COMPUTATIONAL SOCIAL SCIENCE

Department of Computational Social Science Seminar Abstract

Friday, 13th November: 3.00pm

In Search of the Roots of Social Complexity

Chris Rouly, Department of Computational Social Science

This is a report on results from research that tested a hypothesis stating the aggregate phenomenon known to social science as clan-level social complexity can be explained computationally using the following theoretic components: human metabolic and bio-reproductive theory, nutrient seeking (foraging) behaviors, evolutionary theory, and a spatial ecology. To test this hypothesis an agent-based model was created wherein each agent had an artificial chromosome containing eleven genes (8-bits per gene) ten of which had independently inheritable, graded, and expressible traits like draught tolerance, temperature sensitivity, a robust metabolism, and improved fecundity in small-group settings, for example. The agents could move freely within a diverse 2D ecology, enjoyed caloric and water metabolic costs, and had human-like 28-day reproductive cycles with gestation and nursing metabolic adjustments. One simulation in particular ran for more than 200 agent generations of 6,421 simulated years. Reported here are results of that simulation, the changes that occurred in three of the eleven genes in the agent population under test as a consequence of artificial evolution, and how “sociality” may have begun to emerge in the population.