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Predicting Risk Perception: New Insights from Data Science

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  • Sudeep Bhatia

    (Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We outline computational techniques for predicting perceptions of risk. Our approach uses the structure of word distribution in natural language data to uncover rich representations for a very large set of naturalistic risk sources. With the application of standard machine learning techniques, we are able to accurately map these representations onto participant risk ratings. Unlike existing methods in risk perception research, our approach does not require any specialized participant data and is capable of generalizing its learned mappings to make quantitative predictions for novel (out-of-sample) risks. Our approach is also able to quantify the strength of association between risk sources and a very large set of words and concepts and, thus, can be used to identify the cognitive and affective factors with the strongest relationship with risk perception and behavior.

Suggested Citation

  • Sudeep Bhatia, 2019. "Predicting Risk Perception: New Insights from Data Science," Management Science, INFORMS, vol. 65(8), pages 3800-3823, August.
    • Handle: RePEc:inm:ormnsc:v:65:y:2019:i:8:p:3800-3823
    DOI: 10.1287/mnsc.2018.3121
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    References listed on IDEAS

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    4. Daniel Wall & Raymond D. Crookes & Eric J. Johnson & Elke U. Weber, 2020. "Risky choice frames shift the structure and emotional valence of internal arguments: A query theory account of the unusual disease problem," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 685-703, September.
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    6. Borchert, Philipp & Coussement, Kristof & De Weerdt, Jochen & De Caigny, Arno, 2024. "Industry-sensitive language modeling for business," European Journal of Operational Research, Elsevier, vol. 315(2), pages 691-702.

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