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

Jason Xianghua Wu (), Yan (Diana) Wu (), Kay-Yut Chen () and Lei Hua ()
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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

Management Science, 2023, vol. 69, issue 12, 7236-7252

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.

Keywords: artificial intelligence; deep Q-network; interactive learning; trust; trustworthiness; social intelligence; decision support system (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:12:p:7236-7252

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