Risky Choice

In economic decision-making there is a fundamental trade-off between deliberation time to make a good decision and opportunity costs of other rewarding activities. Recent theories analyzed how the optimal strategy of evidence accumulation for this problem depends on the environment. If the utility difference between two options is known a priori, deciders should accumulate evidence according to a drift-diffusion model with constant decision boundaries, if this difference is unknown beforehand collapsing boundaries should be used. Further, the exact position of the boundaries depends on the opportunity costs. However, little is known about whether people use these strategies adaptively. Here, we used a new data visualization to find signature patterns of behavior for optimal strategies. We then conducted two experiments, where participants rated and chose between risky lotteries, while we varied prior information and opportunity costs. We found that while participants were sensitive to opportunity costs, they failed to stop deliberation about their choices fast enough when no information about the utility difference of two lotteries was available. We discuss how this suboptimality can make participants spend too much time on problems where there is little to gain in real-world scenarios. Hence, whereas prior research focused on biases from utility maximization, we show that when taking opportunity costs into account, deciders can be too eager to maximize utility in an isolated choice problem.

This is an in-person presentation on July 19, 2023 (15:20 ~ 15:40 UTC).

Bipolar disorder (BD) is associated with excessive pleasure-seeking risk-taking behaviors that often characterize its clinical presentation. However, the mechanisms of risk-taking behavior are not well-understood in BD. Recent data suggest prior substance use disorder (SUD) in BD may represent certain trait-level vulnerabilities for risky behavior. This study examined the mechanisms of risk-taking and the role of SUD in BD via mathematical modeling of behavior on the Balloon Analogue Risk Task (BART). Three groups—18 euthymic BD with prior SUD (BD+), 15 euthymic BD without prior SUD (BD–), and 33 healthy comparisons (HC)—completed the BART. Behavior was modeled using four competing hierarchical Bayesian models. Model comparison results favored the Exponential-Weight Mean-Variance (EWMV) model, which encompasses and delineates five cognitive components of risk-taking: prior belief, learning rate, risk preference, loss aversion, and behavioral consistency. Both BD groups, regardless of SUD history, showed lower behavioral consistency than HC. BD+ exhibited more pessimistic prior beliefs (relative to BD– and HC) and reduced loss aversion (relative to HC) during risk-taking on the BART. Traditional measures of risk-taking on the BART detected no group differences. These findings suggest that reduced behavioral consistency is a crucial feature of risky decision-making in BD and that SUD history in BD may signal additional trait vulnerabilities for risky behavior even when mood symptoms and substance use are in remission. This study also underscores the value of using mathematical modeling to understand behavior in research on complex disorders like BD.

This is an in-person presentation on July 19, 2023 (15:40 ~ 16:00 UTC).

Probability weighting is one of the most powerful theoretical constructs in formal descriptive models of risky choice and constitutes the backbone of cumulative prospect theory (CPT). Probability weighting has been shown to be related to two facets of attention allocation: one analysis showed that differences in the shape of CPT's probability-weighting function are linked to differences in how attention is allocated across attributes (i.e., probabilities vs. outcomes); another analysis (that used a different measure of attention) showed a link between probability weighting and differences in how attention is allocated across options. However, the relationship between these two links is unclear. We investigate to what extent attribute attention and option attention independently contribute to probability weighting. Reanalyzing data from a process-tracing study, we first demonstrate links between probability weighting and both attribute attention and option attention within the same data set, the same measure of attention, and the same analytical framework. We then find that attribute attention and option attention are at best weakly related and have independent and distinct effects on probability weighting. Moreover, deviations from linear weighting mainly emerged when attribute attention or option attention were imbalanced. Our analyses enrich the understanding of the cognitive underpinnings of preferences and illustrate that similar probability-weighting patterns can be associated with very different attentional policies. This complicates an unambiguous psychological interpretation of psycho-economic functions. Our findings indicate that cognitive process models of decision making should aim to concurrently account for the effects of different dimensions of attention on preference. In addition, we argue that the origins of biases in attribute attention and option attention need to be better understood.

This is an in-person presentation on July 19, 2023 (16:00 ~ 16:20 UTC).

Individuals make countless decisions that involve evaluating uncertain outcomes every day. The resulting behavior, often referred to as risk taking, has been studied for decades, with a strong focus on trait-like predictors of interindividual differences, such as the construct of risk preference. Yet, about 50% of variance in individual’s risky choices cannot be explained with stable predictors, thus raising the question what situational factors affect risk taking. With the current study, we investigate one potential mechanism causing variation in risk taking between different choice-situations, specifically the perception of uncertainty. Individuals intuitively distinguish epistemic uncertainty, referring to lacking knowledge about the world, from aleatory uncertainty, caused by the innate randomness of the world. Previous laboratory research has found that people become increasingly risk-averse when their perception of uncertainty is more epistemic, rather than aleatory, however, it remains unclear if this tendency generalizes to real world decisions. We are tackling this shortcoming by tracking a person’s decisions during their everyday life with an experience sampling study. This allows us to model participants’ decision making throughout the day using Bayesian multilevel models, and show how different levels of epistemic uncertainty can predict risk taking. Additionally, we are collecting data on individuals’ perception of situational uncertainty with a classic, yet often neglected method: Participants record think-aloud protocols, describing decision-situations as they experience them. With the resulting speech-data, we investigate two novel research questions. First, we use participants’ verbal descriptions of choice-situations to quantify the degree of uncertainty an individual faces, and to thus predict variability in risk taking from these estimates. Second, we explore a relatively new way of using natural language data to model which features of a situation are relevant, salient, or accessible to individuals when making decisions. With that, we show how semantic information such as word embeddings can be used for inferring cognitive processes underlying risk taking, or other decision-processes.

This is an in-person presentation on July 19, 2023 (16:20 ~ 16:40 UTC).

Situations requiring to balance exploration and exploitation are ubiquitous. In such, humans frequently have the chance to observe others. Participants performed restless nine-armed bandit tasks, either on their own or while seeing the choices of fictitious agents, which were equally good, but different regarding their tendency to explore. We used different Bayesian Mean Tracker models to fit participants data. Therein, individual choice probabilities are calculated from the expected values of all options using a softmax function, in which random exploration is implemented as temperature parameter while directed exploration biases the expected values of the options towards especially uncertain, informative options. We implemented copying in two different ways: in the unconditional copying model it is assumed that participants copy the observed agent with a fixed probability, independent of the subjective value estimations. In the copy when uncertain model, the probability of copying depends on the entropy in all options’ value estimations. Our results indicate that the copy when uncertain model can account better for participants data than the unconditional copying model. Participants use observational learning directly, i.e., they imitate the specific choices, but they also accommodate their individual exploration strategy towards the strategy of the observed agents.

This is an in-person presentation on July 19, 2023 (16:40 ~ 17:00 UTC).