Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/47900
Título: | Learning to be fair in multiplayer Ultimatum Games |
Autor(es): | Santos, Fernando P. Santos, Francisco C. Melo, Francisco Paiva, Ana Pacheco, Jorge Manuel Santos |
Palavras-chave: | Fairness Groups Learning Multiagent systems Ultimatum Game |
Data: | Jan-2016 |
Editora: | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
Revista: | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
Resumo(s): | We study a multiplayer extension of the well-known Ultimatum Game (UG) through the lens of a reinforcement learning algorithm. Multiplayer Ultimatum Game (MUG) allows us to study fair behaviors beyond the traditional pairwise interaction models. Here, a proposal is made to a quorum of Responders, and the overall acceptance depends on reaching a threshold of individual acceptances. We show that learning agents coordinate their behavior into different strategies, depending on factors such as the group acceptance threshold and the group size. Overall, our simulations show that stringent group criteria trigger fairer proposals and the effect of group size on fairness depends on the same group acceptance criteria. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/47900 |
ISBN: | 9781450342391 |
ISSN: | 1548-8403 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | DBio - Comunicações/Communications in Congresses DMA - Outros trabalhos de investigação |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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197-RUM.pdf Acesso restrito! | 835,05 kB | Adobe PDF | Ver/Abrir |