Conceptual Framework of Behavioural Economics

The main purpose of this paper is to systematize the characteristics of behavioural economics, and, on this basis, to highlight the differences between behavioural economics and neoclassical economics. Special emphasis is placed on the differences between the real and the rational economic man. Attention is focused on economic choice modelling under the influence of behavioural economics and the emergence of the so-called limited rationality. The paper also presents the methodological tools of behavioural economics, as well as the principles on which it is built as a modern branch of economic theory.

Contribution of Lionel Robbins’ essay to economic psychology

Nowadays economic psychology is a field of science that has serious potential to compete classical economic theories. Many contemporary authors are interested in the discipline which is proved by the variety of emerging branches of economic psychology. Some of them are economists, other psychologists, but a small percentage of them have both economic and psychological education. This pattern explains some serious misunderstandings in the scientific literature in the field. The lack of understanding of both sciences at the same time leads to extremes in the conclusions, which in turn are not accepted as universally valid by economists and psychologists. Мoreover, the literature on the subject written by economists and psychologists seems very different. Economists attach more importance to the results of economic choice, while psychologists analyze primarily the causes for it. However, the connection between the two disciplines is indisputable. Despite the variety of branches of economic psychology, it is worth paying attention to the first work in the field written by an economist, which gives fundamental answers that modern scientists seem to miss. Lionel Robbins essay represents an extraordinary balance between the economic and the psychological issues, united in an ideal symbiosis.

Stakes and expertise modulate conformity in economic choice

The influences of expertise and group size on an individual’s tendency to align with a majority opinion have been attributed to informational and normative conformity, respectively: Whereas the former refers to the treatment of others’ decisions as proxies for outcomes, the latter involves positive affect elicited by group membership. In this study, using a social gambling task, we pitted alignment with a high- vs. low-expertise majority against a hypothetical monetary reward, thus relating conformity to a broader literature on valuation and choice, and probed the countering influence of a high-expertise minority opinion. We found that the expertise of a countering minority group significantly modulated alignment with a low-expertise majority, but only if such alignment did not come at a cost. Conversely, participants’ knowledge of payoff probabilities predicted the degree of majority alignment only when a high-expertise majority endorsed a more costly option. Implications for the relative influences of expertise and stakes on conformity are discussed.

Sources of confidence in value-based choice

Confidence, the subjective estimate of decision quality, is a cognitive process necessary for learning from mistakes and guiding future actions. The origins of confidence judgments resulting from economic decisions remain unclear. We devise a task and computational framework that allowed us to formally tease apart the impact of various sources of confidence in value-based decisions, such as uncertainty emerging from encoding and decoding operations, as well as the interplay between gaze-shift dynamics and attentional effort. In line with canonical decision theories, trial-to-trial fluctuations in the precision of value encoding impact economic choice consistency. However, this uncertainty has no influence on confidence reports. Instead, confidence is associated with endogenous attentional effort towards choice alternatives and down-stream noise in the comparison process. These findings provide an explanation for confidence (miss)attributions in value-guided behaviour, suggesting mechanistic influences of endogenous attentional states for guiding decisions and metacognitive awareness of choice certainty.

Frontal but not parietal cortex is required for decisions under risk

Neurons in frontal and parietal cortex encode task variables during decision-making, but causal manipulations of the two regions produce strikingly different results. For example, silencing the posterior parietal cortex (PPC) in rats and monkeys produces minimal effects in perceptual decisions requiring integration of sensory evidence, but silencing frontal cortex profoundly impairs the same decisions. Here, we tested, for the first time, the causal roles of the rat frontal orienting field (FOF) and PPC in economic choice under risk. On each trial, rats chose between a lottery and a small but guaranteed surebet. The magnitude of the lottery was independently varied across trials and was indicated to the rat by the pitch of an auditory cue. As in perceptual decisions, both unilateral and bilateral PPC muscimol inactivations produced weak effects. FOF inactivations produced substantial changes in behavior even though our task had no working memory component. We quantified control and bilateral inactivation behavior with a multi-agent model consisting of a mixture of a 'rational' utility-maximizing agent (U=Vρ) with two `habitual' agents that either choose surebet or lottery. Silencing PPC produced no significant shifts in any parameters relative to controls. Effects of FOF silencing were best explained by a decrease in ρ, the exponent of the utility function. This effect was parsimoniously explained by a dynamical model where the FOF is part of network that performs sensory-to-value transformations.

