-making for the first Global Stocktake under the Paris Agreement

The first ever Global Stocktake is scheduled to conclude during COP28 and aims to assess progress on climate change mitigation, adaptation, and the means of implementation. However, the Global Stocktake runs the risk of overestimating progress by overlooking the symbolic dimensions of climate change adaptation policy.

• Robbert Biesbroek
• Alexandra Lesnikowski

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Frontiers in Psychology
• Research Topics

Decision-making competence: From decision structuring to choice

Total Downloads

Total Views and Downloads

About this Research Topic

Throughout their lives, people make decisions that affect their health, careers, finances, and overall well-being. Traditionally, research in psychology and decision making has focused on identifying decision biases and their situational causes. Studies have usually taken place with undergraduate ...

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Topic coordinators, recent articles, submission deadlines.

Submission closed.

Participating Journals

Total views.

• Demographics

No records found

total views article views downloads topic views

Top countries

Top referring sites, about frontiers research topics.

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

• Harvard Business School →
• Faculty & Research →

Publications

• Global Research Centers
• Case Development
• Initiatives & Projects
• Research Services
• Seminars & Conferences
• Publications →

Show Results For

• All HBS Web  (115,823)
• Faculty Publications  (58,228)

DecisionMaking →

No results found in faculty publications.

• Were any results found in one of the other content buckets on the left?
• Try removing some search filters.
• Use different search filters.

Articles on Decision making

Displaying 1 - 20 of 133 articles.

Seven reasons more female leaders would be a positive step for the climate

Paola Profeta , Bocconi University

What happens when we outsource boring but important work to AI? Research shows we forget how to do it ourselves

Tapani Rinta-Kahila , The University of Queensland

Training to reduce cognitive bias may improve decision making after all

Anne-Laure Sellier , HEC Paris Business School ; Carey K. Morewedge , Boston University , and Irene Scopelliti , City, University of London

AI could improve your life by removing bottlenecks between what you want and what you get

Bruce Schneier , Harvard Kennedy School

#GirlMaths: a seemingly innocent and fun way to justify expenses that can have serious financial consequences

Janneke Blijlevens , RMIT University ; Angel Zhong , RMIT University , and Lauren Gurrieri , RMIT University

The illusion and implications of ‘just following the science’ COVID-19  messaging

Adèle Cassola , York University, Canada and Patrick Fafard , L’Université d’Ottawa/University of Ottawa

Humans set budgets when facing an uncertain future. So do ants 

Daniele Carlesso , Macquarie University

How should a robot explore the Moon? A simple question shows the limits of current AI systems

Sally Cripps , University of Technology Sydney ; Alex Fischer , Australian National University ; Edward Santow , University of Technology Sydney ; Hadi Mohasel Afshar , University of Technology Sydney , and Nicholas Davis , University of Technology Sydney

Does competition make us less moral? New research says yes, but only a little bit

Ozan Isler , The University of Queensland

We make thousands of unconscious decisions every day. Here’s how your brain copes with that

Gina Cleo , Bond University

Cognitive flexibility: the science of how to be successful in business and at work

Barbara Jacquelyn Sahakian , University of Cambridge and Georgios Christopoulos , Nanyang Technological University

Don’t bet with ChatGPT – study shows language AIs often make irrational decisions

Mayank Kejriwal , University of Southern California

Against baseball’s new pitch clock

Alva Noë , University of California, Berkeley

How your brain decides what to think

Valerie van Mulukom , Coventry University

Shake and divide: the cocktail formula for global consensus

M. Ángeles Serrano , Universitat de Barcelona

Consensus decision-making is surprisingly effective in both communities and workplaces

Mel Rutherford , McMaster University

‘What shall we have for dinner?’ Choice overload is a real problem, but these tips will make your life easier

Janneke Blijlevens , RMIT University

COVID inquiry: the UK government’s pandemic response was often not ‘guided by the science’ – yet now scientists are under fire

Christian Yates , University of Bath

Dog owners take more risks, cat owners are more cautious – new research examines how people conform to their pets’ stereotypical traits

Lei Jia , Kent State University

How your brain will help you save during the cost of living crisis

Cathrine Jansson-Boyd , Anglia Ruskin University

Related Topics

• Artificial intelligence (AI)
• Cognitive bias
• Decision making process

Top contributors

Associate Professor of Marketing, University of Technology Sydney

Professor of Cognitive Psychology and Director of the UNSW Institute for Climate Risk and Response, UNSW Sydney

Assistant Professor, School of Rehabilitation Therapy, Queen's University, Ontario

Senior Lecturer in Marketing, RMIT University

Professor of Clinical Neuropsychology, University of Cambridge

PhD Candidate in Economics, Australian National University

Adjunct Professor of Sport Studies, UC-RISE, University of Canberra

Associate Researcher, University of Tasmania

Professor in Evolutionary Biology (jointly appointed with South Australian Museum), Flinders University

Director Sydney Environment Institute and Professor of Environmental Politics, University of Sydney

Associate Professor and Head of Decision Neuroscience Laboratory, The University of Melbourne

Vice-Chancellor, La Trobe University

Associate Professor of Earthquake Science, The University of Melbourne

Associate Professor in Law, Deakin University

Senior Research Fellow, Melbourne School of Psychological Sciences, The University of Melbourne

• X (Twitter)
• Unfollow topic Follow topic

• Skip to Guides Search
• Skip to breadcrumb
• Skip to main content
• Skip to footer
• Skip to chat link
• Report accessibility issues and get help
• Go to Penn Libraries Home
• Go to Franklin catalog

Critical Writing Program: Decision Making - Spring 2024: Researching the White Paper

• Getting started
• News and Opinion Sites
• Academic Sources
• Grey Literature
• Substantive News Sources
• What to Do When You Are Stuck
• Understanding a citation
• Examples of Quotation
• Examples of Paraphrase
• Chicago Manual of Style: Citing Images
• Researching the Op-Ed
• Researching Prospective Employers
• Resume Resources
• Cover Letter Resources

Research the White Paper

Researching the White Paper:

The process of researching and composing a white paper shares some similarities with the kind of research and writing one does for a high school or college research paper. What’s important for writers of white papers to grasp, however, is how much this genre differs from a research paper.  First, the author of a white paper already recognizes that there is a problem to be solved, a decision to be made, and the job of the author is to provide readers with substantive information to help them make some kind of decision--which may include a decision to do more research because major gaps remain. 

