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Simulating human-like decisions in a memory-based agent model

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Abstract

Agent-based models have been used to simulate complex social systems in many domains. Historically research in agent decision-making has been performed with the goal of creating an agent who acts rational. However, experimental economics has shown that human beings do not always make rational decisions. Based on Kahneman and Tversky’s descriptive theory, this paper proposes a computational agent-based model of human-like intuitive decisions and bounded rationality. A series of experiments are conducted to evaluate the concept, the model and their impacts on endowing agents with human-like decisions. Our experiments show that a selfish agent defers from the strategy of the rational agent and is more similar to human strategy.

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Authors and Affiliations

  1. Department of Computer Science, Trinity University, San Antonio, TX, 78216, USA

    Yu Zhang

  2. Pervasive Software, 12365-B Riata Trace Parkway, Austin, TX, 78727, USA

    Jason Leezer

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  1. Yu Zhang
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Correspondence to Yu Zhang.

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Zhang, Y., Leezer, J. Simulating human-like decisions in a memory-based agent model. Comput Math Organ Theory 16, 373–399 (2010). https://doi.org/10.1007/s10588-010-9077-z

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