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Static network structure can stabilize human cooperation

  1. Nicholas A. Christakisd,j,k,l
  1. Departments of aPsychology,
  2. bEconomics,
  3. jSociology,
  4. kMedicine, and
  5. lEcology and Evolutionary Biology,
  6. cSchool of Management, and
  7. dYale Institute for Network Science, Yale University, New Haven, CT 06511;
  8. eProgram for Evolutionary Dynamics and
  9. Departments of fMathematics and
  10. gOrganismic Biology, Harvard University, Cambridge, MA 02138; and
  11. hMedical Genetics Division and
  12. iPolitical Science Department, University of California, San Diego, CA 92093

  1. Edited by Jose A. Scheinkman, Columbia University, New York, NY, and approved October 30, 2014 (received for review January 9, 2014)

Significance

Human populations are both extremely cooperative and highly structured. Mathematical models have shown that fixed network interaction structures can lead to cooperation under certain conditions, by allowing cooperators to cluster together. Here, we provide empirical evidence of this phenomenon. We explore how different fixed social network structures can promote cooperation using economic game experiments. We find that people cooperate at high stable levels, as long as the benefits created by cooperation are larger than the number of neighbors in the network. This empirical result is consistent with a rule predicted by mathematical models of evolution. Our findings show the important role social networks can play in human cooperation and provide guidance for promoting cooperative behavior.

Abstract

The evolution of cooperation in network-structured populations has been a major focus of theoretical work in recent years. When players are embedded in fixed networks, cooperators are more likely to interact with, and benefit from, other cooperators. In theory, this clustering can foster cooperation on fixed networks under certain circumstances. Laboratory experiments with humans, however, have thus far found no evidence that fixed network structure actually promotes cooperation. Here, we provide such evidence and help to explain why others failed to find it. First, we show that static networks can lead to a stable high level of cooperation, outperforming well-mixed populations. We then systematically vary the benefit that cooperating provides to one’s neighbors relative to the cost required to cooperate (b/c), as well as the average number of neighbors in the network (k). When b/c > k, we observe high and stable levels of cooperation. Conversely, when b/c ≤ k or players are randomly shuffled, cooperation decays. Our results are consistent with a quantitative evolutionary game theoretic prediction for when cooperation should succeed on networks and, for the first time to our knowledge, provide an experimental demonstration of the power of static network structure for stabilizing human cooperation.

Footnotes

  • ↵1To whom correspondence should be addressed. Email: david.rand{at}yale.edu.

Freely available online through the PNAS open access option.

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