Modeling Economic Choice under Radical Uncertainty: Machine Learning Approaches

Anton Gerunov

MPRA Paper from University Library of Munich, Germany

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.

Keywords: choice; decision-making; social network; machine learning (search for similar items in EconPapers)
JEL-codes: D12 D81 (search for similar items in EconPapers)
Date: 2016-01
New Economics Papers: this item is included in nep-cmp, nep-dcm and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:69199

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