IIDI Seminar: Crime and Punishment: studies in the evolution of co-operation

29/02/2012 2pm-3pm, F29

 Crime and Punishment: studies in the evolution of co-operation


Peter Ross, Emeritus Professor

Edinburgh Napier University

p.ross@napier.ac.uk

 How did co-operative behaviour evolve? Darwin was perplexed; the notion of "survival of the fittest" seemed to suggest that it shouldn't, because co-operative behaviour costs something. Theories

abound: group selection, kin selection, evolutionary games, indirect reciprocity and so on. Within the game-theoretic strand, Axelrod's famous computational studies of the Iterated Prisoner's Dilemma (ca. 1980) had suggested that co-operative behaviour could successfully invade a population of non-co-operators. However, the payoffs typically involved facilitated such an invasion: co-operators could benefit each other significantly even when they encountered each other rarely. But in the Continuous Prisoner's Dilemma, payoffs may be tiny, and co-operative behaviour then has huge difficulty invading a non-co-operative population. Shutters studied a form of third-party retaliation in which punishment of non-co-operators, even at a cost to the punisher, could promote co-operative behaviour but this depended on the network (social) structure of interactions. In particular, scale-free networks seemed to produce anomalous results.

 

This talk presents some ongoing computational studies, in collaboration with Shutters, that explore this model further.

The results presented are based on trillions of simulated encounters. Interestingly, it turns out than in this model co-operation can arise 'spontaneously' by means of small mutations even when starting with a totally non-cooperating and non-punishing population.

 
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Associated people

Emma Hart
Director of CEC
e.hart@napier.ac.uk
+44 131 455 2783
Bio-inspired Computing
The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.

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