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Subjective optimality in finite sequential decision-making.
Yeonju Sin1, HeeYoung Seon1, Yun Kyoung Shin2
1Department of Biomedical Engineering, UNIST, Ulsan, South Korea.
Plos Computational Biology
|December 16, 2021
Summary
Humans often make suboptimal choices in sequential decision-making tasks, like the secretary problem. A dynamic programming model and pupil dilation reveal subjective valuation drives these deviations, explaining why we don't always choose optimally.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Decision Science
Background:
- Many real-world decisions are sequential and time-bound.
- Optimal strategies exist mathematically, but human choices often deviate.
- Understanding these deviations is key to decision-making research.
Purpose of the Study:
- Investigate mechanisms behind suboptimal choices in finite sequential decision-making.
- Explain human deviations from optimality using a dynamic programming model.
- Explore the role of subjective valuation and physiological responses.
Main Methods:
- Utilized the secretary problem as a model for sequential decision-making.
- Conducted three independent experiments.
- Employed a dynamic programming model with a subjective value function.
- Measured pupil dilation as a physiological indicator.
Main Results:
- The dynamic programming model accurately explained and predicted human choices.
- Pupil dilation correlated with decision difficulty and choice outcomes.
- Value sensitivity, reflecting subjective valuation, linked physiological responses to stimuli.
Conclusions:
- Subjective valuation plays a critical role in finite sequential decision-making.
- Human suboptimality arises from subjective valuation processes.
- Model-based and physiological data support the influence of subjective value.
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