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A Model of Human Cooperation in Social Dilemmas

Overview of attention for article published in PLOS ONE, August 2013
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Title
A Model of Human Cooperation in Social Dilemmas
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0072427
Pubmed ID
Authors

Valerio Capraro

Abstract

Social dilemmas are situations in which collective interests are at odds with private interests: pollution, depletion of natural resources, and intergroup conflicts, are at their core social dilemmas. Because of their multidisciplinarity and their importance, social dilemmas have been studied by economists, biologists, psychologists, sociologists, and political scientists. These studies typically explain tendency to cooperation by dividing people in proself and prosocial types, or appealing to forms of external control or, in iterated social dilemmas, to long-term strategies. But recent experiments have shown that cooperation is possible even in one-shot social dilemmas without forms of external control and the rate of cooperation typically depends on the payoffs. This makes impossible a predictive division between proself and prosocial people and proves that people have attitude to cooperation by nature. The key innovation of this article is in fact to postulate that humans have attitude to cooperation by nature and consequently they do not act a priori as single agents, as assumed by standard economic models, but they forecast how a social dilemma would evolve if they formed coalitions and then they act according to their most optimistic forecast. Formalizing this idea we propose the first predictive model of human cooperation able to organize a number of different experimental findings that are not explained by the standard model. We show also that the model makes satisfactorily accurate quantitative predictions of population average behavior in one-shot social dilemmas.

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Geographical breakdown

Country Count As %
United States 3 2%
Japan 2 1%
France 1 <1%
Hungary 1 <1%
United Kingdom 1 <1%
Unknown 153 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 30%
Student > Master 29 18%
Student > Bachelor 22 14%
Researcher 18 11%
Student > Doctoral Student 9 6%
Other 18 11%
Unknown 17 11%
Readers by discipline Count As %
Psychology 35 22%
Economics, Econometrics and Finance 20 12%
Social Sciences 18 11%
Computer Science 13 8%
Agricultural and Biological Sciences 9 6%
Other 43 27%
Unknown 23 14%