Operations, Information and Decisions Papers

Document Type

Journal Article

Date of this Version

11-2002

Publication Source

Group Decision and Negotiation

Volume

11

Issue

6

Start Page

433

Last Page

447

DOI

10.1023/A:1020687015632

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

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Date Posted: 27 November 2017

This document has been peer reviewed.