Gossip, Sexual Recombination and the El Farol Bar: modelling the emergence of heterogeneity

Brian Arthur's `El Farol Bar' model is extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of mental models inside each agent. The evolutionary process is based on a Genetic Programming algorithm. Each gene is composed of two tree-structures: one to control its action and one to determine its communication. A detailed case-study from the simulations show how the agents have differentiated so that by the end of the run they had taken on very different roles. Thus the introduction of a flexible learning process and an expressive internal representation has allowed the emergence of heterogeneity

Modelling Socially Intelligent Agents

The perspective of modelling agents rather than using them for a specificed purpose entails a difference in approach. In particular an emphasis on veracity as opposed to efficiency. An approach using evolving populations of mental models is described that goes some way to meet these concerns. It is then argued that social intelligence is not merely intelligence plus interaction but should allow for individual relationships to develop between agents. This means that, at least, agents must be able to distinguish, identify, model and address other agents, either individually or in groups. In other words that purely homogeneous interaction is insufficient. Two example models are described that illustrate these concerns, the second in detail where agents act and communicate socially, where this is determined by the evolution of their mental models. Finally some problems that arise in the interpretation of such simulations is discussed

Capturing Social Embeddedness: a constructivist approach

A constructivist approach is applied to characterising social embeddedness and to the design of a simulation of social agents which displays the social embedding of agents. Social embeddedness is defined as the extent to which modelling the behaviour of an agent requires the inclusion of the society of agents as a whole. Possible effects of social embedding and ways to check for it are discussed briefly. A model of co-developing agents is exhibited, which is an extension of Brian Arthur's `El Farol Bar' model, but extended to include learning based upon a GP algorithm and the introduction of communication. Some indicators of social embedding are analysed and some possible causes of social embedding are discussed

Evolution of Cooperation in a Spatial Prisoner's Dilemma

We investigate the spatial distribution and the global frequency of agents who can either cooperate or defect. The agent interaction is described by a deterministic, non-iterated prisoner's dilemma game, further each agent only locally interacts with his neighbors. Based on a detailed analysis of the local payoff structures we derive critical conditions for the invasion or the spatial coexistence of cooperators and defectors. These results are concluded in a phase diagram that allows to identify five regimes, each characterized by a distinct spatiotemporal dynamics and a corresponding final spatial structure. In addition to the complete invasion of defectors, we find coexistence regimes with either a majority of cooperators in large spatial domains, or a minority of cooperators organized in small non-stationary domains or in small clusters. The analysis further allowed a verification of computer simulation results by Nowak and May (1993). Eventually, we present simulation results of a true 5-person game on a lattice. This modification leads to non-uniform spatial interactions that may even enhance the effect of cooperation. Keywords: Prisoner's dilemma; cooperation; spatial 5-person gameComment: 33 pages, 22 multipart figures, for related papers see http://www.ais.fraunhofer.de/~frank/papers.htm