Frequent winners explain apparent skewness preferences in experience-based decisions.

Do people's attitudes toward the (a)symmetry of an outcome distribution affect their choices? Financial investors seek return distributions with frequent small returns but few large ones, consistent with leading models of choice in economics and finance that assume right-skewed preferences. In contrast, many experiments in which decision-makers learn about choice options through experience find the opposite choice tendency, in favor of left-skewed options. To reconcile these seemingly contradicting findings, the present work investigates the effect of skewness on choices in experience-based decisions. Across seven studies, we show that apparent preferences for left-skewed outcome distributions are a consequence of those distributions having a higher value in most direct outcome comparisons, a "frequent-winner effect." By manipulating which option is the frequent winner, we show that choice tendencies for frequent winners can be obtained even with identical outcome distributions. Moreover, systematic choice tendencies in favor of right- or left-skewed options can be obtained by manipulating which option is experienced as the frequent winner. We also find evidence for an intrinsic preference for right-skewed outcome distributions. The frequent-winner phenomenon is robust to variations in outcome distributions and experimental paradigms. These findings are confirmed by computational analyses in which a reinforcement-learning model capturing frequent winning and intrinsic skewness preferences provides the best account of the data. Our work reconciles conflicting findings of aggregated behavior in financial markets and experiments and highlights the need for theories of decision-making sensitive to joint outcome distributions of the available options.

What's in a sample? Epistemic uncertainty and metacognitive awareness in risk taking.

In a fundamentally uncertain world, sound information processing is a prerequisite for effective behavior. Given that information processing is subject to inevitable cognitive imprecision, decision makers should adapt to this imprecision and to the resulting epistemic uncertainty when taking risks. We tested this metacognitive ability in two experiments in which participants estimated the expected value of different number distributions from sequential samples and then bet on their own estimation accuracy. Results show that estimates were imprecise, and this imprecision increased with higher distributional standard deviations. Importantly, participants adapted their risk-taking behavior to this imprecision and hence deviated from the predictions of Bayesian models of uncertainty that assume perfect integration of information. To explain these results, we developed a computational model that combines Bayesian updating with a metacognitive awareness of cognitive imprecision in the integration of information. Modeling results were robust to the inclusion of an empirical measure of participants' perceived variability. In sum, we show that cognitive imprecision is crucial to understanding risk taking in decisions from experience. The results further demonstrate the importance of metacognitive awareness as a cognitive building block for adaptive behavior under (partial) uncertainty.

The Future of Decisions From Experience: Connecting Real-World Decision Problems to Cognitive Processes.

In many important real-world decision domains, such as finance, the environment, and health, behavior is strongly influenced by experience. Renewed interest in studying this influence led to important advancements in the understanding of these decisions from experience (DfE) in the last 20 years. Building on this literature, we suggest ways the standard experimental design should be extended to better approach important real-world DfE. These extensions include, for example, introducing more complex choice situations, delaying feedback, and including social interactions. When acting upon experiences in these richer and more complicated environments, extensive cognitive processes go into making a decision. Therefore, we argue for integrating cognitive processes more explicitly into experimental research in DfE. These cognitive processes include attention to and perception of numeric and nonnumeric experiences, the influence of episodic and semantic memory, and the mental models involved in learning processes. Understanding these basic cognitive processes can advance the modeling, understanding and prediction of DfE in the laboratory and in the real world. We highlight the potential of experimental research in DfE for theory integration across the behavioral, decision, and cognitive sciences. Furthermore, this research could lead to new methodology that better informs decision-making and policy interventions.

The link between cognitive abilities and risk preference depends on measurement.

Risk preference is an important construct for understanding individual differences in risk taking throughout the behavioral sciences. An active stream of research has focused on better understanding risk preference through its connection to other psychological constructs, in particular, cognitive abilities. Here, we examine two large-scale multimethod data sets and demonstrate that the method used to measure risk preference is an important moderator. In self-report measures, we found small but consistent positive correlations between working memory capacity/numeracy, facets of cognitive abilities, and risk tolerance. In behavioral measures, we found, on average, no correlation and large intermethod heterogeneity. This heterogeneity can be explained by the choice architecture that is created in behavioral methods-in particular, the relation between risk and reward and the impact of decision error in a task. Consequently, investigating how risk preference relates to psychological constructs such as cognitive abilities require a profound understanding of the choice architecture in measurements of risk preference and in the real world.

Empirical underidentification in estimating random utility models: The role of choice sets and standardizations.

A standard approach to distinguishing people's risk preferences is to estimate a random utility model using a power utility function to characterize the preferences and a logit function to capture choice consistency. We demonstrate that with often-used choice situations, this model suffers from empirical underidentification, meaning that parameters cannot be estimated precisely. With simulations of estimation accuracy and Kullback-Leibler divergence measures we examined factors that potentially mitigate this problem. First, using a choice set that guarantees a switch in the utility order between two risky gambles in the range of plausible values leads to higher estimation accuracy than randomly created choice sets or the purpose-built choice sets common in the literature. Second, parameter estimates are regularly correlated, which contributes to empirical underidentification. Examining standardizations of the utility scale, we show that they mitigate this correlation and additionally improve the estimation accuracy for choice consistency. Yet, they can have detrimental effects on the estimation accuracy of risk preference. Finally, we also show how repeated versus distinct choice sets and an increase in observations affect estimation accuracy. Together, these results should help researchers make informed design choices to estimate parameters in the random utility model more precisely.

Taxing cognitive capacities reduces choice consistency rather than preference: A model-based test.

How do people make preferential choices in situations where their cognitive capacities are limited? Many studies link the manipulation of cognitive resources to qualitative changes in preferences. However, there is a widely overlooked alternative hypothesis, namely, that a reduction in cognitive capacities leads to an increase in choice inconsistency. We developed a mathematical model and followed a hierarchical Bayesian estimation approach to test to what extent a reduction in cognitive capacities leads to a shift in preference or an increase in choice inconsistency. Using a within-subject n-back task to manipulate cognitive load, we conducted three experiments across different choice domains: risky choice, temporal discounting, and strategic interaction. Across all three domains, results show that a reduction in cognitive capacities predominantly affected participants' level of choice consistency rather than their respective preference. These results hold on an individual and a group level. In sum, our approach and the mathematical model we used provide a rigorous and general test of how reduced cognitive capacities affect people's decision-making. (PsycINFO Database Record