View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Available online at www.sciencedirect.com ScienceDirect From behavioural economics to neuroeconomics to decision neuroscience: the ascent of biology in research § on human decision making 1,2,3 1 Peter Bossaerts and Carsten Murawski Here, we briefly review the evolution of research on human stance, the economist does not need a look-up table: to decision-making over the past few decades. We discern a determine whether one option would be chosen over the trend whereby biology moves from subserving economics alternative, the economist merely picks the option with (neuroeconomics), to providing the data that advance our the maximum value. knowledge of the nature of human decision-making (decision neuroscience). Examples illustrate that the integration of In economic theory, the value function does not neces- behavioural and biological models is fruitful especially for sarily reflect subjective preferences, or the agent’s understanding heterogeneity of choice in humans. ‘needs’ or ‘wants.’ Preferences are formulated in a way Addresses that is independent of the type of agent (human, market, 1 Faculty of Business and Economics, The University of Melbourne, firm) whose choices the preferences describe. Thus, 198 Berkeley Street, Parkville, VIC 3010, Australia the economist’s definition of the term ‘preferences’ is 2 Department of Finance, Eccles School of Business, University of Utah, fundamentally different from the psychologist’s. To 1655 E Campus Center Drive, Salt Lake City, UT 84112, USA 3 economists, preferences are merely a description of The Florey Institute of Neuroscience and Mental Health, 30 Royal choices, and preferences and choices are observationally Parade, Parkville, VIC 3052, Australia equivalent. Corresponding author: Bossaerts, Peter ([email protected]) Soon after the emergence of the first instances of axiom- atic choice theories, it became apparent that they could not capture many key regularities of human choice. The Current Opinion in Behavioral Sciences 2015, 5:37–42 two most famous examples are the Allais [2] and Ellsberg Neuroeconomics This review comes from a themed issue on [3] paradoxes. In subsequent years, new value functions Edited by John P O’Doherty and Colin C Camerer were proposed that improved the fit with the empirical data [4,5]. This development culminated in Prospect For a complete overview see the Issue and the Editorial Theory [6], which summarised salient characteristics of Available online 23rd July 2015 actual human choice under uncertainty in terms of http://dx.doi.org/10.1016/j.cobeha.2015.07.001 maximisation of a utility index that featured a reference # 2352-1546/ 2015 The Authors. Published by Elsevier Ltd. This is an point, a kink, probability weighting, and differential open access article under the CC BY-NC-ND license (http://creative- curvature in the gain and loss domains. Some of these commons.org/licenses/by-nc-nd/4.0/). features accommodated cognitive biases. Loss aversion, for instance, is not merely a tendency to avoid risk (which rational agents are allowed to do). Instead, it is Economic theories of human choice a cognitive bias that makes an agent choose differently For a large part of the 20th century, research on human depending on whether a prospect is presented as losses choice was dominated by economic theories, particularly or as gains [7]. rational choice and revealed preferences theory. This ap- proach starts from a limited set of properties that are Prospect Theory models capture human cognitive biases imposed on choices (rationality axioms). It then deter- within a framework of utility maximisation. Thus, its mines to what extent choices can be summarised (repre- approach is consistent with the approaches of earlier sented) by maximisation of some latent mathematical economic theories. The success of Prospect Theory function, typically referred to as utility or value was sealed when an axiomatic version of the theory function. The form of the value function depends on emerged [8]. At the time, alternative (complementary the nature of the axioms [1]. The value function and or substitutable) theories were proposed such as Herbert its maximisation merely constitute a compact way to Simon’s ‘satisficing’ [9] or Gerd Gigerenzer’s ‘heuristics summarise choices. In binary (pairwise) choice, for in- toolbox’ [10]. However, those theories cannot readily be § The authors gratefully acknowledge financial support from the finance department of the faculty of business and economics at the University of Melbourne and from the finance department of the David Eccles School of Business at the University of Utah. The contents of this article have been shaped by numerous discussions with colleagues in economics, psychology and neuroscience. Comments from two anonymous referees were particularly helpful. The views expressed here, however, are the authors’ only. www.sciencedirect.com Current Opinion in Behavioral Sciences 2015, 5:37–42 38 Neuroeconomics translated into the language of traditional economic the description of algorithms underlying observed choice theory. Some have argued that Simon’s theory choice and their biophysical implementation. Human could be translated into a value maximisation framework, decision-making would thereby become understand- by adding constraints to cognition [11]. Unfortunately, able at a lower level of description than the traditional, constrained optimisation often presupposes cognitive ca- abstract, axiomatic approach had done. It corrected a pabilities that contradict the bounded rationality that situation which actually was the opposite of that in underlies satisficing behaviour. Indeed, constrained opti- vision research, where the biophysical took precedence misation problems may be very ‘hard’ [12]. Still, this is not over the abstract [16]. a concern for traditional economics, where the agent would choose merely ‘as if’ implementing constrained Very quickly, this research program led to some fascinat- optimisation. ing results, including the discovery of, and subsequently, ability to manipulate, the very value (utility) signals that The strength of the axiomatic approach cannot be over- constitute the core of the axiomatic theory [17–21][17– estimated. It provides a disciplined way of modelling 19,20 ,21]. More recently, it has provided more detail into choice as utility maximisation. It avoids the pitfalls of how value maximisation is implemented at a neural level, other approaches that merely fit value functions to data. borrowing ideas from drift-diffusion models in psycho- Indeed, a value function may fit data well but may be such physics [22] and detailed neural networks with mutual that it violates rationality constraints that may be far less inhibition [23], among others. This line of research also controversial than the observed cognitive biases that the led to the discovery that some basic axioms of choice value function was meant to capture in the first place. theory such as Irrelevance of Independent Alternatives Such was the case with the original version of Prospect (IIA) are violated due to fundamental properties of the Theory [6], where the probability weighting function was central nervous system, namely, divisive normalisation at odds with the sure-thing principle — outcomes that [24]. Violations occur when the availability of a third, would occur under any alternative prospect ended up clearly inferior option, makes people choose the lower- influencing choice. (The subsequent, axiomatic version valued option in a pair more frequently than in the of Prospect Theory corrected this [13].) absence of this third option. Under divisive normalisation, inputs (e.g., sources of light, auditory signals, values of The axiomatic approach and behavioural economics alike available options) are re-scaled to fit a preset range. start and finish with choice data. The value or utility Biophysically, divisive normalisation happens because function that is maximised is just another way to describe neuronal firing is affected by activation of nearby neurons. choices. The maximisation process (which, as already The discovery was particularly exciting, because divisive mentioned, could be rather complex) is not to be taken normalisation may predict behavioural features that econ- literally: the agent chooses ‘as if’ maximising utility. omists had not detected yet. One small step in that Importantly, the axiomatic approach does not provide a direction is the prediction that independent alternatives mechanistic account of how choice is implemented but may actually have the reverse effect on choice when the only describes the properties of choices. Equally impor- values of options are relatively close. The example is also tantly, both approaches assume that preferences are ex- important because it shows how biological data, hitherto ogenous, which unfortunately precludes an important outside the field of view of economists, can help to make type of intervention. ‘Bad’ choices (compulsive gambling, sense of choice anomalies. insufficient retirement savings, eating disorders, drug addiction, etc.) cannot be changed through a change of To date, neuroeconomic data have mainly been used to preferences, but only through a change of the available better distinguish between competing valuation models options or re-framing of the options [14], or through when choice data alone were not sufficient (given typical education [15]. sample