On Rational Choice and the Representation of Decision Problems

In economic theory, an agent chooses from available alternatives—modeled as a set. In decisions in the field or in the lab, however, agents do not have access to the set of alternatives at once. Instead, alternatives are represented by the outside world in a structured way. Online search results are lists of items, wine menus are often lists of lists (grouped by type or country), and online shopping often involves filtering items which can be viewed as navigating a tree. Representations constrain how an agent can choose. At the same time, an agent can also leverage representations when choosing, simplifying their choice process. For instance, in the case of a list he or she can use the order in which alternatives are represented to make their choice. In this paper, we model representations and decision procedures operating on them. We show that choice procedures are related to classical choice functions by a canonical mapping. Using this mapping, we can ask whether properties of choice functions can be lifted onto the choice procedures which induce them. We focus on the obvious benchmark: rational choice. We fully characterize choice procedures which can be rationalized by a strict preference relation for general representations including lists, list of lists, trees and others. Our framework can thereby be used as the basis for new tests of rational behavior. Classical choice theory operates on very limited information, typically budgets or menus and final choices. This is in stark contrast to the vast amount of data that specifically web companies collect about their users’ choice process. Our framework offers a way to integrate such data into economic choice models.

Covert valuation for information sampling and choice

We use our eyes to assess the value of objects around us and carefully fixate options that we are about to choose. Neurons in the prefrontal cortex reliably encode the value of fixated options, which is essential for decision making. Yet as a decision unfolds, it remains unclear how prefrontal regions determine which option should be fixated next. Here we show that anterior cingulate cortex (ACC) encodes the value of options in the periphery to guide subsequent fixations during economic choice. In an economic decision-making task involving four simultaneously presented cues, we found rhesus macaques evaluated cues using their peripheral vision. This served two distinct purposes: subjects were more likely to fixate valuable peripheral cues, and more likely to choose valuable options whose cues were never even fixated. ACC, orbitofrontal cortex, dorsolateral pre-frontal cortex, and ventromedial prefrontal cortex neurons all encoded cue value post-fixation. ACC was unique, however, in also encoding the value of cues before fixation and even cues that were never fixated. This pre-saccadic value encoding by ACC predicted which cue was next fixated during the decision process. ACC therefore conducts simultaneous processing of peripheral information to guide information sampling and choice during decision making.

Normative Principles for Decision-Making in Natural Environments

The decisions we make are shaped by a lifetime of learning. Past experience guides the way that we encode information in neural systems for perception and valuation, and determines the information we retrieve when making decisions. Distinct literatures have discussed how lifelong learning and local context shape decisions made about sensory signals, propositional information, or economic prospects. Here, we build bridges between these literatures, arguing for common principles of adaptive rationality in perception, cognition, and economic choice. We discuss how a single common framework, based on normative principles of efficient coding and Bayesian inference, can help us understand a myriad of human decision biases, including sensory illusions, adaptive aftereffects, choice history biases, central tendency effects, anchoring effects, contrast effects, framing effects, congruency effects, reference-dependent valuation, nonlinear utility functions, and discretization heuristics. We describe a simple computational framework for explaining these phenomena. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Computational theories of Alcohol Use Disorder: Mapping learning and choice mechanisms on symptoms

Alcohol use disorder (AUD) is characterized by a combination of symptoms including excessive craving, loss of control and progressive neglect of alternative pleasures. A mechanistic understanding of what drives these symptoms is needed to improve diagnostic stratification and to develop new treatment and prevention strategies for AUD. To date, there is no consensus regarding a unifying mechanistic framework that accounts for the different symptoms of AUD. Reinforcement learning (RL) and economical choice theories may be key to elucidate the underlying processes of symptom development and maintenance in AUD. These algorithms may account for the different behavioral and physiological phenomena and are suited to dissect mechanisms linked to different symptoms of addiction. We here review different RL and economic choice models and how they map onto three symptoms of AUD: (1) cue-induced craving, (2) neglect of alternative rewards, and (3) consumption despite adverse consequences. For each symptom and theory we describe findings from animal and human studies. In humans, we focus on empirical studies that investigated RL models in the context of treatment outcome in AUD. In addition, we briefly compare the RL accounts to the application of the active inference framework to addictive behaviors. The review indicates important gaps to be addressed in the future, i.e., adjusting study designs to improve translation towards improved clinical care. We also critically evaluate the potentials and pitfalls of a symptom-oriented computational phenotyping and highlight the importance to elucidate the role of learning and decision-making processes across diagnostic boundaries.