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Group Decision and Negotiation
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.
artificial agents, cooperative agent systems, reinforcement learning, ultimatum game
Zhong, F., Kimbrough, S. O., & Wu, D. (2002). Cooperative Agent Systems: Artificial Agents Play the Ultimatum Game. Group Decision and Negotiation, 11 (6), 433-447. http://dx.doi.org/10.1023/A:1020687015632
Date Posted: 27 November 2017
This document has been peer reviewed.