Thus, a white paper author would not “brainstorm” a topic. Instead, the white paper author would get busy figuring out how the problem is defined by those who are experiencing it as a problem. Typically that research begins in popular culture--social media, surveys, interviews, newspapers. Once the author has a handle on how the problem is being defined and experienced, its history and its impact, what people in the trenches believe might be the best or worst ways of addressing it, the author then will turn to academic scholarship as well as “grey” literature (more about that later).  Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position.  Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting, and overlapping perspectives. When people research out of a genuine desire to understand and solve a problem, they listen to every source that may offer helpful information. They will thus have to do much more analysis, synthesis, and sorting of that information, which will often not fall neatly into a “pro” or “con” camp:  Solution A may, for example, solve one part of the problem but exacerbate another part of the problem. Solution C may sound like what everyone wants, but what if it’s built on a set of data that have been criticized by another reliable source?  And so it goes. 

For example, if you are trying to write a white paper on the opioid crisis, you may focus on the value of  providing free, sterilized needles--which do indeed reduce disease, and also provide an opportunity for the health care provider distributing them to offer addiction treatment to the user. However, the free needles are sometimes discarded on the ground, posing a danger to others; or they may be shared; or they may encourage more drug usage. All of those things can be true at once; a reader will want to know about all of these considerations in order to make an informed decision. That is the challenging job of the white paper author.     
 The research you do for your white paper will require that you identify a specific problem, seek popular culture sources to help define the problem, its history, its significance and impact for people affected by it.  You will then delve into academic and grey literature to learn about the way scholars and others with professional expertise answer these same questions. In this way, you will create creating a layered, complex portrait that provides readers with a substantive exploration useful for deliberating and decision-making. You will also likely need to find or create images, including tables, figures, illustrations or photographs, and you will document all of your sources. 

Business & Research Support Services Librarian

Connect to a Librarian Live Chat or "Ask a Question"

• Librarians staff live chat from 9-5 Monday through Friday . You can also text to chat: 215-543-7674
• You can submit a question 24 hours a day and we aim to respond within 24 hours 
• You can click the "Schedule Appointment" button above in librarian's profile box (to the left), to schedule a consultation with her in person or by video conference.  
• You can also make an appointment with a  Librarian by subject specialization . 
• Connect by email with a subject librarian

Find more easy contacts at our Quick Start Guide

• Next: Getting started >>
• Last Updated: Feb 15, 2024 12:28 PM
• URL: https://guides.library.upenn.edu/spring2024/decision-making

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

50+ Research Topics for Psychology Papers

How to Find Psychology Research Topics for Your Student Paper

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

• Specific Branches of Psychology
• Topics Involving a Disorder or Type of Therapy
• Human Cognition
• Human Development
• Critique of Publications
• Famous Experiments
• Historical Figures
• Specific Careers
• Case Studies
• Literature Reviews
• Your Own Study/Experiment

Are you searching for a great topic for your psychology paper ? Sometimes it seems like coming up with topics of psychology research is more challenging than the actual research and writing. Fortunately, there are plenty of great places to find inspiration and the following list contains just a few ideas to help get you started.

Finding a solid topic is one of the most important steps when writing any type of paper. It can be particularly important when you are writing a psychology research paper or essay. Psychology is such a broad topic, so you want to find a topic that allows you to adequately cover the subject without becoming overwhelmed with information.

I can always tell when a student really cares about the topic they chose; it comes through in the writing. My advice is to choose a topic that genuinely interests you, so you’ll be more motivated to do thorough research.

In some cases, such as in a general psychology class, you might have the option to select any topic from within psychology's broad reach. Other instances, such as in an  abnormal psychology  course, might require you to write your paper on a specific subject such as a psychological disorder.

As you begin your search for a topic for your psychology paper, it is first important to consider the guidelines established by your instructor.

Research Topics Within Specific Branches of Psychology

The key to selecting a good topic for your psychology paper is to select something that is narrow enough to allow you to really focus on the subject, but not so narrow that it is difficult to find sources or information to write about.

One approach is to narrow your focus down to a subject within a specific branch of psychology. For example, you might start by deciding that you want to write a paper on some sort of social psychology topic. Next, you might narrow your focus down to how persuasion can be used to influence behavior .

Other social psychology topics you might consider include:

• Prejudice and discrimination (i.e., homophobia, sexism, racism)
• Social cognition
• Person perception
• Social control and cults
• Persuasion, propaganda, and marketing
• Attraction, romance, and love
• Nonverbal communication
• Prosocial behavior

Psychology Research Topics Involving a Disorder or Type of Therapy

Exploring a psychological disorder or a specific treatment modality can also be a good topic for a psychology paper. Some potential abnormal psychology topics include specific psychological disorders or particular treatment modalities, including:

• Eating disorders
• Borderline personality disorder
• Seasonal affective disorder
• Schizophrenia
• Antisocial personality disorder
• Profile a  type of therapy  (i.e., cognitive-behavioral therapy, group therapy, psychoanalytic therapy)

Topics of Psychology Research Related to Human Cognition

Some of the possible topics you might explore in this area include thinking, language, intelligence, and decision-making. Other ideas might include:

• False memories
• Speech disorders
• Problem-solving

Topics of Psychology Research Related to Human Development

In this area, you might opt to focus on issues pertinent to  early childhood  such as language development, social learning, or childhood attachment or you might instead opt to concentrate on issues that affect older adults such as dementia or Alzheimer's disease.

