, Volume 189, Supplement 1, pp 81–93

Combining psychological models with machine learning to better predict people’s decisions


    • Department of Industrial EngineeringJerusalem College of Technology
  • Inon Zuckerman
    • Department of Industrial Engineering and ManagementAriel University Center of Samaria
  • Amos Azaria
    • Department of Computer ScienceBar-Ilan University
  • Sarit Kraus
    • Department of Computer ScienceBar-Ilan University
    • Institute for Advanced Computer StudiesUniversity of Maryland

DOI: 10.1007/s11229-012-0182-z

Cite this article as:
Rosenfeld, A., Zuckerman, I., Azaria, A. et al. Synthese (2012) 189: 81. doi:10.1007/s11229-012-0182-z


Creating agents that proficiently interact with people is critical for many applications. Towards creating these agents, models are needed that effectively predict people’s decisions in a variety of problems. To date, two approaches have been suggested to generally describe people’s decision behavior. One approach creates a-priori predictions about people’s behavior, either based on theoretical rational behavior or based on psychological models, including bounded rationality. A second type of approach focuses on creating models based exclusively on observations of people’s behavior. At the forefront of these types of methods are various machine learning algorithms.This paper explores how these two approaches can be compared and combined in different types of domains. In relatively simple domains, both psychological models and machine learning yield clear prediction models with nearly identical results. In more complex domains, the exact action predicted by psychological models is not even clear, and machine learning models are even less accurate. Nonetheless, we present a novel approach of creating hybrid methods that incorporate features from psychological models in conjunction with machine learning in order to create significantly improved models for predicting people’s decisions. To demonstrate these claims, we present an overview of previous and new results, taken from representative domains ranging from a relatively simple optimization problem and complex domains such as negotiation and coordination without communication.


Prediction modelsPsychological models for people’s decisionsMixed agent—human systems

Copyright information

© Springer Science+Business Media Dordrecht 2012