Cooperative Agent Systems: Artificial Agents Play the Ultimatum Game

Fang Zhong (), Steven O. Kimbrough and D.J. Wu
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Fang Zhong: LeBow College of Business, Drexel University
Steven O. Kimbrough: University of Pennsylvania
D.J. Wu: LeBow College of Business, Drexel University

Group Decision and Negotiation, 2002, vol. 11, issue 6, No 2, 433-447

Abstract: Abstract We explore computational approaches for artificial agents to play the ultimatum game. We compare our agents' behavior with that predicted by classical game theory, as well as behavior found in experimental (or behavioral) economics investigations. In particular, we study the following questions: How do artificial agents perform in playing the ultimatum game against fixed rules, dynamic rules, and rotating rules? How do coevolving artificial agents perform? Will learning software agents do better? What is the value of intelligence? What will happen when smart learning agents play against dumb (no-learning) agents? What will be the impact of agent memory size on performance? This exploratory study provides experimental results pertaining to these questions.

Keywords: artificial agents; cooperative agent systems; reinforcement learning; ultimatum game (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1023/A:1020687015632

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