Some other topics you might consider include:

• Language acquisition
• Media violence and children
• Learning disabilities
• Gender roles
• Child abuse
• Prenatal development
• Parenting styles
• Aspects of the aging process

Do a Critique of Publications Involving Psychology Research Topics

One option is to consider writing a critique paper of a published psychology book or academic journal article. For example, you might write a critical analysis of Sigmund Freud's Interpretation of Dreams or you might evaluate a more recent book such as Philip Zimbardo's  The Lucifer Effect: Understanding How Good People Turn Evil .

Professional and academic journals are also great places to find materials for a critique paper. Browse through the collection at your university library to find titles devoted to the subject that you are most interested in, then look through recent articles until you find one that grabs your attention.

Topics of Psychology Research Related to Famous Experiments

There have been many fascinating and groundbreaking experiments throughout the history of psychology, providing ample material for students looking for an interesting term paper topic. In your paper, you might choose to summarize the experiment, analyze the ethics of the research, or evaluate the implications of the study. Possible experiments that you might consider include:

• The Milgram Obedience Experiment
• The Stanford Prison Experiment
• The Little Albert Experiment
• Pavlov's Conditioning Experiments
• The Asch Conformity Experiment
• Harlow's Rhesus Monkey Experiments

Topics of Psychology Research About Historical Figures

One of the simplest ways to find a great topic is to choose an interesting person in the  history of psychology  and write a paper about them. Your paper might focus on many different elements of the individual's life, such as their biography, professional history, theories, or influence on psychology.

While this type of paper may be historical in nature, there is no need for this assignment to be dry or boring. Psychology is full of fascinating figures rife with intriguing stories and anecdotes. Consider such famous individuals as Sigmund Freud, B.F. Skinner, Harry Harlow, or one of the many other  eminent psychologists .

Psychology Research Topics About a Specific Career

​Another possible topic, depending on the course in which you are enrolled, is to write about specific career paths within the  field of psychology . This type of paper is especially appropriate if you are exploring different subtopics or considering which area interests you the most.

In your paper, you might opt to explore the typical duties of a psychologist, how much people working in these fields typically earn, and the different employment options that are available.

Topics of Psychology Research Involving Case Studies

One potentially interesting idea is to write a  psychology case study  of a particular individual or group of people. In this type of paper, you will provide an in-depth analysis of your subject, including a thorough biography.

Generally, you will also assess the person, often using a major psychological theory such as  Piaget's stages of cognitive development  or  Erikson's eight-stage theory of human development . It is also important to note that your paper doesn't necessarily have to be about someone you know personally.

In fact, many professors encourage students to write case studies on historical figures or fictional characters from books, television programs, or films.

Psychology Research Topics Involving Literature Reviews

Another possibility that would work well for a number of psychology courses is to do a literature review of a specific topic within psychology. A literature review involves finding a variety of sources on a particular subject, then summarizing and reporting on what these sources have to say about the topic.

Literature reviews are generally found in the  introduction  of journal articles and other  psychology papers , but this type of analysis also works well for a full-scale psychology term paper.

Topics of Psychology Research Based on Your Own Study or Experiment

Many psychology courses require students to design an actual psychological study or perform some type of experiment. In some cases, students simply devise the study and then imagine the possible results that might occur. In other situations, you may actually have the opportunity to collect data, analyze your findings, and write up your results.

Finding a topic for your study can be difficult, but there are plenty of great ways to come up with intriguing ideas. Start by considering your own interests as well as subjects you have studied in the past.

Online sources, newspaper articles, books , journal articles, and even your own class textbook are all great places to start searching for topics for your experiments and psychology term papers. Before you begin, learn more about  how to conduct a psychology experiment .

What This Means For You

After looking at this brief list of possible topics for psychology papers, it is easy to see that psychology is a very broad and diverse subject. While this variety makes it possible to find a topic that really catches your interest, it can sometimes make it very difficult for some students to select a good topic.

If you are still stumped by your assignment, ask your instructor for suggestions and consider a few from this list for inspiration.

• Hockenbury, SE & Nolan, SA. Psychology. New York: Worth Publishers; 2014.
• Santrock, JW. A Topical Approach to Lifespan Development. New York: McGraw-Hill Education; 2016.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Choose Your Test

Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

Are you also learning about dynamic equilibrium in your science class? We break this sometimes tricky concept down so it's easy to understand in our complete guide to dynamic equilibrium .

Thinking about becoming a nurse practitioner? Nurse practitioners have one of the fastest growing careers in the country, and we have all the information you need to know about what to expect from nurse practitioner school .

Want to know the fastest and easiest ways to convert between Fahrenheit and Celsius? We've got you covered! Check out our guide to the best ways to convert Celsius to Fahrenheit (or vice versa).

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

Student and Parent Forum

Our new student and parent forum, at ExpertHub.PrepScholar.com , allow you to interact with your peers and the PrepScholar staff. See how other students and parents are navigating high school, college, and the college admissions process. Ask questions; get answers.

Ask a Question Below

Have any questions about this article or other topics? Ask below and we'll reply!

Improve With Our Famous Guides

• For All Students

The 5 Strategies You Must Be Using to Improve 160+ SAT Points

How to Get a Perfect 1600, by a Perfect Scorer

Series: How to Get 800 on Each SAT Section:

Score 800 on SAT Math

Score 800 on SAT Reading

Score 800 on SAT Writing

Series: How to Get to 600 on Each SAT Section:

Score 600 on SAT Math

Score 600 on SAT Reading

Score 600 on SAT Writing

Free Complete Official SAT Practice Tests

What SAT Target Score Should You Be Aiming For?

