Please use this identifier to cite or link to this item: http://hdl.handle.net/10077/9677
Title: Learning with whom to Interact: A Public Good Game on a Dynamic Network
Authors: Greiff, Matthias
Keywords: Dynamic networksevolutionary game theorypublic goodsreinforcement learningsocial networks
Issue Date: 2013
Publisher: EUT Edizioni Università di Trieste
Source: Matthias Greiff, "Learning with whom to Interact: A Public Good Game on a Dynamic Network", in: Etica & Politica / Ethics & Politics, XV (2013) 2, pp. 58–81
Series/Report no.: Etica & Politica / Ethics & Politics
XV (2013) 2
Abstract: We use a public good game with rewards, played on a dynamic network, to illustrate how self-organizing communities can achieve the provision of a public good without a central authority or privatization. Given that rewards are given to contributors and that the choice of whom to reward depends on social distance, free-riders will be excluded from rewards and the (almost efficient) provision of a public good becomes possible. We review the related experimental economics literature and illustrate how the model can be tested in the laboratory.
URI: http://hdl.handle.net/10077/9677
ISSN: 1825-5167
Appears in Collections:Etica & Politica / Ethics & Politics (2013) XV/2

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