The Evolution of Quantitatively Responsive Cooperative Trade

The iterated Prisonerʹs Dilemma reflects the essence of repeated cooperative interactions with selfish incentives. However, the classical form of this game assumes that individuals either cooperate or defect, whereas in practice different degrees of cooperation are usually possible. To overcome this limitation, we present a model of alternating cooperative trade in which individuals controlled the costs they incurred in benefiting their partners. Since the range of possible strategies is enormous, competitively successful solutions were identified using a genetic algorithm, a powerful search technique in which good performers are iteratively selected and recombined from an initial “strategy soup”. Beginning with a population of asocial individuals, altruistic behaviour readily emerged. Like the pre-defined strategy of “Raise-the-Stakes”, the emerging strategies evolved protection from cheats by investing relatively little in strangers and subsequently responding quantitatively to a partnerʹs altruism. Unlike “Raise-the-Stakes”, they began trading relations at intermediate levels and, when the benefit-to-cost ratio of cooperation was relatively low, mean investment was considerably below the maximum level. Our approach is novel in allowing us to predict not just whether cooperation will occur, but how cooperative individuals will be, in relation to factors such as the number of rounds and the cost effectiveness of cooperative trade.