Risky Choice 1

When making decisions under risk, information about the options' payoff distributions is often initially limited and must therefore be actively gathered. Making such experience-based decisions not only requires a procedure for comparing options, but also a procedure for guiding and stopping information search. Although search, comparison, and stopping are elementary components of the decision-making process, rather little is known about how they might contribute to particular preference patterns. Here, we develop a computational framework to specify sampling strategies for experience-based risky choice in terms of a search, a comparison, and a stopping rule, and examine the choice patterns emerging under different settings of these rules. Our analyses demonstrate how descriptive hallmarks of decision making under risk–such as deviations from expected value (EV) maximization, risk aversion, and over- or underweighting of rare events–can arise from the operation and interplay of the building blocks that compose the sampling strategies. For instance, we show how frequent switching between options during search and a longer search process lead to more EV maximization and a linear weighting of outcomes and probabilities when the samples are integrated and evaluated according to a summary comparison rule. In contrast, we show how the same search pattern leads to systematic deviations from EV maximization, an S-shaped probability weighting function, and a highly compressed value function when the samples are integrated and evaluated according to a roundwise comparison rule. Moreover, our analyses reveal how sampling strategies produce different choice behaviors depending on the properties of the choice ecology.

This is an in-person presentation on July 20, 2024 (10:00 ~ 10:20 CEST).

An old debate in the decision sciences is concerned with the question of how different forms of uncertainty influence people’s perceptions and hence their choices. One plausible classification distinguishes between aleatory uncertainty which refers to stochastic variability in outcomes and epistemic uncertainty which refers to a lack of knowledge of something that is, in principle, knowable. Although this distinction is commonly used, to date no study has systematically disentangled (1) whether people perceive these two forms of uncertainty differently, (2) whether their perceptions are independent or correlated, and (3) how the respective perceptions are associated with people’s perception of a situation’s general uncertainty. To answer these questions, we conduct an experiment with a 2 (epistemic uncertainty: low vs. high) x 2 (aleatory uncertainty: low vs. high) between-subjects design. We implement this design both in the self-report (vignette-based scenarios) and the task (incentivized lotteries) space to thus model the relationship between different types of perceived uncertainty. By modeling how an individual’s perception of uncertainty changes as a function of source and degree of uncertainty, we intend to make at least two contributions. First, we inform researchers on how to investigate the distinct influence of types and perceptions of uncertainty on a decision maker’s choices. Second, we aim to contribute to the debate on how to distinguish risk, uncertainty, and different flavors thereof.

This is an in-person presentation on July 20, 2024 (10:20 ~ 10:40 CEST).

There has been considerable interest in exploring how the utility of an outcome impacts the probability with which it is mentally simulated. Earlier studies using varying methodologies have yielded divergent conclusions with different directions of the influence. To directly examine such mental process, we employed a random generation paradigm in which all the outcomes were either equally (i.e., followed a uniform distribution) or unequally (i.e., a binomial distribution) probable. While our results revealed individual differences in how the utility influenced responses, the overall findings suggested that it is the outcomes' probabilities, not their utilities, that guide this process. Notably, an initial utility-independent bias emerged, with individuals displaying a tendency to start with smaller values when all outcomes are equally likely. Our findings offer insights into the benefits of studying the mental sampling processes and provide empirical support for particular sampling models in this domain.

This is an in-person presentation on July 20, 2024 (10:40 ~ 11:00 CEST).

Decisions about extinction risk are ubiquitous, both in everyday life and for our continued existence as a species. We introduce a new risky-choice task that can be used to study this topic: The Extinction Gambling Task. Here, we investigate two versions of this task: A Keep variant, where participants cannot accumulate any more earnings after the extinction event, and a Lose variant, where extinction also wipes out all previous earnings. We derive optimal strategies for both variants and compare them to participants' behaviour in a series of four experiments. Our findings suggest that people understand the difference between the variants and their behaviour is qualitatively in line with the optimal solution. We further test heuristic accounts of the strategies that participants use to approximate the optimal solutions, which would not be tractable to calculate. Finally, we provide mixture modelling results to better understand variability between participants in terms of the employed strategies. We hope that our task and results will motivate further research on this vital topic.

This is an in-person presentation on July 20, 2024 (11:00 ~ 11:20 CEST).

The development of formal models of decision making under risk has been shaped largely by decisions between options with monetary outcomes, with the most prominent model being cumulative prospect theory (CPT). Whereas CPT is good at describing choices between monetary lotteries, it shows poorer performance in the context of decisions between options with nonmonetary and nonnumerical outcomes (e.g., medications with possible side effects). We suggest that this may be due to affective processes and context-dependent evaluation—which are not considered in CPT—playing a larger role in nonmonetary than in monetary choices. We propose three psychologically motivated modifications to CPT's modeling framework to capture these differences: (a) representing the subjective value of a nonmonetary outcome by an affect rating (rather than a monetary equivalent); (b) determining the subjective affective value of an outcome relative to the value of the worst outcome in the choice problem; (c) assuming that the probability weighting for an outcome depends on the amount of affect triggered. We submit model variants of CPT implementing the proposed modifications to a model comparison in three empirical data sets. For choices between options with negative nonmonetary outcomes (medications with possible side effects), these modifications substantially improve CPT's performance relative to that of the original version of CPT. The same does not hold for monetary choices. Overall, in addition to fleshing out key differences in the processing of monetary and nonmonetary risky options, our work demonstrates how affective processes can be formally integrated within classical theories of decision making under risk.

This is an in-person presentation on July 20, 2024 (11:20 ~ 11:40 CEST).