A Cognitive Modeling Analysis of Risk in Sequential Choice Tasks

There exists a variety of instruments that assess risk propensity, or an individual's intrinsic tendency to be risk seeking. This thesis looks at four widely-studied cognitive tasks (the optimal stopping problem, the Balloon Analogue Risk Task, bandit problems, and a preferential choice gambling task) and three commonly used risk questionnaires (Risk Propensity Scale, Risk Taking Index, and Domain-Specific Risk-Taking Scale). Although these decision-making tasks and risk questionnaires have been studied extensively in isolation, there has been less research comparing measures of risk propensity across them. The motivation for examining the relationships between the tasks is that if an individual has a fundamental propensity to take risks, then this trait should be reflected in various questionnaires and cognitive tasks in which behavior is sensitive to risk. Within-subjects data was collected through Amazon Mechanical Turk from 56 participants. As measures of risk from the decision-making tasks, four cognitive models are implemented in which there are psychological variables that can be interpreted as risk propensity. Modeling results, based on Bayesian inferences about parameters and their correlations, show that people's risk behavior is consistent within tasks, but there is less evidence that the way people manage risk in each domain generalizes across tasks and questionnaires

Subjective optimality in finite sequential decision-making

Author summaryIn many real-life decisions, such as hiring an employee, the current candidate is the only option decision-makers can choose among sequentially revealed options, while past options are forgone and future options are unknown. To make the best choice in such problems, decision-makers should set appropriate criteria considering the distribution of values and remaining chances. Here, we provide behavioral and physiological evidence for subjective valuation that explains how individuals set criteria deviating from optimality. The extent to which individuals expect from candidates, how sensitive they are to the value of candidates, and how costly it is to examine each candidate determine the way how individuals make choices. Our results suggest that seemingly suboptimal decision strategies in finite sequential decisions may be optimal in subjective valuation. Many decisions in life are sequential and constrained by a time window. Although mathematically derived optimal solutions exist, it has been reported that humans often deviate from making optimal choices. Here, we used a secretary problem, a classic example of finite sequential decision-making, and investigated the mechanisms underlying individuals' suboptimal choices. Across three independent experiments, we found that a dynamic programming model comprising subjective value function explains individuals' deviations from optimality and predicts the choice behaviors under fewer and more opportunities. We further identified that pupil dilation reflected the levels of decision difficulty and subsequent choices to accept or reject the stimulus at each opportunity. The value sensitivity, a model-based estimate that characterizes each individual's subjective valuation, correlated with the extent to which individuals' physiological responses tracked stimuli information. Our results provide model-based and physiological evidence for subjective valuation in finite sequential decision-making, rediscovering human suboptimality in subjectively optimal decision-making processes

On the Significance of Information

Diese Dissertation unterstreicht die Rolle von Informationen bei der Erforschung ökonomischer Entscheidungen. Das erste Kapitel beschäftigt sich mit dem Zugang zu akkuraten Informationen über die Wirksamkeit medizinischer Interventionen. Unserem Model zufolge besteht in vielen entwickelten Ländern Nachfrage für Zugang zu unabhängigen medizinischen Informationen wie Cochrane Reviews. Wir schätzen, dass für viele Länder diese Nachfrage zu moderaten oder geringen Kosten erfüllt werden kann. Das zweite Kapitel beschäftigt sich mit der Kommunikation solcher Informationen und untersucht den unterstützenden Effekt natürlicher Häufigkeiten bei der Berechnung von A-posteriori Wahrscheinlichkeiten (Gigerenzer & Hoffrage, 1995). Durch eine Meta-Analyse erklären wir Konzepte und entflechten die Effekte von 15 Studienmerkmalen. Im einfachsten Studiendesign führen natürliche Häufigkeiten zu 24 Prozent korrekten Antworten verglichen mit 4 Prozent bei konditionellen Wahrscheinlichkeiten. Die finalen beiden Kapitel analysieren Satisficing-Strategien für unsichere Entscheidungsumgebungen in denen Agenten eine vollumfassende, probabilistische Beschreibung des Entscheidungsproblems fehlt. Simon (1955) zufolge nutzen Satisficing-Strategien ein Anspruchsniveau um die Suche nach weiteren Entscheidungsalternativen zu beenden. Das dritte Kapitel beschreibt wie solche Strategien in der ökonomischen Literatur als Präferenz modelliert werden um Entscheidungen zu erklären die der Nutzenmaximierung unterlegen sind während die Kognitionswissenschaften diese Strategien als Lösungen für Inferenzprobleme betrachten. Wir erklären die Divergenz mit unterschiedlichen Annahmen über die vorliegenden Informationen der Agenten. Das letzte Kapitel untersucht Satisficing-Strategien unter Taxifahrern. Wir stellen fest, dass die Stundenlöhne von Taxifahrern kaum vorhersagbar sind und ihre Entscheidungen Schichten zu beenden am besten durch einfache Satisficing-Strategien vorhergesagt werden können.This doctoral thesis emphasizes the significance of informational conditions in studying economic decisions. The first chapter concerns access to accurate probabilistic information in the domain of medical interventions. We estimate that, in many developed countries, there appears to be demand for governments to grant citizens free access to impartial reviews of medical evidence, as provided in Cochrane Reviews. For these countries, we estimate that this demand can be met at low costs. The second chapter concerns the communication of such information and examines the facilitating effect of natural frequencies on the derivation of posterior probabilities, as delineated by Gigerenzer and Hoffrage (1995). In a meta-analysis, we clarify concepts and disentangle the effects of 15 study characteristics. We find that in the simplest study design, 4 percent correct solutions when presented with conditional probabilities and 24 percent when presented with natural frequencies. The final two chapters examine the satisficing class of strategies for uncertain decision environments in which agents lack a full probabilistic description of the decision problem. According to Simon (1955), satisficing strategies use aspiration levels to terminate search for suitable alternatives. The third chapter describes how in economics, satisficing is modeled as a preference structure or as a decision rule that yields choices inferior to utility maximization, whereas in cognitive science, satisficing strategies use aspiration levels to solve inference problems. We explain the divergence, noting that they refer to risky and uncertain environments, respectively. The final chapter examines satisficing in an applied setting, studying taxi drivers' shift termination behavior. We find that drivers' hourly earnings are very uncertain and drivers’ behavior is best predicted by simple satisficing strategies that terminate shifts when reaching an aspired shift income or shift duration