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A normative inference approach for optimal sample sizes in decisions from experience.
Dirk Ostwald1, Ludger Starke2, Ralph Hertwig3
1Arbeitsbereich Computational Cognitive Neuroscience, Department of Education and Psychology, Free University Berlin Berlin, Germany ; Center for Adaptive Rationality, Max Planck Institute for Human Development Berlin, Germany.
Frontiers in Psychology
|October 7, 2015
Summary
This study explores optimal sample sizes in decisions from experience (DFE). We link DFE sampling to statistical decision theory to evaluate optimal sample sizes, providing benchmarks for human behavior.
Area of Science:
- Behavioral Decision Making
- Cognitive Psychology
- Behavioral Economics
Background:
- Decisions from experience (DFE) research investigates choices made with limited information.
- The sampling paradigm models decision-making by allowing participants to sample outcomes from distributions before choosing.
Purpose of the Study:
- To theoretically evaluate optimal sample sizes in the DFE sampling paradigm.
- To establish a normative benchmark for assessing human sampling behavior in DFE.
Main Methods:
- Relating the DFE sampling paradigm to classical statistical decision theory.
- Utilizing probabilistic inference assumptions to derive optimal sample sizes.
- Extending analytical results to numerically evaluable constraints.
Main Results:
- Developed a framework to calculate optimal sample sizes under various constraints.
- Demonstrated the applicability of statistical decision theory to DFE.
- Provided a method for generating testable predictions for experimental studies.
Conclusions:
- Statistical decision theory offers a valuable framework for understanding optimal sampling in DFE.
- Derived optimal sample sizes can serve as benchmarks for human decision-making.
- This theoretical approach facilitates empirical investigation of DFE.
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