15 Strategies to Improve Your SAT Essay

The 5 Strategies You Must Be Using to Improve 4+ ACT Points

How to Get a Perfect 36 ACT, by a Perfect Scorer

Series: How to Get 36 on Each ACT Section:

36 on ACT English

36 on ACT Math

36 on ACT Reading

36 on ACT Science

Series: How to Get to 24 on Each ACT Section:

24 on ACT English

24 on ACT Math

24 on ACT Reading

24 on ACT Science

What ACT target score should you be aiming for?

ACT Vocabulary You Must Know

ACT Writing: 15 Tips to Raise Your Essay Score

How to Get Into Harvard and the Ivy League

How to Get a Perfect 4.0 GPA

How to Write an Amazing College Essay

What Exactly Are Colleges Looking For?

Is the ACT easier than the SAT? A Comprehensive Guide

Should you retake your SAT or ACT?

When should you take the SAT or ACT?

Stay Informed

Get the latest articles and test prep tips!

Looking for Graduate School Test Prep?

Check out our top-rated graduate blogs here:

GRE Online Prep Blog

GMAT Online Prep Blog

TOEFL Online Prep Blog

Holly R. "I am absolutely overjoyed and cannot thank you enough for helping me!”

Behavioral Economics Research Paper Topics

This list of behavioral economics research paper topics is intended to provide students and researchers with a comprehensive guide for selecting research topics in the field of behavioral economics. The importance of choosing a pertinent and engaging topic for your research paper is paramount, and this guide is designed to facilitate this crucial process. We offer an extensive list of topics, divided into ten categories, each with ten unique ideas. Additionally, we provide expert advice on how to select a topic from this multitude and how to write a compelling research paper in behavioral economics. Lastly, we introduce iResearchNet’s professional writing services, tailored to support your academic journey and ensure success in your research endeavors.

100 Behavioral Economics Research Paper Topics

Choosing a research paper topic is a critical step in the research process. The topic you select will guide your study and influence the complexity and relevance of your work. In the field of behavioral economics, there are numerous intriguing topics that can be explored. To assist you in this process, we have compiled a comprehensive list of behavioral economics research paper topics. These topics are divided into ten categories, each offering a different perspective on behavioral economics.

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% off with 24start discount code.

• The role of heuristics in decision-making
• Prospect theory and its applications
• Time inconsistency and hyperbolic discounting
• The endowment effect and loss aversion
• Mental accounting and its implications
• The role of anchoring in economic decisions
• Framing effect in marketing strategies
• The paradox of choice: More is less
• Nudge theory in public policy
• Trust game in behavioral economics
• The role of emotions in economic decisions
• Overconfidence bias in financial markets
• Decision-making under uncertainty
• The impact of social norms on economic behavior
• The role of fairness and inequality aversion in economic decisions
• The effect of cognitive dissonance on consumer behavior
• The impact of stress on economic decisions
• The role of regret and disappointment in economic decisions
• The effect of peer influence on economic behavior
• The role of culture in economic decision-making
• The use of nudges in public policy
• The impact of behavioral economics on tax policy
• The role of behavioral economics in health policy
• Behavioral insights in environmental policy
• The influence of behavioral economics on education policy
• The role of behavioral economics in social welfare policy
• The impact of behavioral economics on retirement policy
• Behavioral economics and traffic policy
• The role of behavioral economics in energy policy
• The influence of behavioral economics on housing policy
• The role of behavioral biases in personal financial decisions
• The impact of financial literacy on economic behavior
• Behavioral economics and retirement savings
• The role of behavioral economics in credit card debt
• The impact of behavioral economics on investment decisions
• Behavioral economics and insurance decisions
• The role of behavioral economics in household budgeting
• The impact of behavioral economics on mortgage decisions
• Behavioral economics and financial planning
• The role of behavioral economics in financial education
• The role of behavioral economics in pricing strategies
• Behavioral economics and consumer choice
• The impact of behavioral economics on advertising
• Behavioral economics and product design
• The role of behavioral economics in sales strategies
• Behavioral economics and customer loyalty
• The impact of behavioral economics on branding
• Behavioral economics and e-commerce
• The role of behavioral economics in business negotiations
• Behavioral economics and corporate decision-making
• The role of behavioral economics in health behaviors
• Behavioral economics and healthcare decisions
• The impact of behavioral economics on health insurance choices
• Behavioral economics and preventive health care
• The role of behavioral economics in obesity and diet choices
• Behavioral economics and smoking cessation
• The impact of behavioral economics on medication adherence
• Behavioral economics and mental health
• The role of behavioral economics in exercise and physical activity
• Behavioral economics and alcohol consumption
• The role of behavioral economics in promoting sustainable behavior
• Behavioral economics and energy conservation
• The impact of behavioral economics on recycling behavior
• Behavioral economics and water conservation
• The role of behavioral economics in climate change mitigation
• Behavioral economics and sustainable transportation
• The impact of behavioral economics on sustainable consumption
• Behavioral economics and green investments
• The role of behavioral economics in biodiversity conservation
• Behavioral economics and waste reduction
• The role of behavioral economics in digital marketing
• Behavioral economics and online shopping behavior
• The impact of behavioral economics on social media usage
• Behavioral economics and cybersecurity
• The role of behavioral economics in technology adoption
• Behavioral economics and online privacy decisions
• The impact of behavioral economics on mobile app usage
• Behavioral economics and virtual reality
• The role of behavioral economics in video game design
• Behavioral economics and artificial intelligence
• The role of behavioral economics in educational choices
• Behavioral economics and student motivation
• The impact of behavioral economics on study habits
• Behavioral economics and school attendance
• The role of behavioral economics in academic performance
• Behavioral economics and college enrollment decisions
• The impact of behavioral economics on student loan decisions
• Behavioral economics and teacher incentives
• The role of behavioral economics in educational policy
• Behavioral economics and lifelong learning
• The role of neuroscience in behavioral economics
• Behavioral economics and inequality
• The impact of behavioral economics on economic modeling
• Behavioral economics and big data
• The role of behavioral economics in addressing social issues
• Behavioral economics and virtual currencies
• The impact of behavioral economics on behavioral change interventions
• Behavioral economics and the sharing economy
• The role of behavioral economics in understanding happiness and well-being
• Behavioral economics and the future of work

