Planning & Dynamics

Traditional random utility models assume stable preferences in preferential decision-making. However, behavioural decision theory suggests that preferences can be dynamic, influenced by various factors. Here, we investigated preference change using extended Discrete Choice Experiments (DCE). Across multiple experiments, we investigated whether instructions emphasising specific product attributes impacted decision outcomes, people adapt their preferences based on feedback, and how varying the signal-to-noise ratio of the feedback signal affects learning in preferential choice environments. Linear mixed effects models revealed that preferences shifted over time, even in static choice environments. Under emphasis instructions, utility systematically increased for emphasised attributes at the cost of unemphasised attributes. Participants also demonstrated the ability to learn preferences from feedback. However, beyond changes in attribute weighting, we also considered whether participants altered their underlying decision strategies in response to task demands. We also tested whether people may adopt simpler strategies with increasing task experience, such as choice based on a subset of information or random choice. To explore this, we developed a dynamic random utility model to examine shifts in decision strategy. We found that fatigue introduced additional decision noise but did not lead to purely random choice, or a reduction in information use—participants continued to engage in cognitively demanding decision strategies. These findings highlight the dynamic nature of preferences and suggest that preferential decision-making can be influenced by multiple factors. Our findings highlight the importance of developing dynamic models to capture preference change, offering a more nuanced understanding of decision-making processes beyond static assumptions of traditional models.

This project examines changes in individuals’ strategies for multi-stage decision tasks with varying levels of complexity. In Experiment 1, decision-makers were presented with decision problems where they chose between a two-stage gamble, and an alternative option that varied in complexity. Individuals who chose the two-stage gamble continued to a second decision stage, where they chose between two gambles. The alternative option was either a sure reward (low complexity), a gamble (medium complexity), or another two-stage gamble (high complexity). We use backward induction to derive the optimal decision strategy on each trial and find that the proportion of participants who made optimal decisions decreased as the level of complexity increased. We also observe a main effect of ordering, where participants behave more optimally as they progressed in the experiment. In Experiment 2, we replicate and extend our findings with manipulations aimed at elucidating the roles of learning and adaptation across trials and conditions. Additionally, we use decision field theory to investigate how changes in information processing and decision strategies can explain the observed results.

We evaluate changes in risk-seeking behavior across multi-stage decision tasks. In three experiments, participants chose between a sure reward or a risky gamble with equal expected value. Those who chose to gamble continued to a second decision stage and faced another choice problem. Rational choice theories require consequentialist behavior: choices should only depend on possible future outcomes. However, our findings indicate that previous, consequentially irrelevant chance events affected participants’ willingness to gamble at the second decision stage. Participants made riskier choices after experiencing an ‘unfortunate’ event than after a ‘fortunate’ one. We consider three candidate explanations for these inconsistencies: the Gambler’s Fallacy, Shifting Reference Points, and Aspiration Level. Behavioral analyses and computational model comparisons indicate that Aspiration Level provides the best account, with people choosing to gamble after an unfortunate event in an attempt to “redeem” their performance.

Although most research into risky decision making has focused on simple scenarios – where isolated choices are made independent of one another – many important decisions in life play out across sequences of interdependent events and actions. Despite the ubiquity and importance of such decision problems, we know relatively little about how people manage the complexities of dynamic, multistage decisions. Our work combines techniques from two research traditions to investigate how people handle the challenges of dynamic decision making. We use true-and-error models to estimate the distribution and stability of preference profiles, and the presence of errors. In a complementary analysis we use cognitive modeling based on Decision Field Theory to investigate the psychological processes underlying dynamic decision making. Decision Field Theory provides a unified framework for testing competing hypotheses about how people collect information and plan for the future. Results from both sets of analyses identify distinct groups of individuals. We discuss the behavioral and cognitive factors distinguishing groups from one another, including degree of planning, strategy shifts, biased information sampling, and effort-saving information processing.