Decision‐Making and Neuroeconomics

Abstract

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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References

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

Celebrini S and Newsome WT (1994) Neuronal and psychophysical sensitivity to motion signals in extrastriate area MST of the macaque monkey. Journal of Neuroscience 14: 4109–4124.

Crockett MJ, Braams B, Clark L et al. (2013) Restricting temptations: neural mechanisms of precommitment. Neuron 7: 391–401.

Crockett MJ, Clark L, Tabibnia G, Lieberman MD and Robbins TW (2008) Serotonin modulates behavioral reactions to unfairness. Science 320: 1739.

De Martino B, Kumaran D, Seymour B and Dolan RJ (2006) Frames, biases, and rational decision‐making in the human brain. Science 313: 684–687.

Fehr E and Schmidt KM (1999) A theory of fairness, competition and cooperation. Quarterly Journal of Economics 114: 817–868.

Fiorillo CD, Tobler PN and Schultz W (2003) Discrete coding of reward probability and uncertainty by dopamine neurons. Science 299: 1898–1902.

Gigerenzer G and Goldstein DG (1996) Reasoning the fast and frugal way, models of bounded rationality. Psychological Review 103: 650–669.

Glimcher PW (2010) Economic kinds: understanding the abstractions and esthetics of economic thought. In: Glimcher PW (ed.) Foundations of Neuroeconomic Analysis. New York: Oxford University Press.

Glimcher PW and Rustichini A (2004) Neuroeconomics: the consilience of brain and decision. Science 306: 447–452.

Goldstein DG and Gigerenzer G (2002) Models of ecological rationality, the recognition heuristic. Psychological Review 109: 75–90.

Gul F and Pesendorfer W (2005) The Case for Mindless Economics. Princeton: Princeton Press.

Güth W, Schmittberger R and Schwarze B (1982) An experimental analysis of the ultimatum game. Journal of Economic Behaviour and Organization 3: 367–388.

Hare TA, Camerer CF and Rangel A (2009) Self‐control in decision‐making involves modulation of the vmPFC valuation system. Science 324: 646–648.

Hart AS, Rutledge RB, Glimcher PW and Phillips PE (2014) Phasic dopamine release in the rat nucleus accumbens symmetrically encodes a reward prediction error term. Journal of Neuroscience 34: 698–704.

Huettel SA, Stowe CJ, Gordon EM, Warner BT and Platt ML (2006) Neural signatures of economic preferences for risk and ambiguity. Neuron 49: 765–775.

Kable JW and Glimcher PW (2007) The neural correlates of subjective value during intertemporal choice. Nature Neuroscience 10: 1625–1633.

Kahneman D and Tversky A (1979) Prospect theory, an analysis of decision under risk. Econometrica 47: 263–291.

Kahneman D and Tversky A (1984) Choices, values, and frames. American Psychology 39: 341–350.

Kalenscher T, Ohmann T, Windmann S, Freund N and Gunturkun O (2006b) Single forebrain neurons represent interval timing and reward amount during response scheduling. European Journal Neuroscience 24: 2923–2931.

Kalenscher T and Pennartz CM (2008) Is a bird in the hand worth two in the future? The neuroeconomics of intertemporal decision‐making. Progress in Neurobiology 84: 284–315.

Kalenscher T and van Wingerden M (2011) Why we should use animals to study economic decision making – a perspective. Frontiers in Neuroscience 5: 1–11.

Kalenscher T, Windmann S, Diekamp B et al. (2005) Single units in the pigeon brain integrate reward amount and time‐to‐reward in an impulsive choice task. Current Biology 15: 594–602.

Kim JN and Shadlen MN (1999) Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nature Neuroscience 2: 176–185.

Kim S, Hwang J and Lee D (2008) Prefrontal coding of temporally discounted values during intertemporal choice. Neuron 59: 161–172.

Knoch D, Gianotti LR, Pascual‐Leone A et al. (2006a) Disruption of right prefrontal cortex by lowfrequency repetitive transcranial magnetic stimulation induces risk taking behavior. Journal of Neuroscience 26: 6469–6472.

