Modeling Social and Political Ecosystems

Heather Zinn Brooks

Harvey Mudd College

"An opinion reproduction number for infodemics in a bounded-confidence content-spreading process on networks"

We study the spreading dynamics of content on networks. Our content-spreading model, which one can also interpret as an independent-cascade model, introduces a twist into bounded-confidence models of opinion dynamics by using bounded confidence for the content spread itself. We define an analog of the basic reproduction number from disease dynamics that we call an opinion reproduction number. A critical value of the opinion reproduction number indicates whether or not there is an “infodemic” (i.e., a large content-spreading cascade) of content that reflects a particular opinion. By determining this critical value, one can determine whether or not an opinion dies off or propagates widely as a cascade in a population of agents. Using configuration-model networks, we quantify the size and shape of content dissemination by calculating a variety of summary statistics, and we illustrate how network structure and spreading-model parameters affect these statistics.

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Olivia Chu

Bryn Mawr College

"Adaptive network models and the dynamics of political polarization and social activism"

The formation of activist groups can spark social movements, coalitions, and revolutions. The creation of such groups can be influenced by social ties, network structure, ideology and culture, and the institutional environment. Still, the relative importance of these factors, the mechanisms through which individuals develop or lose their commitment to various causes, and the channels through which like-minded individuals find each other and establish social connections are not thoroughly understood. In this work, we develop a theory that begins to explain two phenomena: 1) how a potential activist's conviction co-evolves with their social network, and 2) how 'socially-mobilizable activist networks' tend to arise or disappear based on the distribution of potential activists and overall environment. We illustrate this theory by modifying the adaptive voter model (AVM) with a conviction variable, which represents the strength with which an individual holds on to their beliefs and the comfort of holding on to them in their surroundings, encapsulating the co-evolutionary dynamics of networks and attitudes. As is expected from empirical evidence, we find that activists are systematically discouraged by exposure to disengaged individuals. However, some situations with increased interaction payoffs and strong homophily preferences favor the formation and persistence of activist networks.

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Alexandria Volkening

Purdue University

" Forecasting U.S. elections with compartmental models of infection"

Election dynamics are a rich complex system, and forecasting U.S. elections is a high-stakes problem with many sources of subjectivity and uncertainty. In this talk, I take a dynamical-systems perspective on election forecasting, with the goal of helping to shed light on choices in this process and raising questions for future work. By adapting a Susceptible-Infected-Susceptible model to account for interactions between voters in different states, I will show how to combine a compartmental approach with polling data to produce forecasts of senatorial, gubernatorial, and presidential elections at the state level. Our results for the last two decades of U.S. elections are largely in agreement with those of popular analysts, and we correctly called all of the state-level outcomes of the 2024 U.S. presidential race. We use our modeling framework to determine how weighting polling data by polling organization affects our forecasts, and explore how our forecast accuracy changes in time in the months leading up to each election.

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David Sabin-Miller

University of Michigan

"Data-driven modeling of US information-ideological dynamics"

We may view the ideological ecosystem as an interplay between individuals’ acceptance and rejection of political ideas, and the algorithmically-mediated information environment which supplies those ideas according to each individual’s preference. This framework may help us make sense of the frustrating coexistence of seemingly contradictory worldviews in today’s polarized ideological climate; each may seem totally nonsensical or irrational to the opposing side, leaving little room for productive discourse or compromise. However, with fresh eyes and an interdisciplinary mindset, it is possible to make useful progress on this classically social-science domain by seeking an underlying dynamical model supported by data. This talk will present recent empirical results from a purpose-built ideological survey which find robust and seemingly universal patterns in individual-level political reasoning, a quantitative estimate of the political information landscape, and the implications of dynamically connecting the two. These efforts point to further illuminative data-gathering possibilities, laying the groundwork for a theory-experiment loop towards accurately understanding this powerful aspect of modern society.

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