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An agent-based model of scapegoating

Carlos Paes | Published Thursday, August 28, 2025 | Last modified Thursday, August 28, 2025

This agent-based model investigates scapegoating as a social mechanism of crisis management. Inspired by René Girard’s mimetic theory, it simulates how individual tension accumulates and spreads across a small-world network. When tension exceeds certain thresholds, leaders emerge and accuse marginalized agents, who may attempt to transfer blame to substitutes. If scapegoating occurs, collective tension decreases, but victims become isolated while leaders consolidate temporary authority. This simulation provides a conceptual and methodological framework for exploring how collective blame, crisis contagion, and leadership paradoxes emerge in complex networks. It can also be extended with empirical data, such as social media dynamics of online harassment and virtual lynching, offering potential applications for both theoretical research and practical crisis monitoring.

This model is intended to study the way information is collectively managed (i.e. shared, collected, processed, and stored) in a system and how it performs during a crisis or disaster. Performance is assessed in terms of the system’s ability to provide the information needed to the actors who need it when they need it. There are two main types of actors in the simulation, namely communities and professional responders. Their ability to exchange information is crucial to improve the system’s performance as each of them has direct access to only part of the information they need.

In a nutshell, the following occurs during a simulation. Due to a disaster, a series of randomly occurring disruptive events takes place. The actors in the simulation need to keep track of such events. Specifically, each event generates information needs for the different actors, which increases the information gaps (i.e. the “piles” of unaddressed information needs). In order to reduce the information gaps, the actors need to “discover” the pieces of information they need. The desired behavior or performance of the system is to keep the information gaps as low as possible, which is to address as many information needs as possible as they occur.

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