Neuroscience and the Economics of Decision Making

Alessandro Innocenti, Alessandro Innocenti, Angela Sirigu

• Publisher: Routledge
• Year: 2013
• Print ISBN: 9780415678438
• eBook ISBN: 9781136333743
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

In the last two decades there has been a flourishing research carried out jointly by economists, psychologists and neuroscientists. This meltdown of competences has lead towards original approaches to investigate the mental and cognitive mechanisms involved in the way the economic agent collects, processes and uses information to make choices. This research field involves a new kind of scientist, trained in different disciplines, familiar in managing experimental data, and with the mathematical foundations of decision making. The ultimate goal of this research is to open the black-box to understandthe behavioural and neural processes through which humans set preferences and translate these behaviours into optimal choices. This volume intends to bring forward new results and fresh insights into this matter.

Part I

Evidence on the neuroscientific foundations of decision-making

1 Private and social counterfactual emotions

Behavioural and neural effects

Chiara Crespi , Giuseppe Pantaleo , Stefano F. Cappa and Nicola Canessa

Introduction

Decision-making is a multi-component and ubiquitous process prompted by the individual’s needs, desires and goals. People are continuously involved in several concurrent choices, concerning both short-term and long-term purposes, in order to achieve an overall satisfactory state in line with the desired one. From a computational perspective, decision-making may be decomposed into different stages. First, the decision-maker has to realize the current state as unsatisfying. Such awareness highlights the need for the exploration of the decisional environment, i.e. the research and recognition of potentially rewarding options. Then, the evaluation of available options in terms of the cost–benefit ratios leads to select the one that might provide the better output. Choices that promote an increase of so-called ‘utility’, compared with those that turn out bad, are more likely to be replicated in the future. To put it differently, the valence of reinforcement (reward vs punishment) results in a positive vs negative association between the choice made and a pleasant vs unpleasant output, respectively. This association elicits subjective expectations about the reinforcing value of stimuli, and enables a learning process leading to adaptive behavioural changes. Moreover, the efforts invested to reach a well-being state are deeply rooted in a dynamic environment, where the subjective value of potential sources of reward is highly variable. Therefore, the balance between exploration and exploitation of potential sources of reward is crucial for optimal choice behaviour in an extremely complex system characterized by risk and/or uncertainty.

While such key concepts about decision-making may appear straightforward, it is by no means clear how people evaluate available options in order to choose the one that maximizes utility. Ever since the beginning of theoretical reflection and, more recently, scientific research on decision-making, this issue has been a matter of debate.

Classical economic theories of choice, locating decision-making under risk in the realm of rational cognitive processes, specify a set of normative prescriptions to describe rational economic behaviour. Within a historical framework, such prescriptions are reflected first in the notion of expected value (Bernoulli 1954) – i.e. a measure of the overall amount of reward potentially resulting from a choice, weighted by its probability – and then in that of expected utility (von Neumann and Morgenstern 1944) – i.e. a measure of the subjective desirability of that reward, once again weighted by its probability. In particular, von Neumann and Morgenstern (1944) suggested that an individual’s drive to choose a specific option under risk depends on the desire to maximize utility, in terms of either satisfaction or profit, and developed a set of axioms constraining the way in which people (are supposed to) represent their decisional preferences. In their view, equipped with a complete knowledge about both one’s own preference-system and choice-outcomes probabilities, the rational decision-maker always goes for the alternative that maximizes expected utility. While useful for choice-quality assessment in specific settings, such a normative framework clearly appears unrealistic from the point of view of the psychological aspects of choice. To put it simply, expected utility theory indicates how an individual should choose in order to be considered rational, but is not truly informative about how real people actually decide, or why they frequently violate such normative prescriptions.

In the last decades, a renowned interest in these topics arose from cognitive psychology, and particularly from seminal studies by Amos Tversky and Daniel Kahneman leading to prospect theory (Kahneman and Tversky 1979), probably the most influential descriptive model of choice behaviour under risk and uncertainty. In addition, these authors describe several heuristics (i.e. simplifying strategies in cognitive demanding situations) and ensuing cognitive biases (i.e. systematic deviations from normative prescriptions) to account for violations of rational theories of choice (Tversky and Kahneman 1974). Within their framework, while evaluating options individuals assess their potential outcomes as gains or losses with respect to a subjective reference point, rather than in terms of their absolute value. Moreover, such evaluation entails the engagement of two distinct functions, concerning either the value or the probability of outcomes. In the first case, the traditional monotonic utility function is replaced by a value function, whose S-shape reflects several important properties of choice behaviour (Figure 1.1). Namely, while concavity in the gain domain reflects risk aversion for gains, convexity in the loss domain explains risk seeking for losses. The value function is steeper for losses than gains, reflecting loss aversion, i.e. the greater sensitivity to losses than equivalent gains (approximately twice as much). Furthermore, the status of gains and losses as related to an abstract reference point accounts for the framing effect, i.e. the fact that different choices (e.g. to risk or not to risk) may be elicited by different descriptions of the same decisional setting. Importantly, in prospect theory such a subjective value is not integrated with normatively defined probability, but rather with a psychological weight, reflecting the impact of probability on the overall value of the prospect, and mentally represented by an inverse S-shaped weighting function. The shape of this function represents a crucial dimension of the theory, as it reflects the individuals’ tendency to overweight small probabilities and underweight medium-large ones. Both value function and weighted function share the principle of diminishing sensitivity, i.e. the fact that the marginal impact of a change in outcome diminishes with distance from the subjective reference point.

