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Fairness in multi-agent systems

Published online by Cambridge University Press:  01 June 2008

STEVEN DE JONG ,
KARL TUYLS  and
KATJA VERBEECK
STEVEN DE JONG
Affiliation:
MICC, Maastricht University, The Netherlands; e-mail: [email protected]
KARL TUYLS
Affiliation:
MICC, Maastricht University, The Netherlands; e-mail: [email protected] Eindhoven Technical University, The Netherlands; e-mail: [email protected]
KATJA VERBEECK
Affiliation:
Katholieke Hogeschool Sint Lieven, Gent, Belgium; e-mail: [email protected]
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Abstract

Multi-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, they strongly care about fairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 23 , Issue 2 , June 2008 , pp. 153 - 180
Copyright
Copyright © Cambridge University Press 2008

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