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

Kobayashi S and Schultz W (2008) Influence of reward delays on responses of dopamine neurons. Journal of Neuroscience 28: 7837–7846.

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

Louie K and Glimcher PW (2010) Separating value from choice: delay discounting activity in the lateral intraparietal area. Journal of Neuroscience 30: 5498–5507.

Mazur JE (1984) Tests of an equivalence rule for fixed and variable reinforcer delays. Journal of Experimental Psychology: Animal Behavior Processes 10: 426–436.

McClure SM, Laibson DI, Loewenstein G and Cohen JD (2004) Separate neural systems value immediate and delayed monetary rewards. Science 306: 503–507.

McCoy AN and Platt ML (2005) Risk‐sensitive neurons in macaque posterior cingulate cortex. Nature Neuroscience 8: 1220–1227.

Metcalfe J and Mischel W (1999) A hot/cool‐system analysis of delay of gratification, dynamics of willpower. Psychological Review 106: 3–19.

Mulder MJ, Wagenmakers EJ, Ratcliff R, Boekel W and Forstmann BU (2012) Bias in the brain: a diffusion model analysis of prior probability and potential payoff. Journal of Neuroscience 32: 2335–2343.

Nash JF (1950) Equilibrium points in n‐person games. Proceedings of the National Academy of Sciences of the USA 36: 48–49.

von Neumann J and Morgenstern O (1944) Theory of Games and Economic Behavior. Princeton, NJ: University Press.

O'Doherty J, Dayan P, Schultz J et al. (2004) Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304: 452–454.

O'Neill M and Schultz W (2010) Coding of reward risk by orbitofrontal neurons is mostly distinct from coding of reward value. Neuron 68: 789–800.

Padoa‐Schioppa C and Assad JA (2006) Neurons in the orbitofrontal cortex encode economic value. Nature 441: 223–226.

Platt ML and Glimcher PW (1999) Neural correlates of decision variables in parietal cortex. Nature 400: 233–238.

Rangel A, Camerer C and Montague PR (2008) A framework for studying the neurobiology of value‐based decision making. Nature Reviews Neuroscience 9: 545–556.

Ratcliff R (1988) Continuous versus discrete information processing, modeling accumulation of partial information. Psychological Review 95: 238–255.

Roesch MR, Calu DJ and Schoenbaum G (2007) Dopamine neurons encode the better option in rats deciding between differently delayed or sized rewards. Nature Neuroscience 10: 1615–1624.

Roesch MR and Olson CR (2005) Neuronal activity in primate orbitofrontal cortex reflects the value of time. Journal of Neurophysiology 94: 2457–2471.

Samuelson PA (1937) A note on measurement of utility. Review of Economic Studies 4: 155–161.

Sanfey AG, Rilling JK, Aronson JA, Nystrom LE and Cohen JD (2003) The neural basis of economic decision‐making in the ultimatum game. Science 300: 1755–1758.

Schall JD (2001) Neural basis of deciding, choosing and acting. Nature Reviews Neuroscience 2: 33–42.

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

Singer T, Seymour B, O'Doherty JP et al. (2006) Empathic neural responses are modulated by the perceived fairness of others. Nature 439: 466–469.

Stephens DW (1981) The logic of risk‐sensitive foraging preferences. Animal Behavior 29: 628–629.

Stephens DW and Anderson D (2001) The adaptive value of preference for immediacy, when shortsighted rules have farsighted consequences. Behavioral Ecology 12: 330–339.

Stephens DW and Krebs JR (1986) Foraging Theory. Monographs in Behavior and Ecology. Princeton, NJ: University Press.

Tobler PN, Christopoulos GI, O'Doherty JP, Dolan RJ and Schultz W (2009) Risk‐dependent reward value signal in human prefrontal cortex. Proceedings of the National Academy of Sciences of the USA 106: 7185–7190.

Tom SM, Fox CR, Trepel C and Poldrack RA (2007) The neural basis of loss aversion in decision‐making under risk. Science 315: 515–518.

Yang T and Shadlen MN (2007) Probabilistic reasoning by neurons. Nature 447: 1075–1080.

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