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2018 | OriginalPaper | Chapter

Compromise as a Way to Promote Convention Emergence and to Reduce Social Unfairness in Multi-Agent Systems

Authors : Shuyue Hu, Ho-fung Leung

Published in: AI 2018: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Recently, the study of social conventions has attracted much attention. We notice that different agents may tend to establish different conventions, even though they share common interests in convention emergence. We model such scenarios to be competitive-coordination games. We hypothesize that agents may fail to establish a convention under these scenarios and introducing the option of compromise may help solve this problem. Experimental study confirms this hypothesis. In particular, it is shown that besides convention emergence is promoted, the undesirable social unfairness is also significantly reduced. In addition, we discuss how the reward of coordination via compromise affects convention emergence, social efficiency and unfairness.

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next chapter Hierarchical Population-Based Learning for Optimal Large-Scale Coalition Structure Generation in Smart Grids

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Title: Compromise as a Way to Promote Convention Emergence and to Reduce Social Unfairness in Multi-Agent Systems
Authors: Shuyue Hu
Ho-fung Leung
Publisher: Springer International Publishing
Book: AI 2018: Advances in Artificial Intelligence
Print ISBN: 978-3-030-03990-5

Electronic ISBN: 978-3-030-03991-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-03991-2_1

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