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Avoiding the Worst Decisions: A Simulation and Experiment

Author

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  • Kazuhisa Takemura

    (Center for Decision Research, Waseda University, Tokyo 162-8644, Japan
    Department of Psychology, Waseda University, Tokyo 162-8644, Japan)

  • Yuki Tamari

    (School of Management and Information, University of Shizuoka, Shizuoka 422-8526, Japan)

  • Takashi Ideno

    (School of Management, Tokyo University of Science, Tokyo 162-8601, Japan)

Abstract

Many practical decisions are more realistic concerning preventing bad decisions than seeking better ones. However, there has been no behavioral decision theory research on avoiding the worst decisions. This study is the first behavioral decision research on decision strategies from the perspective of avoiding the worst decisions. We conducted a computer simulation with the Mersenne Twister method and a psychological experiment using the monitoring information acquisition method for two-stage decision strategies of all combinations for different decision strategies: lexicographic, lexicographic semi-order, elimination by aspect, conjunctive, disjunctive, weighted additive, equally weighted additive, additive difference, and a majority of confirming dimensions. The rate of choosing the least expected utility value among the alternatives was computed as the rate of choosing the worst alternative in each condition. The results suggest that attention-based decision rules such as disjunctive strategy lead to a worse decision, and that striving to make the best choice can conversely often lead to the worst outcome. From the simulation and the experiment, we concluded that simple decision strategies such as considering what is most important can lead to avoiding the worst decisions. The findings of this study provide practical implications for decision support in emergency situations.

Suggested Citation

  • Kazuhisa Takemura & Yuki Tamari & Takashi Ideno, 2023. "Avoiding the Worst Decisions: A Simulation and Experiment," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1165-:d:1081680
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    References listed on IDEAS

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    1. Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.
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