Discovering Rational Heuristics for Risky Choice

For computationally limited agents such as humans, perfectly rational decision-making is almost always out of reach. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a machine learning method that identifies the best heuristics to use in any given situation. To demonstrate the generalizability and accuracy of our method, we compare the strategies it discovers against those used by people across a wide range of multi-alternative risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although they tend to collect too little information and their strategy choices do not always fully exploit the structure of the environment.

Probability weighting explains Independence Axiom violations of Expected Utility Theory in monkeys

Expected Utility Theory (EUT) provides axioms for maximizing utility in risky choice. The independence axiom (IA) is its most demanding axiom: preferences between two options should not change when altering both options equally by mixing them with a common gamble. We tested common consequence (CC) and common ratio (CR) violations of the IA in thousands of stochastic choice over several months using a large variety of binary option sets. Three monkeys showed few outright Preference Reversals (8%) but substantial graded Preference Changes (46%) between the initial preferred gamble and the corresponding altered gamble. Linear Discriminant Analysis (LDA) indicated that gamble probabilities predicted most Preference Changes in CC (72%) and CR (87%) tests. The Akaike Information Criterion indicated that probability weighting within Cumulative Prospect Theory (CPT) explained choices better than models using Expected Value (EV) or EUT. Fitting by utility and probability weighting functions of CPT resulted in nonlinear and non-parallel indifference curves (IC) in the Marschak-Machina triangle and suggested IA non-compliance of models using EV or EUT. Indeed, CPT models predicted Preference Changes better than EV and EUT models. Indifference points in out-of-sample tests were closer to CPT-estimated ICs than EV and EUT ICs. Finally, while the few outright Preference Reversals may reflect the long experience of our monkeys, their more graded Preference Changes corresponded to those reported for humans. In benefitting from the wide testing possibilities in monkeys, our stringent axiomatic tests contribute critical information about risky decision-making and serves as basis for investigating neuronal decision mechanisms.

Reward-Related Decision-Making in Current and Past Disordered Gambling: Implications for Impulsive Choice and Risk Preference in the Maintenance of Gambling Disorder

Impulsive reward-related decision-making (RRDM) is robustly associated with gambling disorder (GD), although its role in the development and perpetuation of GD is still being investigated. This project sought to examine the possible roles of impulsive and risky choice, two aspects of RRDM, in the perpetuation of GD. Additionally, the potential moderating role of comorbid substance misuse was considered. A total of 434 participants with symptoms of current GD and symptoms of concurrent substance use disorder (SUD; n = 105), current GD with past SUD (n = 98), past GD with current SUD (n = 53), or past GD with past substance use disorder (SUD; n = 92), and 96 healthy controls were recruited through MTurk. Participants completed a randomly adjusting delay discounting (a measure of impulsive choice) and probabilistic discounting (a measure of risky choice) task and self-report questionnaires of gambling participation, GD and SUD symptomology, and trait impulsivity. Although control participants showed significantly greater delay discounting compared to individuals with a current or history of GD, no significant group differences emerged between individuals with current GD or a history of GD. Individuals with current GD showed significantly less probabilistic discounting compared to individuals with a history of GD and control participants showed the greatest rates of probabilistic discounting. These effects remained after controlling for lifetime gambling symptom severity and trait impulsivity. Overall, these findings suggest a potential maintaining role of risky choice in gambling disorder, but do not support a maintaining role for impulsive choice.

Asymmetrical Property of the Subproportionality of Weighting Function in Prospect Theory: Is It Real and How Can It Be Achieved?

An asymmetrical property of the probability weighting function, namely, subproportionality, was derived from observations. Subproportionality can provide a reasonable explanation for accommodating the Allais paradox and, therefore, deserves replication for its high impact. The present study aimed to explore the mechanism of subproportionality by comparing the two completely opposite decision mechanisms: prospect theory and equate-to-differentiate theory. Results revealed that the underlying mechanism supports the prediction of equate-to-differentiate theory but not prospect theory in the diagnostic stimuli condition. Knowledge regarding which intra-dimensional difference between Options A and B is greater, not knowledge regarding which option’s overall prospect value is greater, indeed predicts option preference. Our findings may deepen current understanding on the mechanisms behind the simple risky choice with a single-non-zero outcome. Additionally, these findings will hopefully encourage subsequent researchers to take a fresh look at the Allais paradox.

