A framework for studying the evolution of
cooperative behaviour in a random environment, using evolution of
finite state strategies, is presented. The interaction between
agents is modelled by a repeated game with random observable
payoffs. The agents are thus faced with a more complex situation,
compared to the Prisoner's Dilemma
that
has been widely used for investigating the conditions for
cooperation in evolving populations.
Still, there are robust cooperating strategies that usually evolve
in a population of agents. In the cooperative mode, these strategies
selects an action that allows for maximizing the payoff sum of both
players in each round, regardless of the own payoff. Two such
players maximize the expected total long-term payoff. If the
opponent deviates from this scheme, the strategy invokes a
punishment action, which aims to lower the opponent's score for the
rest of the (possibly infinitely) repeated game. The introduction of
mistakes to the game actually pushes evolution towards more
cooperative strategies even though the game becomes more difficult.