Figure 1.1 A typical value function.

Since its formulation, prospect theory provided enormous theoretical and practical contributions to a descriptive approach to decision-making, i.e. how real agents make real decisions. In the meantime, other data have made it clear that decision-making cannot be conceived as a purely cognitive process, and that spontaneous facets of choice, such as loss and risk aversion, are likely to be also driven by factors other than cognition, and particularly by emotional drives (Loewenstein et al. 2001; Camerer 2005).

In line with this proposal, among the several theoretical approaches to emotion-based decision-making, decision affect theory (Mellers et al. 1997) suggests that choices are influenced by the anticipation of emotions that people expect to feel about the outcome. In this view, choices are strictly associated with, and can be predicted from, emotional experiences. In general, elation and disappointment arise after wins and losses, respectively. Both elation and disappointment are cognitively based emotions involving counterfactual comparisons between two states of the world. That is, emotional responses to the same outcome may differ, depending on alternative (counterfactual) outcomes, so that foregone outcomes work as a reference for evaluating obtained (factual) outcomes. Thus, when a counterfactual outcome is better or worse than the actual one, people experience disappointment or elation, respectively. Moreover, the effect of surprise associated with the outcome probability seems to modulate individuals’ emotional responses, leading to an overall enhancement of emotional post-decisional experience. Namely, unexpected wins and losses are perceived as more elating and disappointing than expected ones, respectively. In sum, decision affect theory claims that maximizing subjective expected emotions is different from maximizing subjective expected utilities. In general, people select those alternatives that minimize potential negative affects. As a result, small gains may even be perceived as more pleasurable than larger ones, depending on expectations and counterfactual comparisons.

As discussed above, variables other than cognition, and precisely emotional factors, are needed to explain the decisional behaviour displayed by real decision-makers engaged in everyday-life choices. Yet, it is likely that, besides basic counterfactual feelings such as elation and disappointment, a crucial role is also played by more complex emotions arising from cognitive processing. Starting from this assumption, various attempts have been made to incorporate negative cognitively based feelings, such as regret, elicited by counterfactual reasoning, into a theory of choice (Bell 1982; Loomes and Sudgen 1982).

Counterfactual thinking and cognitively based emotions

Counterfactual thinking is a pervasive aspect of mental life, entailing mental simulations of alternatives to facts, events and beliefs (Epstude and Roese 2008; Roese 1997). From an ecological perspective, counterfactual thoughts play a central role in evaluating actuality, and offer tangible alternatives that contribute to regulating individuals’ behaviour. Counterfactual-based evaluations of one’s own experience occur spontaneously, particularly when things turn out badly. In these situations, when mental alternatives are better than reality, counterfactual thoughts are triggered by the unpleasant emotional state arising from the negative outcome. Via this mechanism, counterfactual simulations mediate, through top-down processes, more complex emotional states, such as regret/relief and envy/gloating, in the private and social domain, respectively.

Clues into the mechanisms underlying counterfactual reasoning are provided by mental models theory (Johnson-Laird and Byrne 1991), which encompasses counterfactual statements into a general theory of conditionals. Unlike other ‘if … then’ assertions, counterfactuals make two different mental representations immediately explicit. While the first mental model is referred to actuality (i.e. the factual world), the second one is related to a possible alternative to reality (i.e. the counterfactual world). Thus, simultaneous representations of contrasting mental models elicit the experience of a wide range of complex feelings. For this reason, counterfactual thinking has been considered as a sort of emotional amplifier (Kahneman and Miller 1986), affecting both personal and int...

Table of contents

• Cover
• Halftitle
• Title
• Copyright
• Contents
• List of Figures
• List of Tables
• List of contributors
• Foreword
• Acknowledgments
• PART I. Evidence on the neuroscientific foundations of decision-making
• PART II. Emotions and morality in decision-making
• PART III. Learning and risk attitude in decision-making
• PART IV. Probability and judgment in decision-making
• PART V. Decision-making in social interaction
• Index