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Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches

Author

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  • Gerunov, Anton

Abstract

This paper utilizes a novel data on consumer choice under uncertainty, obtained in a laboratory experiment in order to gain substantive knowledge of individual decision-making and to test the best modeling strategy. We compare the performance of logistic regression, discriminant analysis, naïve Bayes classifier, neural network, decision tree, and Random Forest (RF) to discover that the RF model robustly registers the highest classification accuracy. This model also reveals that apart from demographic and situational factors, consumer choice is highly dependent on social network effects.

Suggested Citation

  • Gerunov, Anton, 2016. "Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches," MPRA Paper 69199, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:69199
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    File URL: https://mpra.ub.uni-muenchen.de/69199/1/MPRA_paper_69199.pdf
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    References listed on IDEAS

    as
    1. Daniel Ellsberg, 1961. "Risk, Ambiguity, and the Savage Axioms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 75(4), pages 643-669.
    2. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. Xi Zhao & Yong Shi & Jongwon Lee & Heung Kee Kim & Heeseok Lee, 2014. "Customer Churn Prediction Based on Feature Clustering and Nonparallel Support Vector Machine," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 13(05), pages 1013-1027.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Anton A. Gerunov, 2020. "Machine Learning Algorithms For Forecasting Asset Prices: An Application To The Housing Market," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 17(1), pages 27-42.
    2. Anton Gerunov, 2020. "Classification algorithms for modeling economic choice," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 45-67.

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    More about this item

    Keywords

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    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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