This comprehensive list of behavioral economics research paper topics provides a wide range of options for your research. Each category offers unique insights into the different aspects of behavioral economics, from fundamental concepts to future directions. Remember, the best research paper topic is one that not only interests you but also has sufficient resources for you to explore. We hope this list inspires you and aids you in your journey to write a compelling research paper in behavioral economics.

Introduction to Behavioral Economics

Behavioral economics is an intriguing and dynamic field that bridges the gap between traditional economic theory and actual human behavior. It integrates insights from psychology, judgment, and decision-making into economic analysis, providing a more accurate and nuanced understanding of human behavior.

Traditional economic theory often assumes that individuals are rational agents who make decisions based on maximizing their utility. However, behavioral economics challenges this assumption, recognizing that individuals often act irrationally due to various cognitive biases and heuristics. These deviations from rationality can significantly impact economic decisions and outcomes, making behavioral economics a critical field of study.

Research papers in behavioral economics allow students to delve deeper into specific areas of interest, contributing to their personal knowledge and the broader academic community. These papers can explore a wide range of topics, from understanding the role of cognitive biases in financial decision-making to examining the impact of behavioral interventions on public policy.

The importance of behavioral economics extends beyond academia. It has real-world implications in various sectors, including policy-making, business, finance, and healthcare. By understanding the psychological underpinnings of economic decisions, we can design better products, policies, and interventions that align with actual human behavior.

How to Choose a Behavioral Economics Topic

Choosing a research topic is a critical step in the research process. The topic you select will guide your study, influence the complexity and relevance of your work, and determine how engaged you are throughout the process. In the field of behavioral economics, there are numerous intriguing topics that can be explored. Here are some expert tips to assist you in this process:

• Understanding Your Interests: The first step in choosing a research topic is to understand your interests. What areas of behavioral economics fascinate you the most? Are you interested in how behavioral economics influences policy making, or are you more intrigued by its role in personal finance or marketing? Reflecting on these questions can help you narrow down your options and choose a topic that truly engages you. Remember, research is a time-consuming process, and your interest in the topic will keep you motivated.
• Evaluating the Scope of the Topic: Once you have identified your areas of interest, the next step is to evaluate the scope of potential topics. A good research topic should be neither too broad nor too narrow. If it’s too broad, you may struggle to cover all aspects of the topic effectively. If it’s too narrow, you may have difficulty finding enough information to support your research. Try to choose a topic that is specific enough to be manageable but broad enough to have sufficient resources.
• Assessing Available Resources and Data: Before finalizing a topic, it’s important to assess the available resources and data. Are there enough academic sources, such as books, journal articles, and reports, that you can use for your research? Is there accessible data that you can analyze if your research requires it? A preliminary review of literature and data can save you from choosing a topic with limited resources.
• Considering the Relevance and Applicability of the Topic: Another important factor to consider is the relevance and applicability of the topic. Is the topic relevant to current issues in behavioral economics? Can the findings of your research be applied in real-world settings? Choosing a relevant and applicable topic can increase the impact of your research and make it more interesting for your audience.
• Seeking Advice: Don’t hesitate to seek advice from your professors, peers, or other experts in the field. They can provide valuable insights, suggest resources, and help you refine your topic. Discussing your ideas with others can also help you see different perspectives and identify potential issues that you may not have considered.
• Flexibility: Finally, be flexible. Research is a dynamic process, and it’s okay to modify your topic as you delve deeper into your study. You may discover new aspects of the topic that are more interesting or find that some aspects are too challenging to explore due to constraints. Being flexible allows you to adapt your research to these changes and ensure that your study is both feasible and engaging.

Remember, choosing a research topic is not a decision to be taken lightly. It requires careful consideration and planning. However, with these expert tips, you can navigate this process more effectively and choose a behavioral economics research paper topic that not only meets your academic requirements but also fuels your passion for learning.

How to Write a Behavioral Economics Research Paper

Writing a research paper in behavioral economics, like any other academic paper, requires careful planning, thorough research, and meticulous writing. Here are some expert tips to guide you through this process:

• Understanding the Structure of a Research Paper: A typical research paper includes an introduction, literature review, methodology, results, discussion, and conclusion. The introduction presents your research question and its significance. The literature review provides an overview of existing research related to your topic. The methodology explains how you conducted your research. The results section presents your findings, and the discussion interprets these findings in the context of your research question. Finally, the conclusion summarizes your research and suggests areas for future research.
• Developing a Strong Thesis Statement: Your thesis statement is the central argument of your research paper. It should be clear, concise, and debatable. A strong thesis statement guides your research and helps your readers understand the purpose of your paper.
• Conducting Thorough Research: Before you start writing, conduct a thorough review of the literature related to your topic. This will help you understand the current state of research in your area, identify gaps in the literature, and position your research within this context. Use academic databases to find relevant books, journal articles, and other resources. Remember to evaluate the credibility of your sources and take detailed notes to help you when writing.
• Writing and Revising Drafts: Start writing your research paper by creating an outline based on the structure of a research paper. This will help you organize your thoughts and ensure that you cover all necessary sections. Write a first draft without worrying too much about perfection. Focus on getting your ideas down first. Then, revise your draft to improve clarity, coherence, and argumentation. Make sure each paragraph has a clear topic sentence and supports your thesis statement.
• Proper Citation and Avoiding Plagiarism: Always cite your sources properly to give credit to the authors whose work you are building upon and to avoid plagiarism. Familiarize yourself with the citation style required by your institution or discipline, such as APA, MLA, Chicago/Turabian, or Harvard. There are many citation tools available online that can help you with this.
• Seeking Feedback: Don’t hesitate to seek feedback on your drafts from your professors, peers, or writing centers at your institution. They can provide valuable insights and help you improve your paper.
• Proofreading: Finally, proofread your paper to check for any grammatical errors, typos, or inconsistencies in formatting. A well-written, error-free paper makes a good impression on your readers and enhances the credibility of your research.

Remember, writing a research paper is a process that requires time, effort, and patience. Don’t rush through it. Take your time to understand your topic, conduct thorough research, and write carefully. With these expert tips, you can write a compelling behavioral economics research paper that contributes to your academic success and the broader field of behavioral economics.

iResearchNet’s Writing Services

In the academic journey, students often encounter challenges that may hinder their ability to produce high-quality research papers. Whether it’s a lack of time, limited understanding of the topic, or difficulties in writing, these challenges can make the process stressful and overwhelming. This is where iResearchNet’s professional writing services come in. We are committed to supporting students in their academic journey by providing top-notch writing services tailored to their unique needs.

• Expert Degree-Holding Writers: At iResearchNet, we understand the importance of quality in academic writing. That’s why we have a team of expert writers who hold degrees in various fields, including economics. Our writers are not only knowledgeable in their respective fields but also experienced in academic writing. They understand the nuances of writing research papers and are adept at producing well-structured, coherent, and insightful papers.
• Custom Written Works: We believe that every research paper is unique and should be treated as such. Our writers work closely with you to understand your specific requirements and expectations. They then craft a custom research paper that meets these requirements and reflects your understanding and perspective.
• In-Depth Research: Good research is the backbone of a compelling research paper. Our writers conduct thorough research using reliable and relevant sources to ensure that your paper is informative and credible. They are skilled at analyzing and synthesizing information, presenting complex ideas clearly, and developing strong arguments.
• Custom Formatting: Formatting is an essential aspect of academic writing that contributes to the readability and professionalism of your paper. Our writers are familiar with various formatting styles, including APA, MLA, Chicago/Turabian, and Harvard, and can format your paper according to your preferred style.
• Top Quality: Quality is at the heart of our services. We strive to deliver research papers that are not only well-written and well-researched but also original and plagiarism-free. Our writers adhere to high writing standards, and our quality assurance team reviews each paper to ensure it meets these standards.
• Customized Solutions: We understand that each student has unique needs and circumstances. Whether you need a research paper on a complex behavioral economics topic, assistance with a specific section of your paper, or editing and proofreading services, we can provide a solution that fits your needs.
• Flexible Pricing: We believe that professional writing services should be accessible to all students. That’s why we offer flexible pricing options that cater to different budgets. We are transparent about our pricing, and there are no hidden charges.
• Short Deadlines up to 3 hours: We understand that time is of the essence when it comes to academic assignments. Whether you need a research paper in a week or a few hours, our writers are up to the task. They are skilled at working under pressure and can deliver high-quality papers within short deadlines.
• Timely Delivery: We respect your deadlines and are committed to delivering your paper on time. Our writers start working on your paper as soon as your order is confirmed, and we keep you updated on the progress of your paper.
• 24/7 Support: We believe in providing continuous support to our clients. Our customer support team is available 24/7 to answer your questions, address your concerns, and assist you with your order.
• Absolute Privacy: We respect your privacy and are committed to protecting your personal and financial information. We have robust privacy policies and security measures in place to ensure that your information is safe.
• Easy Order Tracking: We provide an easy and transparent order tracking system that allows you to monitor the progress of your paper and communicate with your writer.
• Money Back Guarantee: Your satisfaction is our top priority. If you are not satisfied with our service, we offer a money-back guarantee.

At iResearchNet, we are committed to helping you succeed in your academic journey. We understand the challenges of writing a research paper and are here to support you every step of the way. Whether you need help choosing a topic, conducting research, writing your paper, or editing and proofreading your work, our expert writers are ready to assist you. With our professional writing services, you can focus on learning and leave the stress of writing to us. So why wait? Order a custom economics research paper from iResearchNet today and experience the difference.

Secure Your Academic Success with iResearchNet Today!

Embarking on a research paper journey can be a daunting task, especially when it comes to complex fields like behavioral economics. But remember, you don’t have to do it alone. iResearchNet is here to provide you with the support you need to produce a high-quality, insightful, and impactful research paper.

Our team of expert degree-holding writers is ready to assist you in creating a custom-written research paper that not only meets but exceeds academic standards. Whether you’re struggling with topic selection, research, writing, or formatting, we’ve got you covered. Our comprehensive services are designed to cater to your unique needs and ensure your academic success.

Don’t let the stress of writing a research paper hinder your learning experience. Take advantage of our professional writing services and focus on what truly matters – your learning and growth. With iResearchNet, you can be confident that you’re submitting a top-quality research paper that reflects your understanding and hard work.

So, are you ready to take the leap? Order a custom economics research paper on any topic from iResearchNet today. Let us help you navigate your academic journey and secure your success. Remember, your academic achievement is our top priority, and we’re committed to helping you reach your goals. Order now and experience the iResearchNet difference!

