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From behavioural to to

decision : 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 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 .

knowledge of the 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 , or the ’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, ,

1

Faculty of Business and Economics, The , 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, ,

fundamentally different from the psychologist’s. To

1655 E Campus Center Drive, Salt Lake City, UT 84112, USA

3 , 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 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] . 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 in terms of

http://dx.doi.org/10.1016/j.cobeha.2015.07.001

maximisation of a 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. ,

for instance, is not merely a tendency to avoid

(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 models capture human cognitive biases

imposed on choices ( 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 ’s ‘

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 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 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 , eating disorders, drug

, 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 sizes). Neuroeconomics has shown, for example,

that valuation based on Bayesian principles better

From understanding choice to understanding explains neural activation and choices in a reversal learn-

neural circuitry: the advent of ing task [25]. Similarly, neurobiology demonstrated that

neuroeconomics in certain settings, choice under uncertainty seems to be

With the emergence of non-invasive human imag- based on mean-variance analysis rather than more tradi-

ing techniques such as functional magnetic resonance tional expected utility theory [26]. Mean-variance analy-

imaging (fMRI), it was only a matter of time before sis is popular in financial economics, yet unlike expected

economists and set out to determine if utility theory, can cause violations of simple rationality

there was any biological foundation of economic theories principles [27].

of choice. Key aims were to determine how choices were

implemented biologically, which neural circuitry was Despite all the successes of the neuroeconomic research

involved, and what algorithms were employed. A new program, economists may argue that it is of little rele-

field emerged, referred to as neuroeconomics, focusing on vance to economic theory, because of the perception that

Current Opinion in Behavioral Sciences 2015, 5:37–42 www.sciencedirect.com

Biology in research on human decision making Bossaerts and Murawski 39



the levels at which one can understand human decision [34 ]. Closer inspection, however, suggests that partici-

making are relatively independent, an opinion also voiced pants who received L-dopa were less erratic in their

in vision research [16]. While it may be interesting to choices, which effectively meant that they were ‘better

know which neural algorithms implement observed optimisers.’ Increases in estimated learning speed could

choice, and what biophysical constraints cause violations merely be the consequence of better fit of the economic

of the axioms of choice theory, such knowledge is deemed model. Thus, administration of L-dopa changed the prop-

irrelevant for the future development of choice theory erties of the error term of the softmax model that linked

[28,29]. valuation with choice. More recently, it has been shown

that, even absent learning, L-dopa intervention has no

Curiously, economists do appeal to biological principles effect on Prospect Theory parameters, but instead shifts



in other domains, in order to put discipline on the the error term of the softmax function [35 ]. The

parameters of their choice models. An important example researchers who discovered the effect offered a quintes-

is the use of principles of evolutionary fitness to answers sentially biological explanation, namely, Pavlovian ap-



questions such as: What are acceptable proach behaviour [51 ]. This dimension of behaviour

parameters? Will preferences feature inter-generational (phenotype) has yet to be captured by economic theories.

substitution? [30,31]. Such approaches referring to the

theory of evolution to restrict preferences is not based on Other research has shown that while genetic variation

observation, however (there are no data in the cited explains differences in risk taking across humans, this

work!). It is merely a device to restrict the parameter genetic variation does not cause shifts in the parameters

space when axioms of choice are too weak to constrain the in Prospect Theory that are meant to capture risk atti-



theory sufficiently. tudes [36 ]. Genetic variation actually correlated with a

tendency towards more or less optimising (relative to the

What if we started from biology? The predictions of Prospect Theory), but only when available

emergence of decision neuroscience

So far, biology has only played a supporting role in the Figure 1

quest for a better understanding of human behaviour,

helping to differentiate between existing valuation mod- 100

els, or elucidating the biophysical mechanics and imple-

90

mentation algorithms behind human economic decision

making. However, in recent years evidence has emerged 80

that there is significant biological variation that does not 70

map into parametric variation of even the best economic

60

models. These findings are part of a new field, decision

50

neuroscience, focused on decision-making, but where biol-

ogy no longer subserves economics and instead takes a 40 central role [32]. 30

20

Below, we give some examples of biological diversity that 10

propensity to accept gamble (%) propensity to accept gamble

maps into variation in behaviour that is reflected in the

error term of the most popular that ties 0

–6 –4 –2 0 2 4 6

valuation to choice, namely, logit, or in the language of

expected utility of gamble

neuroeconomics, softmax. For economists, the error term

Current Opinion in Behavioral Sciences

of the softmax model captures ‘unobserved heterogene-

ity’ [33]. However, it appears that this ‘error’ actually

Propensity to choose one gamble against another as a function of

contains useful information, because biological markers

difference in value. Value is prospect theory expected utility as

explain it. Hence, it ought to be modelled explicitly.

