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Letters to Nature

Nature 414, 441-443 (22 November 2001) | doi:10.1038/35106555; Received 3 August 2001; Accepted 17 September 2001

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Evolution of cooperation without reciprocity

Rick L. Riolo1, Michael D. Cohen2 & Robert Axelrod3

  1. Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan 48109, USA
  2. School of Information, University of Michigan, Ann Arbor, Michigan 48109, USA
  3. Gerald R. Ford School of Public Policy, University of Michigan, Ann Arbor, Michigan 48109, USA

Correspondence to: Rick L. Riolo1 Correspondence and requests for materials should be addressed to R.L.R. (e-mail: Email: rlriolo@umich.edu).

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A long-standing problem in biological and social sciences is to understand the conditions required for the emergence and maintenance of cooperation in evolving populations. For many situations, kin selection1 is an adequate explanation, although kin-recognition may still be a problem. Explanations of cooperation between non-kin include continuing interactions that provide a shadow of the future (that is, the expectation of an ongoing relationship) that can sustain reciprocity2, 3, 4, possibly supported by mechanisms to bias interactions such as embedding the agents in a two-dimensional space4, 5, 6 or other context-preserving networks7. Another explanation, indirect reciprocity8, applies when benevolence to one agent increases the chance of receiving help from others. Here we use computer simulations to show that cooperation can arise when agents donate to others who are sufficiently similar to themselves in some arbitrary characteristic. Such a characteristic, or 'tag', can be a marking, display, or other observable trait. Tag-based donation can lead to the emergence of cooperation among agents who have only rudimentary ability to detect environmental signals and, unlike models of direct3, 4 or indirect reciprocity9, 10, no memory of past encounters is required.

  1. Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan 48109, USA
  2. School of Information, University of Michigan, Ann Arbor, Michigan 48109, USA
  3. Gerald R. Ford School of Public Policy, University of Michigan, Ann Arbor, Michigan 48109, USA

Correspondence to: Rick L. Riolo1 Correspondence and requests for materials should be addressed to R.L.R. (e-mail: Email: rlriolo@umich.edu).