Teaching & Learning Guide for: Risky-choice framing and rational decision-making

An influential program of psychological research suggests that people’s judgements and decisions depend on the way in which information is presented, or ‘framed’. In a central choice paradigm, decision-makers seem to adopt different preferences, and different attitudes to risk, depending on whether the options specify the number of people who will be saved or the corresponding number who will die. It is standardly assumed that such responses violate a foundational tenet of rational decision-making, known as the principle of description invariance. However, recent theoretical and empirical research has begun to challenge the dominant ‘irrationalist’ narrative. The alternative approaches being developed typically pay close attention to how decision- makers represent decision problems (including their interpretation of numerical quantifiers or predicate choice). They also highlight the need for a more robust characterization of the description invariance principle itself.

Selective outcome reinstatement during evaluation drives heuristics in risky choice

A ubiquitous feature of human decision making under risk is that individuals differ from each other, as well as from normativity, in how they incorporate reward and probability information. One possible explanation for these deviations is a desire to reduce the number of potential outcomes considered during choice evaluation. Although multiple behavioral models can be invoked involving selective consideration of choice outcomes, whether differences in these tendencies underlie behavioral differences in sensitivity to reward and probability information is unknown. Here we consider neural evidence where we exploit magnetoencephalography (MEG) to decode the actual choice outcomes participants consider when they decide between a gamble and a safe outcome. We show that variability in tendencies of individual participants to reinstate neural outcome representations, based on either their probability or reward, explains variability in the extent to which their choices reflect consideration of probability and reward information. In keeping with this we also show that participants who are higher in behavioral impulsivity fail to preferentially reinstate outcomes with higher probability. Our results suggest that neural differences in the degree to which outcomes are considered shape risk taking strategy, both in decision making tasks, as well as in real life.

The relationship between physiological arousal and multiple levels of temporal context in risky choice

Context-dependence is fundamental to risky monetary decision-making. A growing body of evidence suggests that temporal context, or recent events, alters risk-taking at a minimum of three timescales: immediate (e.g. trial-by-trial), neighborhood (e.g. a group of consecutive trials), and global (e.g. task-level). To examine context effects, we created a novel monetary choice set with intentional temporal structure in which option values shifted between multiple levels of value magnitude (“contexts”) several times over the course of the task. This structure allowed us to examine whether effects of each timescale were simultaneously present in risky choice behavior and the potential mechanistic role of arousal, an established correlate of risk-taking, in context-dependency. We found that risk-taking was sensitive to immediate, neighborhood, and global timescales, increasing following small (vs. large) outcome amounts, large positive (but not negative) shifts in context, and when cumulative earnings exceeded expectations. We quantified arousal with skin conductance responses, which were specifically related to the global timescale, increasing with cumulative earnings, suggesting that physiological arousal captures a task-level assessment of performance. We complimented this correlational analysis with a secondary reanalysis of risky monetary choices following the double-blind administration of propranolol and a placebo during a temporally unstructured choice task. We replicated our behavioral finding that risk-taking is context-sensitive at three timescales but found no change in temporal context-effects following propranolol administration. Our results demonstrate that risky decision-making is consistently dynamic at multiple timescales and that arousal is likely the consequence, rather than the cause, of temporal context in risky monetary decision-making.

How top-down and bottom-up attention modulate risky choice

We examine how bottom-up (or stimulus-driven) and top-down (or goal-driven) processes govern the distribution of attention in risky choice. In three experiments, participants chose between a certain payoff and the chance of receiving a payoff drawn randomly from an array of eight numbers. We tested the hypothesis that initial attention is driven by perceptual properties of the stimulus (e.g., font size of the numbers), but subsequent choice is goal-driven (e.g., win the best outcome). Two experiments in which task framing (goal driven) and font size (stimulus driven) were manipulated demonstrated that payoffs with the highest values and the largest font sizes had the greatest impact on choice. The third experiment added a number in large font to the array, which could not be an outcome of the gamble (i.e., a distractor). Eye movement and choice data indicated that although the distractor attracted attention, it had no influence on option selection. Together with computational modeling analyses, the results suggest that perceptual salience can induce bottom-up effects of overt selection but that the perceived value of information is the crucial arbiter of intentional control over risky choice.

The continuous and changing impact of affect on risky decision-making

Affective experience has an important role in decision-making with recent theories suggesting a modulatory role of affect in ongoing subjective value computations. However, it is unclear how varying expectations and uncertainty dynamically influence affective experience and how dynamic representation of affect modulates risky choices. Using hierarchical Bayesian modeling on data from a risky choice task (N = 101), we find that the temporal integration of recently encountered choice parameters (expected value, uncertainty, and prediction errors) shapes affective experience and impacts subsequent choice behavior. Specifically, experienced arousal prior to choice was associated with increased loss aversion, risk aversion, and choice consistency. Taken together, these findings provide clear behavioral evidence for continuous affective modulation of subjective value computations during risky decision-making.