ORDER HIGH QUALITY CUSTOM PAPER

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Make Decisions with a VC Mindset

• Ilya A. Strebulaev

Venture capitalists’ unique approach to investment and innovation has played a pivotal role in launching one-fifth of the largest U.S. public companies. And three-quarters of the largest U.S. companies founded in the past 50 years would not have existed or achieved their current scale without VC support.

The question is, Why? What makes venture firms so good at finding start-ups that go on to achieve tremendous success? What skills do they have that experienced, networked, and powerful large corporations lack?

The authors’ research reveals that the venture mindset is characterized by several principles: the individual over the group, disagreement over consensus, exceptions over dogma, and agility over bureaucracy. This article offers guidance to traditional firms in using the VC mindset to spur innovation.

The key is to embrace risk, disagreement, and agility.

Idea in Brief

The opportunity.

Venture capitalists’ unique approach to investment and innovation has played a pivotal role in launching one-fifth of the largest U.S. public companies, demonstrating the power of the venture mindset.

The Challenge

Traditional companies often struggle to replicate the success of venture firms because of their aversion to risk and failure and their preference for consensus and stability.

The Solution

When faced with market changes or disruptive technology, big companies should adopt the venture mindset, prioritizing the individual over the group, disagreement over consensus, exceptions over dogma, and agility over bureaucracy.

Venture investors are the hidden hand behind the most innovative companies surrounding us. According to research conducted by one of us (Ilya), venture capitalists were causally responsible for the launch of one-fifth of the 300 largest U.S. public companies in existence today. They have played an essential role in unlocking the power of the internet, the mobile revolution, and now artificial intelligence in all its forms. Apple, Google, Moderna, Netflix, Airbnb, OpenAI, Salesforce, Tesla, Uber, and Zoom—these firms disrupted entire industries despite initially having fewer resources and less support and experience than their mature, successful, cash-rich competitors. All these businesses could theoretically have emerged from within an established company—but they didn’t. Instead, they were financed and shaped by VCs. Indeed, we estimate that three-quarters of the largest U.S. companies founded in the past 50 years would not have existed or achieved their current scale without VC support.

• IS Ilya A. Strebulaev is the David S. Lobel Professor of Private Equity and a professor of finance at the Stanford Graduate School of Business. He is also the founder of the Stanford GSB Venture Capital Initiative and a research associate at the National Bureau of Economic Research.
• AD Alex Dang is a venture builder and a digital strategy adviser. He was a partner at McKinsey and EY and launched numerous businesses at Amazon.

Partner Center

• Open access
• Published: 18 April 2024

Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

• James Shaw 1 , 13 ,
• Joseph Ali 2 , 3 ,
• Caesar A. Atuire 4 , 5 ,
• Phaik Yeong Cheah 6 ,
• Armando Guio Español 7 ,
• Judy Wawira Gichoya 8 ,
• Adrienne Hunt 9 ,
• Daudi Jjingo 10 ,
• Katherine Littler 9 ,
• Daniela Paolotti 11 &
• Effy Vayena 12  

BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

1065 Accesses

6 Altmetric

Metrics details

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Peer Review reports

Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

Clark P, Kim J, Aphinyanaphongs Y, Marketing, Food US. Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical devices: a systematic review. JAMA Netw Open. 2023;6(7):e2321792–2321792.

Article   Google Scholar  

Potnis KC, Ross JS, Aneja S, Gross CP, Richman IB. Artificial intelligence in breast cancer screening: evaluation of FDA device regulation and future recommendations. JAMA Intern Med. 2022;182(12):1306–12.

Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: a systematic review. Soc Sci Med. 2022;296:114782.

Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194.

Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med. 2023;6(1):120.

Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1(9):389–99.

Minssen T, Vayena E, Cohen IG. The challenges for Regulating Medical Use of ChatGPT and other large Language models. JAMA. 2023.

Ho CWL, Malpani R. Scaling up the research ethics framework for healthcare machine learning as global health ethics and governance. Am J Bioeth. 2022;22(5):36–8.

Yeung K. Recommendation of the council on artificial intelligence (OECD). Int Leg Mater. 2020;59(1):27–34.

Maddox TM, Rumsfeld JS, Payne PR. Questions for artificial intelligence in health care. JAMA. 2019;321(1):31–2.

Dzau VJ, Balatbat CA, Ellaissi WF. Revisiting academic health sciences systems a decade later: discovery to health to population to society. Lancet. 2021;398(10318):2300–4.

Ferretti A, Ienca M, Sheehan M, Blasimme A, Dove ES, Farsides B, et al. Ethics review of big data research: what should stay and what should be reformed? BMC Med Ethics. 2021;22(1):1–13.

Rahimzadeh V, Serpico K, Gelinas L. Institutional review boards need new skills to review data sharing and management plans. Nat Med. 2023;1–3.

Kling S, Singh S, Burgess TL, Nair G. The role of an ethics advisory committee in data science research in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–3.

Google Scholar  

Cengiz N, Kabanda SM, Esterhuizen TM, Moodley K. Exploring perspectives of research ethics committee members on the governance of big data in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–9.

Doerr M, Meeder S. Big health data research and group harm: the scope of IRB review. Ethics Hum Res. 2022;44(4):34–8.

Ballantyne A, Stewart C. Big data and public-private partnerships in healthcare and research: the application of an ethics framework for big data in health and research. Asian Bioeth Rev. 2019;11(3):315–26.

Samuel G, Chubb J, Derrick G. Boundaries between research ethics and ethical research use in artificial intelligence health research. J Empir Res Hum Res Ethics. 2021;16(3):325–37.

Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):1–17.

Teixeira da Silva JA. Handling ethics dumping and neo-colonial research: from the laboratory to the academic literature. J Bioethical Inq. 2022;19(3):433–43.

