Reciprocity in evolving social networks

We study the viability of conditional cooperation in a dynamically evolving social network. The network possesses the small world property, with high clustering coefficient but low characteristic path length. The interaction among linked individuals takes the form of a multiperson prisoners’ dilemma, and actions can be conditioned on the past behavior of one’s neighbors. Individuals adjust their strategies based on performance within their neighborhood, and both strategies and the network itself are subject to random perturbation. We find that the long-run frequency of cooperation is higher under the following conditions: (i) the interaction radius is neither too small nor too large, (ii) clustering is high and characteristic path length low, (iii) the mutation rate of strategies is small, and (iv) the rate of adjustment in strategies is neither too fast nor too slow.

Rajiv SethiEmail: