Center for the Study of Complex Systems, University of Michigan
Tuesday, October 4, 2022
Weiser Hall Room 747 and
CSCS (all caps)
Link to full event listingAbstract: Modeling the dynamics of political ideology can help us understand societal issues like polarization, which affect the evolution of many systems of power. This talk will go over our modeling framework, which utilizes a network-free system of determining political influence and a local-attraction, distal-repulsion dynamic for reaction to perceived content. Our approach allows for the incorporation of intergroup bias such that messages from trusted in-group sources enjoy greater leeway than out-group ones. We are able to extrapolate these nonlinear microscopic dynamics to macroscopic population distributions by tying them to inputs from systematically biased, probabilistic environments. The framework we put forward can reproduce both real-world political distributions and experimentally observed dynamics, and—importantly—is amenable to further refinement as more data becomes available.