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Building Socially Intelligent AI Systems: Evidence from the Trust Game Using Artificial Agents with Deep Learning

Author

Listed:
  • Jason Xianghua Wu

    (School of Information Systems and Technology Management, University of New South Wales, Sydney, New South Wales 2052, Australia)

  • Yan (Diana) Wu

    (School of Global Innovation and Leadership, Lucas College and Graduate School of Business, San José State University, San José, California 95112)

  • Kay-Yut Chen

    (Department of Information Systems and Operations Management, University of Texas at Arlington, Arlington, Texas 76013)

  • Lei Hua

    (Soules College of Business, University of Texas at Tyler, Tyler, Texas 75799)

Abstract

The trust game, a simple two-player economic exchange, is extensively used as an experimental measure for trust and trustworthiness of individuals. We construct deep neural network–based artificial intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another repeatedly without any prior knowledge, assumption, or data regarding human behaviors. We find that, under certain conditions, AI agents produce actions that are qualitatively similar to decisions of human subjects reported in the trust game literature. Factors that influence the emergence and levels of cooperation by artificial agents in the game are further explored. This study offers evidence that AI agents can develop trusting and cooperative behaviors purely from an interactive trial-and-error learning process. It constitutes a first step to build multiagent-based decision support systems in which interacting artificial agents are capable of leveraging social intelligence to achieve better outcomes collectively.

Suggested Citation

  • Jason Xianghua Wu & Yan (Diana) Wu & Kay-Yut Chen & Lei Hua, 2023. "Building Socially Intelligent AI Systems: Evidence from the Trust Game Using Artificial Agents with Deep Learning," Management Science, INFORMS, vol. 69(12), pages 7236-7252, December.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:12:p:7236-7252
    DOI: 10.1287/mnsc.2023.4782
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