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Cognitive models of optimal sequential search with recall.
Sudeep Bhatia1, Lisheng He2, Wenjia Joyce Zhao3
1University of Pennsylvania, United States.
People often use suboptimal thresholds in sequential search tasks. However, cognitive models reveal that this behavior aligns with optimal search when accounting for risk aversion, effort costs, and decision errors, demonstrating resource-rational decision-making.
Area of Science:
- Cognitive Psychology
- Decision Science
- Behavioral Economics
Background:
- Everyday decisions frequently involve sequential search, where options are evaluated one by one at a cost.
- Optimal strategies typically employ threshold rules to balance search costs against potential rewards.
- Previous research indicates human search thresholds are often lower than theoretically optimal values.
Purpose of the Study:
- To investigate the reasons behind seemingly suboptimal stopping thresholds in sequential search behavior.
- To evaluate cognitive models that incorporate psychological factors influencing search decisions.
- To determine if observed behavior is consistent with resource-rational decision-making under specific conditions.
Main Methods:
- Development and application of various cognitive models.
- Parametric model fitting to individual participant data from search tasks.
- Analysis of search behavior considering factors like risk aversion, effort cost, and decision error.
Main Results:
- Participant search behavior aligns with optimal strategies when risk aversion, psychological effort costs, and decision errors are considered.
- Observed stopping thresholds can be explained by models incorporating these psychological factors.
- Decision makers exhibit resource-rational behavior, maximizing stochastic risk-averse utility.
Conclusions:
- Human sequential search behavior is not necessarily suboptimal but can be explained by resource-rationality.
- Psychological factors such as risk aversion and effort significantly influence search strategy.
- Threshold models provide a valuable framework for understanding both computational and algorithmic aspects of search.
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