Information theoretic models of social interaction

This dissertation demonstrates, in a non-semantic information-theoretic framework, how the principles of “maximisation of relevant information” and “information parsimony” can guide the adaptation of an agent towards agent-agent interaction. Central to this thesis is the concept of digested information; I argue that an agent is intrinsically motivated to a.) process the relevant information in its environment and b.) display this information in its own actions. From the perspective of similar agents, who require similar information, this differentiates other agents from the rest of the environment, by virtue of the information they provide. This provides an informational incentive to observe other agents and integrate their information into one’s own decision making process. This process is formalized in the framework of information theory, which allows for a quantitative treatment of the resulting effects, specifically how the digested information of an agent is influenced by several factors, such as the agent’s performance and the integrated information of other agents. Two specific phenomena based on information maximisation arise in this thesis. One is flocking behaviour similar to boids that results when agents are searching for a location in a girdworld and integrated the information in other agent’s actions via Bayes’ Theorem. The other is an effect where integrating information from too many agents becomes detrimental to an agent’s performance, for which several explanations are provided.