estimated from all other choices. Diamonds are MAOA-L carriers; dots

are MAOA-H carriers. Estimated differences in values are no different

One important area of research investigates the relation across genotypes (observations across genotypes are equally distant

on horizontal axis), yet choice propensities are very different

between and behaviour. Administra-

(observations on vertical axis are not equidistant). Solid black line

tion of Levadopa (L-dopa), a drug designed to increase

extending to black dashed line is best fit (softmax) for MAOA-H

levels of the in the brain, has

carriers; best fit for MAOA-L carriers (solid black line extending to grey

unintended effects on economic choice (unintended in dotted line) has a kink at zero (softmax with kink). Consequently,

the sense that it does not explain conjectured effects on prospect theory cannot capture observed differences in choice

propensities (phenotypes) for MAOA-H against MAOA-L carriers.

parameters of existing choice models). In one study, L-

Overall, MAOA-H carriers take less risk, yet prospect theory does not

dopa appeared to speed up learning in a two-armed

predict so. Parameter estimates (loss aversion, risk attitudes in gain

reward bandit problem where subjects had to discover and loss domain, probability weighting) were statistically



the option that was most rewarding on average indistinguishable (P = 0.05) across genotypes. Reproduced from [36 ].

www.sciencedirect.com Current Opinion in Behavioral Sciences 2015, 5:37–42

40 Neuroeconomics

gambles were more valuable than the risk-free alterna- hence, the genetic effect on optimisation quality is not



tive. There, too, the error term of the softmax model unlike that of administrating L-Dopa [34 ].)

shifted (Figure 1).

The examples point to a potential weakness of neuroe-

This example is rather limited, because it is rare that a conomics: it often presupposes existing economic theo-

single gene predicts behaviour. There is more promise in ries when analysing biological data, which has sometimes

polygenic or entire gene pathway analysis [37,38]. But the led to the mis-interpretation of the latter. A particularly

example demonstrates how prior research had been pertinent example is related to the role of emotions in

wrong to focus only on phenotypes that traditional eco- economic decision making. It has been known for some

nomic analysis recognised. In this case, the phenotype time that emotions and rational decision-making are not

concerned risk attitudes, and humans who were willing to orthogonal. A key study [40] contrasted choices under

accept risky gambles were categorised as ‘more risk uncertainty among patients with prefrontal and amygdala

tolerant.’ The genes correlating with this tendency to brain lesions, and discovered that emotional engagement

accept risky gambles, it was concluded, were the ones during risk taking is crucial for ‘reasoned’ decision mak-

controlling risk aversion [39]. But the conclusion was ing. In contrast, economists had been modeling emotions

wrong: those who accepted risky gambles more frequent- as interfering with rational decision making, in the form of

ly were not more risk tolerant; they were actually merely dual-self theory (e.g., [41]). Accounts of neuroeconomics,

‘better optimisers.’ Quality of optimisation is a phenotype too, often tend to emphasise a sharp delineation between,

that is not captured by traditional economic analysis, but among others, ‘cognitive’ and ‘affective’ processes

evidently very much present in human decision-making, [42]. Such dual-self theories unfortunately have biased

and apparently has a simple genetic basis. (The gene, interpretation of neural signals on a number of occasions

MAOA, regulates catabolism of, among others, dopamine; [43].

Figure 2

Biological models Behaviour (choices) Biology Economic models

Pharmacological intervention

Neuro- economics freq

Δu u(c) = 1 – e–γc

L-DOPA intervention Brain Genes

Current Opinion in Behavioral Sciences

The link between genetics, brain and behaviour (choices). Economic preferences revealed by choices are linked to brain and genes through the

biological organism (green box). The neuroeconomic approach links preferences to properties of the brain and the genome through economic

models (blue box). In this approach, an economic model is fitted to choice data and model parameters, such as risk aversion, are then correlated

with properties of the brain or the genome. Recent research suggests that this approach has severe limitations. A study using an intervention at



the biological level (administration of L-DOPA) [35 ] to induce behavioural change showed that the change in behaviour was not captured by

parameters of traditional economic models. Instead, the intervention changed the properties of the error term of economic models, suggesting

that existing (neuro-)economic models are missing important dimensions of human behaviour.

Current Opinion in Behavioral Sciences 2015, 5:37–42 www.sciencedirect.com

Biology in research on human decision making Bossaerts and Murawski 41

The future populate the market [45,46]. Only recently have we

Traditional economic approaches (including behavioural begun to understand why [47]. Importantly, this does

economics) as well as neuroeconomics have taken a not mean that decision neuroscience cannot help explain

choice-centred approach. If biology is appealed to at phenomena at an aggregate level, such as in markets.

all, it is used as ‘supporting’ or ‘converging’ evidence Indeed, recent research in neuroscience has interesting

for extant theories of choice. In contrast, we advocate an things to say about how individuals behave in the face of,

approach where biology takes a more central role. In such for example, institution-generated uncertainty, and why

an approach, biological variation would be used to iden- [48–50].

tify potential behavioural variation that would be missed

(read: absorbed by the error term) if one were to follow

Conflict of statement

economic theory alone. Behavioural scientists are to en-

Nothing declared.

gage in a genuine dialogue with biologists, because biol-

ogists observe phenomena relevant to choice that

traditional models do not capture, and they have research References and recommended reading

methods to manipulate these phenomena (e.g., pharma- Papers of particular interest, published within the period of review,

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Current Opinion in Behavioral Sciences 2015, 5:37–42 www.sciencedirect.com