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Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach
Daniel R Cavagnaro1, Richard Gonzalez2, Jay I Myung3
1Mihaylo College of Business and Economics, California State University, Fullerton dcavagnaro.
Management Science
|February 18, 2014
Summary
This study introduces Adaptive Design Optimization (ADO) to select the best decision stimuli for testing risky choice models. ADO dynamically adapts stimuli during experiments for more efficient model discrimination.
Area of Science:
- Decision science
- Cognitive psychology
- Behavioral economics
Background:
- Discriminating between models of risky choice necessitates careful selection of decision stimuli.
- Current methods often rely on a limited set of stimuli, potentially missing optimal choices for model differentiation.
Purpose of the Study:
- To present a theoretical and methodological framework for the adaptive selection of optimal stimuli.
- To enhance the efficiency and accuracy of discriminating between various models of risky choice.
Main Methods:
- Introduced Adaptive Design Optimization (ADO), a novel approach that dynamically adjusts stimuli in each trial.
- Validated the ADO approach through simulation studies using established models of risky choice.
Main Results:
- The ADO approach demonstrated effectiveness in selecting stimuli that optimally discriminate between competing models.
- Simulation results confirmed the utility of adaptive stimulus selection for model comparison.
Conclusions:
- Adaptive Design Optimization provides a powerful framework for improving the empirical testing of decision-making models.
- This method offers a more efficient and rigorous way to gather data for model discrimination in risky choice research.
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