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Machine learning and behavioral economics for personalized choice architecture

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  • Emir Hrnjic
  • Nikodem Tomczak

Abstract

Behavioral economics changed the way we think about market participants and revolutionized policy-making by introducing the concept of choice architecture. However, even though effective on the level of a population, interventions from behavioral economics, nudges, are often characterized by weak generalisation as they struggle on the level of individuals. Recent developments in data science, artificial intelligence (AI) and machine learning (ML) have shown ability to alleviate some of the problems of weak generalisation by providing tools and methods that result in models with stronger predictive power. This paper aims to describe how ML and AI can work with behavioral economics to support and augment decision-making and inform policy decisions by designing personalized interventions, assuming that enough personalized traits and psychological variables can be sampled.

Suggested Citation

  • Emir Hrnjic & Nikodem Tomczak, 2019. "Machine learning and behavioral economics for personalized choice architecture," Papers 1907.02100, arXiv.org.
    • Handle: RePEc:arx:papers:1907.02100
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    File URL: http://arxiv.org/pdf/1907.02100
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    Cited by:

    1. Olena LIASHENKO & Tetyana KRAVETS & Matvii PROKOPENKO, 2021. "Consumer behavior clustering of food retail chains by machine learning algorithms," Access Journal, Access Press Publishing House, vol. 2(3), pages 234-251, September.

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