Modelling exchange using the prisoner’s dilemma and genetic programming

In this paper we show how exchange, co-operation and other complex strategies found in nature can be modelled using the prisoner’s dilemma game and genetic programming. We are able to produce and evolve different strategies represented by computer programs that can play the prisoner’s dilemma against a set of predefined strategies or against other programs in the population (co-evolution). Although the game is simple the number of possible strategies for playing it is huge. Genetic programming provides an efficient search mechanism capable of identifying and propagating strategies that do well in a particular environment. Our implementation provides a distinct advantage over previous investigations into the prisoner’s dilemma using genetic algorithms. In particular strategies can be based upon the entire history of a game at any point, rather than on recent moves only. We incorporate the use of list data structures as terminals and provide list-searching capability in the function set so that potentially large volumes of data can be utilised by the evolved programs.