Centralized versus Personalized Commitments and Their Influence on Cooperation in Group Interactions

Authors

  • The Anh Han Teesside University
  • Luis Moniz Pereira Universidade Nova de Lisboa
  • Luis A. Martinez-Vaquero Institute of Cognitive Sciences and Technologies (ISTC-CNR)
  • Tom Lenaerts Université libre de Bruxelles

DOI:

https://doi.org/10.1609/aaai.v31i1.10704

Keywords:

Commitment, Cooperation, Evolutionary Game Theory, Public Goods

Abstract

Before engaging in a group venture agents may seek commitments from other members in the group and, based on the level of participation (i.e. the number of actually committed participants), decide whether it is worth joining the venture. Alternatively, agents can delegate this costly process to a (beneficent or non-costly) third-party, who helps seek commitments from the agents. Using methods from Evolutionary Game Theory, this paper shows that, in the context of Public Goods Game, much higher levels of cooperation can be achieved through such centralized commitment management. It provides a more efficient mechanism for dealing with commitment free-riders, those who are not willing to bear the cost of arranging commitments whilst enjoying the benefits provided by the paying commitment proposers. We show that the participation level plays a crucial role in the decision of whether an agreement should be formed; namely, it needs to be more strict in terms of the level of participation required from players of the centralized system for the agreement to be formed; however, once it is done right, it is much more beneficial in terms of the level of cooperation and social welfare achieved. In short, our analysis provides important insights for the design of multi-agent systems that rely on commitments to monitor agents' cooperative behavior.

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Published

2017-02-12

How to Cite

Han, T. A., Moniz Pereira, L., Martinez-Vaquero, L. A., & Lenaerts, T. (2017). Centralized versus Personalized Commitments and Their Influence on Cooperation in Group Interactions. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10704

Issue

Section

AAAI Technical Track: Multiagent Systems