Joining direct and indirect reciprocity

Context

Human being is the social animal par excellence. An individual can help another even if it is the first time they meet or if they know that they will never meet again. Several mechanisms have been proposed to explain cooperation between unrelated individuals in Evolutionary Game Theory. Among them, reciprocity, either direct or indirect, stands as one of the most successful explanations of altruism. In direct reciprocity individuals pay back the help received in previous encounters with the same partner (“I help you because you help me”). In society, however, many interactions have low chances to be repeated with the same individual. To explain altruism in those interactions, the concept of indirect reciprocity was introduced. In the standard approach of this mechanism, individuals observe the others interactions and assign reputations to them. These reputation are used to decide future actions ("I help you because I saw you helping another").
 

Goal

Real life observations suggest that individuals actually use a combination of direct and indirect reciprocity to make their decisions. These mechanisms has been analysed independently, however very few studies combined both together. We would like to develop a new framework, mainly through agent-based simulations, where individuals choose between using direct reciprocity, when they can remember previous iterations with her co-player, or indirect reciprocity, when she cannot remember it. The final goal is to study under which conditions and characteristics of the population, it is more efficient for individuals trust more in their own experience or in the collective memory.
 

References

Sigmund K. (2010), The Calculus of Selfishness. Princeton: Princeton University Press.
Nowak M.A. (2006), Five rules for the evolution of cooperation. Science 314: 1560–1563.
H. Ohtsuki and Y. Iwasa (2004), How should we define goodness? reputation dynamics in indirect reciprocity J. Theor. Biol. 231, 107.

 

Contact

Contact Luis A. Martinez or Tom Lenaerts if you are interested in this project.