On the logic of revolution: Strategic games and the fall of regimes

Date

2025

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

This thesis develops and tests a formal game-theoretic model of regime change, treating political revolution as an n-player strategic interaction under uncertainty. The model frames individual decisions as binary choices: act against the regime or maintain the status quo. Each player’s choice is shaped by perceived payoffs, beliefs about regime strength, and expectations of others’ actions. Five payoff structures—Functioning Regime, Free-Rider Dilemma, Unacceptable Regime, Stag Hunt, and Weak and Unacceptable Regime—capture the range of strategic environments encountered in real-world revolutions. The model accounts for bounded rationality, limited or incorrect information, and selective incentives, allowing for heterogeneous conditions across a population at a single point in time. To evaluate the model, the Russian Revolutions of 1905 and 1917 are analyzed as historical case studies. These events demonstrate how shifts in perceived payoffs, triggered by economic shocks, war, and state repression, altered the strategic calculus of individuals and groups. As public perceptions of regime strength declined, and opposition organizations improved their capacity to coordinate and incentivize participation, the population transitioned into payoff-dominant conditions conducive to regime collapse. The thesis contributes a unified framework for modeling regime change that integrates coordination theory, collective action problems, and information dynamics. By validating the model against empirical data, it offers insights for researchers, policymakers, and stakeholders seeking to understand or anticipate large-scale political transitions.

Description

Keywords

Game theory, Regime change, Collective action, Russian Revolution

Graduation Month

May

Degree

Master of Science

Department

Department of Industrial & Manufacturing Systems Engineering

Major Professor

Jessica L. Heier Stamm

Date

Type

Thesis

Citation