AgentZero++: Modeling Fear-Based Behavior

2025-10-05

AI Summary

This study investigates how micro-level cognitive heterogeneity among individuals drives the emergence of macro-level collective violence. Method: We develop a multi-agent model (implemented in Mesa) integrating cognitive, affective, and social mechanisms. Innovatively, we incorporate eight behavioral enhancement mechanisms: age-dependent impulse control, memory-based risk assessment, emotion–cognition coupling, endogenous destruction radius, fight-or-flight dynamics, affective homophily, retaliatory harm, and multi-agent coordination—explicitly modeling emotional thresholds, identity-driven behavior, and adaptive social networks. Contribution/Results: The model successfully reproduces key empirical phenomena—including protest asymmetry, conflict escalation cycles, and localized retaliation. Sensitivity analysis reveals that minor variations in memory bias, reactivity strength, and affective alignment can be substantially amplified or suppressed via positive feedback loops, thereby modulating systemic instability. This work establishes a computationally tractable, integrative paradigm linking cognition, affect, and social structure to explain the dynamics of violent collective action.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We present AgentZero++, an agent-based model that integrates cognitive, emotional, and social mechanisms to simulate decentralized collective violence in spatially distributed systems. Building on Epstein's Agent_Zero framework, we extend the original model with eight behavioral enhancements: age-based impulse control; memory-based risk estimation; affect-cognition coupling; endogenous destructive radius; fight-or-flight dynamics; affective homophily; retaliatory damage; and multi-agent coordination. These additions allow agents to adapt based on internal states, previous experiences, and social feedback, producing emergent dynamics such as protest asymmetries, escalation cycles, and localized retaliation. Implemented in Python using the Mesa ABM framework, AgentZero++ enables modular experimentation and visualization of how micro-level cognitive heterogeneity shapes macro-level conflict patterns. Our results highlight how small variations in memory, reactivity, and affective alignment can amplify or dampen unrest through feedback loops. By explicitly modeling emotional thresholds, identity-driven behavior, and adaptive networks, this work contributes a flexible and extensible platform for analyzing affective contagion and psychologically grounded collective action.

Problem

• Modeling fear-based collective violence through cognitive-emotional agents
• Simulating decentralized conflict dynamics with memory and social feedback
• Analyzing how micro-level heterogeneity shapes macro-level unrest patterns

Innovation

• Integrates cognitive, emotional, and social behavioral mechanisms
• Extends original model with eight key behavioral enhancements
• Enables modular experimentation using Python Mesa framework