Decision‐Making and Neuroeconomics


Decision‐making is the process of choosing one out of several alternatives. The study of decision‐making is inherently multidisciplinary and can be approached from many different angles. Traditional accounts in economics and biology have a normative flavour and prescribe, rather than describe decision‐making. Recently, however, efforts in psychology and behavioural ecology have resulted in the development of theories with higher descriptive validity. Furthermore, neuroeconomics is a new interdisciplinary field that combines contributions from economics, biology and psychology and aims to identify a biologically valid, mechanistic, mathematical and behavioural theory of choice and exchange. Recent neurophysiological advances have further deepened our understanding of the mechanistic implementation of a decision in the brain. This article gives an overview of the field, departing from axiomatic economic theories of choice and the psychology as well as biology of decision‐making. A particular emphasis is put on illustrating psychological, biological, neuroeconomic and neurophysiological approaches to economic decision‐making as well as interdisciplinary accounts of deviations in choice behaviour from the economic ideal.

Key Concepts:

  • Utility function: A mathematical function that models the relationship between an objective measure of a commodity (such as money) and the subjective value or desirability of that commodity. The utility function is often assumed to be concave, which is supposed to underly risk aversion.

  • Risk attitude: Animals and humans are almost never risk neutral, that is, when choosing between a very risky and a less risky (or even certain) option, most organisms prefer the less risky option (risk aversion) even if the payoff of both options is identical, or even better for the risky option. Occasionally, some individuals systematically prefer the more risky option (risk proneness).

  • Expected utility theory: Normatively flavoured model in economics that tries to prescribe how humans should make decisions under risk. It is assumed that decision‐makers compute the probability‐weighted sum of the utilities of all possible outcomes, integrate them with their current asset and then choose the option with the highest expected utility in final assets.

  • Discounted utility theory: A model similar to expected utility, but for decisions over time. The weighting factor in discounted utility theory is not probability, but a function of the delay between choice and consumption.

  • Game theory: Framework to formalise choice behaviour involving multiple interacting decision‐makers. A game allows to isolate and investigate in a controlled environment a limited set of premises of economic decision theory.

  • Prospect theory: Alternative theory to expected utility theory with higher descriptive validity. It is assumed that outcomes of a decision are evaluated with respect to changes in wealth (gains versus losses), not in final states. Furthermore, the utility function is concave in the domain of gains, and convex in the domain of losses, and steeper for losses than for gains.

  • Choice heuristic: A simple decision rule, or set of rules, that humans and animals apply to make decisions in complex environments.

  • Optimal foraging theory: Based on the assumption that evolution should have favoured the development of decision mechanisms that maximise Darwinian fitness, optimal foraging theory aims to explore animal behaviour from an optimal choice perspective.

  • Neuroeconomics: Interdisciplinary field that attempts to identify a biologically valid, mechanistic, mathematical and behavioural theory of choice and exchange.

Keywords: neuroeconomics; reward; risk; intertemporal choice; optimal foraging; brain

Figure 1.

The shape of the utility function linking utility to the objective magnitude of a commodity, relates to risk attitude. (a) Utility as a function of the value of a commodity, expressed in monetary gains. The utility curve is concave and is a decelerating function of the current level of wealth because the marginal utility, that is, the utility increment with each additional unit, decreases with increasing level of wealth. A concave utility function predicts risk aversion when choosing between a medium‐sized, certain (MM) and a large and small risky monetary option (MS and ML). The average utility of the certain option exceeds the average utility of the risky option because the utility of the large outcome is only sublinearly larger compared with the utilities of the other outcomes. (b) A convex function predicts risk proneness.

Figure 2.

Exponential versus hyperbolic discounting of future events. The figure describes a situation in which an agent chooses between a small, short‐term outcome or a large, long‐term outcome (proximal) and another situation in which both outcomes are deferred into the future by the same time interval (distant). (a) Exponential utility function of a large, delayed (grey line) and small, short‐term commodity (black line). With exponential discounting, stationarity holds because the utility of the large reward (UL) is always higher than the utility of the small reward (US). This is true when both rewards are temporally proximal, and also when they are deferred by the same time interval into the future. (b) Hyperbolic discounting can explain preference reversals and the violation of stationarity. Owing to the steeper decay for short delays, the utility of the small, short‐term commodity is higher than the large, delayed reward for temporally proximal outcomes, but the utility order reverses when both outcomes are deferred into the future.

Figure 3.

Value and weighting function according to prospect theory. (a) The value function for the domains of gains and losses. The x‐axis represents the objective measure of a commodity, such as money, the y‐axis depicts the subjective value, or utility, of the commodity as a function of its objective value. (b) The weighting function. The dotted line is the objective measure of probability, the solid line is the weighting function. See text for description.

Figure 4.

Neural representation of discounted value, and evidence for multiple choice systems in the brain. (a) A pigeon chooses between a short, always immediate and a large, gradually delayed reward. This figure shows the discounted value representation on single‐cell level in the avian forebrain. In the left panel, the animal chooses the large reward, but the neuron's activity (grey area) decreases with increasing delay duration (left, from top to bottom). When the animal chooses the small, always immediate reward (right panel), there is no noticeable difference in neural activity. Further analysis has shown that this neuron was not only responsive to changes in delay, but also reward amount. Reprinted from Kalenscher et al. () with friendly permission from Elsevier. © Elsevier. (b) Humans choose between small, short‐term or large, delayed rewards. Left panel: Activated brain regions for choices in which money is available immediately. PCC, posterior cingulate cortex; MPFC, medial prefrontal cortex; VStr, ventral striatum and MOFC, medial orbitofrontal cortex. Right panel: Brain regions that were activated while making choices independent of delay. RPar, right intraparietal cortex; DLPFC, dorsolateral prefrontal cortex; VLPFC, ventrolateral prefrontal cortex; LOFC, right lateral orbitofrontal cortex; VCtx, visual cortex; PMA, premotor area and SMA, supplementary motor area. Reprinted from McClure et al. () with friendly permission from AAAS. © AAAS Publishing.

Figure 5.

Neural signatures of self‐control and precommitment. (a) Willpower‐related activation: dorsolateral prefrontal cortex was activated when the temptation to end the delay period had to be suppressed. (b) Frontopolar cortex was activated when subjects precommitted to the larger, later reward. Reprinted from Crockett et al. (). Reprinted from an open‐access article distributed under the terms of the Creative Commons Attribution Licence, with friendly permission from the authors. © Cell Press.



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Kalenscher, Tobias(May 2014) Decision‐Making and Neuroeconomics. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0021397.pub2]