AgentZero++: Modeling Fear-Based Behavior (arxiv.org)

Researchers introduced AgentZero++, an agent-based model that extends Joshua Epstein’s Agent_Zero to simulate fear- and emotion-driven collective violence in spatially distributed populations. The paper adds eight behavioral mechanisms—age-based impulse control, memory-based risk estimation, affect–cognition coupling, an endogenous destructive radius, fight-or-flight dynamics, affective homophily, retaliatory damage, and multi-agent coordination—so agents adapt from internal states, prior experiences, and social feedback. Implemented in Python with the Mesa ABM framework, the platform is modular, visualizable, and comes with code and demos for experimenting with parameterized scenarios. AgentZero++ is significant because it links psychologically grounded micro‑level heterogeneity (memory, reactivity, emotional thresholds, identity alignment) to macro‑level outcomes such as protest asymmetries, escalation cycles, and localized retaliation through feedback loops. Key technical implications include sensitivity of emergent unrest to small changes in memory length, affective alignment, and network connectivity; explicit modeling of affective contagion and retaliatory dynamics; and support for testing interventions or counterfactuals in a controlled, reproducible simulation environment. The model offers researchers and policymakers a flexible testbed to study how cognitive and emotional mechanisms interact with social structure to produce collective action, while enabling targeted experiments on mitigation and escalation pathways.