Cooperation in Networked Populations of Selfish Adaptive Agents: Sensitivity to Learning Speed

Abstract

This paper investigates the evolution of cooperation in iterated Prisoner's Dilemma (IPD) games with individually learning agents, subject to the structure of the interaction network. In particular, we study how Tit-for-Tat or All-Defection comes to dominate the population on Watts-Strogatz networks, under varying learning speeds and average network path lengths. We find that the presence of a cooperative regime (where almost the entire population plays Tit-for-Tat) is dependent on the quickness of information spreading across the network. More precisely, cooperation hinges on the relation between individual adaptation speed and average path length in the interaction topology. Our results are in good agreement with previous works both on discrete choice dynamics on networks and in the evolution of cooperation literature.