COMPUTATIONAL SOCIAL SCIENCE – Modeling the Co-Evolution of Culture, Signs and Network Structure: Theory and Applications – Revay

When:
October 20, 2017 @ 3:00 pm – 4:30 pm
2017-10-20T15:00:00-04:00
2017-10-20T16:30:00-04:00
Where:
Center for Social Complexity Suite 3rd Floor, Research Hall, Fairfax Campus
Cost:
Free
Contact:
Karen Underwood
7039939298

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Peter Revay, Ph.D. Candidate
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University

Modeling the Co-Evolution of Culture, Signs and Network Structure: Theory and Applications

Friday, October 20,3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall

ABSTRACT:  I focus on the drivers of diffusion and adoption of cultural traits, such as values, beliefs, and behaviors. I adopt an evolutionary view of cultural dynamics. I use concepts from dual-inheritance theories of cultural evolution to develop and test an agent-based model capable of simulating the changing distributions of cultural traits in a large population of actors over the course of prolonged periods of time. Particularly, I pay close attention to the mechanisms of indirectly biased transmission of traits and guided variation, which are both hypothesized to be significant aspects of cultural dynamics. Indirectly biased transmission consists of the adoption of specific trait variants on the basis of possession of initially unrelated external markers. Guided variation is then individual adaptation driven by self-exploration.

Furthermore, I make use of large publicly available datasets to validate my models. The first one of these is the database of bill co-authorship in the U.S. House of Representatives from 1973 to 2008. The other is a comprehensive dataset of scientific co-authorship in various disciplines stretching back for over a century.

The results show that cultural evolution models based on indirectly biased transmission and guided variation are suitable to explaining the dynamics of various complex social networks.