Incentives to Cooperate in Network Formation
We propose a mechanism based on taxes and subsidies that enhances high cooperation in evolutionary networks. The interactions among agents are based on a Prisoners' Dilemma game in which each agent plays the same strategy with its local neighbors, collects an aggregate payoff and imitates the strategy of its best neighbor. The network can be adaptive if agents are able to change their local neighborhood according to their satisfaction level and the strategy played. The condition, in order to obtain highly cooperative non-taxed networks in the long-run time, is that the initial fraction of cooperators has to be sufficiently high. Focussing on this restriction, the implementation of our mechanism produces successful results, a highly cooperative network is reached. Additionally, we observe that the mechanism slightly affects the macrostructure of networks once they have reached a sufficiently high fraction of cooperative agents, this suggests that the mechanism could be implemented only for a short finite period of time.
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Volume (Year): 28 (2006)
Issue (Month): 1 (August)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Vriend, Nicolaas J., 2006.
"ACE Models of Endogenous Interactions,"
Handbook of Computational Economics,
in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 21, pages 1047-1079
- Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
- Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
- Nobuyuki Hanaki & Alexander Peterhansl & Peter S. Dodds & Duncan J. Watts, 2007. "Cooperation in Evolving Social Networks," Management Science, INFORMS, vol. 53(7), pages 1036-1050, July.
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