A cognitive modeling analysis of risk in sequential choice tasks

Abstract

There are many ways to measure how people manage risk when they make decisions. A standard approach is to measure risk propensity using self-report questionnaires. An alternative approach is to use decision-making tasks that involve risk and uncertainty, and apply cognitive models of task behavior to infer parameters that measure people’s risk propensity. We report the results of a within-participants experiment that used three questionnaires and four decision-making tasks. The questionnaires are the Risk Propensity Scale, the Risk Taking Index, and the DomainSpecific Risk Taking Scale. The decision-making tasks are the Balloon Analogue Risk Task, the preferential choice gambling task, the optimal stopping problem, and the bandit problem. We analyze the relationships between the risk measures and cognitive parameters using Bayesian inferences about the patterns of correlation, and using a novel cognitive latent variable modeling approach. The results show that people’s risk propensity is generally consistent within different conditions for each of the decision-making tasks. There is, however, little evidence that the way people manage risk generalizes across the tasks, or that it corresponds to the questionnaire measures.

Model‐Based Wisdom of the Crowd for Sequential Decision‐Making Tasks

“…Fig. 1 shows the experimental interfaces for the three tasks developed by Guan et al (2020) to collect the data that we use.…”

“…The top panel of Fig. 3 shows the accuracy of individual and crowd performance on the specific problems used by Guan et al (2020), measured in terms of the average monetary value gained per problem. The yellow violin plots show the distribution of individual-participant performance.…”

“…3.1. Data Guan et al (2020) conducted an experiment using full-information optimal stopping problems, with the same 56 participants who completed the BART experiment. On each problem of the optimal stopping experiment, a participant was presented with a numerical value.…”

“…These results are discussed below after the modeling approach has been described. Guan et al (2020) used a previously developed model of optimal stopping behavior known as the biased-from-optimal model (Guan et al, 2015). While this is a useful model for measuring people's decisions, it makes the unrealistic processing assumption that people have access to the optimal thresholds but deviate from them in systematic ways.…”

“…While this is a useful model for measuring people's decisions, it makes the unrealistic processing assumption that people have access to the optimal thresholds but deviate from them in systematic ways. More realistic cognitive process models of optimal stopping assume that people use a set of internal and optimal decision process performance on the optimal stopping problems used by Guan et al (2020). Bottom panel: Model-based individual performance, model-based crowd performance, and optimal decision process performance on 20,000 new optimal stopping problems in the four conditions used by Guan et al (2020).…”