Evolution
Among the problems in studying evolution is that it consists of a possibly unique and unrepeatable sequence of events, leaving records that are often sparse and open to multiple interpretations. However, computer simulations enable experiments on evolution, with access to complete records. We have begun to explore the evolution of ethnocentrism with agent-based computational models that allow for reproduction with mutation. Simple computer agents interact with each other in non-zero-sum games (e.g., Prisoner’s Dilemma) and these interactions modulate their reproductive fitness. Under certain assumptions and from a neutral start, simulations show that an ethnocentric strategy, involving cooperation within group and defection against agents from other groups, comes to comprise about 75% of a population. We investigated the underlying dynamics of these results, explaining the occurrence of early humanitarian stages (universal cooperation), eventual ethnocentric dominance, and generally poor evolutionary performance of selfish (cooperate with no one) and traitorous (cooperate only with other groups) strategies. This GIF movie playable in Quicktime, from colleague Max Hartshorn, shows sample results on a 25 x 25 lattice. The movie shows an evolving world in which Humanitarians dominate early but are eventually overcome by Ethnocentrics. Means and SEs of strategy frequencies averaged across 50 simulated worlds are plotted here.
Current interests focus on discovering the basic principles underlying the evolution of cooperation, and attempts to increase cooperation across groups of innately ethnocentric agents.
In other work, we are investigating interactions between learning and evolution, particularly the natural selection of learning mechanisms such as individual learning, imitation, and theory passing. The last of these may be unique to humans and might explain the ratchet effect in human cultural transmission that could be the basis of the elusive human spark that sets us apart from other species. In a Bayesian implementation of theory passing, the teacher’s posterior probability distribution becomes the student’s prior probability distribution.
Current interests focus on discovering the basic principles underlying the evolution of cooperation, and attempts to increase cooperation across groups of innately ethnocentric agents.
In other work, we are investigating interactions between learning and evolution, particularly the natural selection of learning mechanisms such as individual learning, imitation, and theory passing. The last of these may be unique to humans and might explain the ratchet effect in human cultural transmission that could be the basis of the elusive human spark that sets us apart from other species. In a Bayesian implementation of theory passing, the teacher’s posterior probability distribution becomes the student’s prior probability distribution.
