Hierarchy Formation in Agent Based Models
The emergence and dynamic evolution of hierarchies of
various kinds are not adequately described by existing models. One
of the promises of computational social science is that it could provide
a means through which such phenomena can be better understood. In this
talk I will provide examples of models in which such emergence can be observed
and describe a framework for using computational experiments to build a theoretical
understanding of these phenomena. That is, the goal of this research
is not just interesting models, but a rigorous means for making inferences
about social systems using such models.
There are various sorts of social hierarchies that are potentially of interest,
including those based on economic relationships, personal bonds, or communication.
But most central to the future of complexity science is a better understanding
of the formation of adaptive hierarchies. Examples of this phenomenon
appear in biology in connection with the appearance of eukaryotes, multi-celled
animals, and in speciation. Similar questions of how adaptive groups
emerge from the action of individuals occur in social science across a range
of scales, from small tribal groupings to the formation of nation states.
In this talk I will exhibit simple models that exhibit
hierarchy formation. While interesting in their own right, the focus
of this talk is demonstrating an approach to creating social science theory
through modeling experiments.
Steve Bankes
Evolving Logic
RAND Graduate School
www.evolvinglogic.com
bankes@evolvinglogic.com