Ferryman K. The dangers of data colonialism in precision public health. Glob Policy. 2021;12:90–2.

Couldry N, Mejias UA. Data colonialism: rethinking big data’s relation to the contemporary subject. Telev New Media. 2019;20(4):336–49.

Organization WH. Ethics and governance of artificial intelligence for health: WHO guidance. 2021.

Metcalf J, Moss E. Owning ethics: corporate logics, silicon valley, and the institutionalization of ethics. Soc Res Int Q. 2019;86(2):449–76.

Data Protection Act - OFFICE OF THE DATA PROTECTION COMMISSIONER KENYA [Internet]. 2021 [cited 2023 Sep 30]. https://www.odpc.go.ke/dpa-act/ .

Sharon T, Lucivero F. Introduction to the special theme: the expansion of the health data ecosystem–rethinking data ethics and governance. Big Data & Society. Volume 6. London, England: SAGE Publications Sage UK; 2019. p. 2053951719852969.

Reisman D, Schultz J, Crawford K, Whittaker M. Algorithmic impact assessments: a practical Framework for Public Agency. AI Now. 2018.

Morgan RK. Environmental impact assessment: the state of the art. Impact Assess Proj Apprais. 2012;30(1):5–14.

Samuel G, Richie C. Reimagining research ethics to include environmental sustainability: a principled approach, including a case study of data-driven health research. J Med Ethics. 2023;49(6):428–33.

Kwete X, Tang K, Chen L, Ren R, Chen Q, Wu Z, et al. Decolonizing global health: what should be the target of this movement and where does it lead us? Glob Health Res Policy. 2022;7(1):3.

Abimbola S, Asthana S, Montenegro C, Guinto RR, Jumbam DT, Louskieter L, et al. Addressing power asymmetries in global health: imperatives in the wake of the COVID-19 pandemic. PLoS Med. 2021;18(4):e1003604.

Benatar S. Politics, power, poverty and global health: systems and frames. Int J Health Policy Manag. 2016;5(10):599.

Download references

Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

Author information

Authors and affiliations.

Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada

Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, USA

Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

Department of Philosophy and Classics, University of Ghana, Legon-Accra, Ghana

Caesar A. Atuire

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

Phaik Yeong Cheah

Berkman Klein Center, Harvard University, Bogotá, Colombia

Armando Guio Español

Department of Radiology and Informatics, Emory University School of Medicine, Atlanta, GA, USA

Judy Wawira Gichoya

Health Ethics & Governance Unit, Research for Health Department, Science Division, World Health Organization, Geneva, Switzerland

Adrienne Hunt & Katherine Littler

African Center of Excellence in Bioinformatics and Data Intensive Science, Infectious Diseases Institute, Makerere University, Kampala, Uganda

Daudi Jjingo

ISI Foundation, Turin, Italy

Daniela Paolotti

Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland

Effy Vayena

Joint Centre for Bioethics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

You can also search for this author in PubMed   Google Scholar

Contributions

JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

Corresponding author

Correspondence to James Shaw .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

Download citation

Received : 31 October 2023

Accepted : 01 April 2024

Published : 18 April 2024

DOI : https://doi.org/10.1186/s12910-024-01044-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Machine learning
• Research ethics
• Global health

BMC Medical Ethics

ISSN: 1472-6939

• General enquiries: [email protected]

Mechanism Reform: An Application to Child Welfare

In many market-design applications, a new mechanism is introduced to reform an existing institution. Compared to the design of a mechanism in isolation, the presence of a status-quo system introduces both challenges and opportunities for the designer. We study this problem in the context of reforming the mechanism used to assign Child Protective Services (CPS) investigators to reported cases of child maltreatment in the U.S. CPS investigators make the consequential decision of whether to place a child in foster care when their safety at home is in question. We develop a design framework built on two sets of results: (i) an identification strategy that leverages the status-quo random assignment of investigators—along with administrative data on previous assignments and outcomes—to estimate investigator performance; and (ii) mechanism-design results allowing us to elicit investigators’ preferences and efficiently allocate cases. This alternative mechanism can be implemented by setting personalized non-linear rates at which each investigator can exchange various types of cases. In a policy simulation, we show that this mechanism reduces the number of investigators’ false positives (children placed in foster care who would have been safe in their homes) by 10% while also decreasing false negatives (children left at home who are subsequently maltreated) and overall foster care placements. Importantly, the mechanism is designed so that no investigator is made worse-off relative to the status quo. We show that a naive approach which ignores investigator preference heterogeneity would generate substantial welfare losses for investigators, with potential adverse effects on investigator recruitment and turnover.

We thank Peter Arcidiacono, Pat Bayer, Sylvain Chassang, Michael Dinerstein, Laura Doval, Joseph Doyle, Federico Echenique, Natalia Emanuel, Maria Fitzpatrick, Ezra Goldstein, Peter Hull, Chris Mills, Matt Pecenco, Katherine Rittenhouse, Alex Teytelboym and seminar participants at the 2024 ASSA Annual Meeting, SAET 2024, UC Berkeley, and Duke University for helpful comments and suggestions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

Working Groups

More from nber.

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

• Age & Generations
• Demographic Research
• Generation X
• Generation Z
• Generations
• Greatest Generation
• Methodological Research
• Millennials
• Silent Generation

Kim Parker is director of social trends research at Pew Research Center

As Biden and Trump seek reelection, who are the oldest – and youngest – current world leaders?

How teens and parents approach screen time, who are you the art and science of measuring identity, u.s. centenarian population is projected to quadruple over the next 30 years, older workers are growing in number and earning higher wages, most popular.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

Terms & Conditions

Privacy Policy

Cookie Settings

Reprints, Permissions & Use Policy