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AgentZero++: Modeling Fear-Based Behavior

Vrinda Malhotra, Jiaman Li, Nandini Pisupati

TL;DR

AgentZero++ extends Epstein's Agent_Zero by embedding affective learning, probabilistic risk estimation, and social contagion within a $50×50$ toroidal grid to model fear-based collective violence. Implemented in Python with the Mesa ABM framework, the model introduces eight cognitively grounded extensions (including age-based impulse control, memory, endogenous destructive radius, flight-or-fight dynamics, retaliatory damage, affect-cognition coupling, affective homophily, and multi-agent expansion) that enable modular experiments and visualization of micro-to-macro dynamics. Results show that small changes in memory length, affective alignment, and impulse control can trigger tipping points, escalation cycles, and localized retaliation via feedback loops, with flight behavior mitigating damage and longer memory sustaining activation. By explicitly modeling internal thresholds, identity-driven behavior, and adaptive networks, AgentZero++ provides a flexible platform for studying affective contagion, decentralized collective action, and resilience of social systems under emotional stress.

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.

AgentZero++: Modeling Fear-Based Behavior

TL;DR

AgentZero++ extends Epstein's Agent_Zero by embedding affective learning, probabilistic risk estimation, and social contagion within a toroidal grid to model fear-based collective violence. Implemented in Python with the Mesa ABM framework, the model introduces eight cognitively grounded extensions (including age-based impulse control, memory, endogenous destructive radius, flight-or-fight dynamics, retaliatory damage, affect-cognition coupling, affective homophily, and multi-agent expansion) that enable modular experiments and visualization of micro-to-macro dynamics. Results show that small changes in memory length, affective alignment, and impulse control can trigger tipping points, escalation cycles, and localized retaliation via feedback loops, with flight behavior mitigating damage and longer memory sustaining activation. By explicitly modeling internal thresholds, identity-driven behavior, and adaptive networks, AgentZero++ provides a flexible platform for studying affective contagion, decentralized collective action, and resilience of social systems under emotional stress.

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.

Paper Structure

This paper contains 18 sections, 6 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Spatial Environment
  5. Internal Agent Architecture
  6. Simulation Loop and Logic
  7. Behavioral Extensions
  8. Age and Impulse Control
  9. Endogenous Destructive Radius
  10. Flight vs. Fight Dynamics
  11. Temporal Memory
  12. Retaliatory Damage
  13. Coupling of Affect and Cognition
  14. Affective Homophily and Endogenous Network Dynamics
  15. Expansion to Multiple Agents
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Netlogo Model: Activation by Dispositional Contagion.
  • Figure 2: Sample of fight vs. flight dynamics shows the difference in coverage.
  • Figure 3: Our basic model with five agents and the following additions: age and impulse control, endogenous destructive radius, and retaliatory damage.
  • Figure 4: Temporal Memory Trends
  • Figure 5: Coupling Effect over Affect and Contagion.
  • ...and 2 more figures