Emotional Modulation in Swarm Decision Dynamics

2026-03-10

AI Summary

This study investigates how emotion influences the formation of group consensus, with a focus on its roles in recruitment and inhibition mechanisms. By incorporating emotional valence and arousal into a swarm decision-making model, the authors develop an extended agent-based framework that integrates simulated facial expressions to model emotional contagion. For the first time, emotional dimensions are embedded into the classical swarm equations, revealing that collective decisions are jointly driven by emotional asymmetry and nonlinear amplification within the system. The results demonstrate that emotional modulation can significantly bias decision outcomes and alter convergence speed; notably, even under emotionally symmetric conditions, the system achieves rapid consensus through a nonlinear “snowball effect.”

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Collective decision-making in biological and human groups often emerges from simple interaction rules that amplify minor differences into consensus. The bee equation, developed initially to describe nest-site selection in honeybee swarms, captures this dynamic through recruitment and inhibition processes. Here, we extend the bee equation into an agent-based model in which emotional valence (positive-negative) and arousal (low-high) act as modulators of interaction rates, effectively altering the recruitment and cross-inhibition parameters. Agents display simulated facial expressions mapped from their valence-arousal states, allowing the study of emotional contagion in consensus formation. Three scenarios are explored: (1) the joint effect of valence and arousal on consensus outcomes and speed, (2) the role of arousal in breaking ties when valence is matched, and (3) the "snowball effect" in which consensus accelerates after surpassing intermediate support thresholds. Results show that emotional modulation can bias decision outcomes and alter convergence times by shifting effective recruitment and inhibition rates. At the same time, intrinsic non-linear amplification can produce decisive wins even in fully symmetric emotional conditions. These findings link classical swarm decision theory with affective and social modelling, highlighting how both emotional asymmetries and structural tipping points shape collective outcomes. The proposed framework offers a flexible tool for studying the emotional dimensions of collective choice in both natural and artificial systems.

Problem

• emotional modulation
• swarm decision-making
• collective consensus
• valence-arousal
• emotional contagion

Innovation

• emotional modulation
• swarm decision-making
• agent-based modeling
• emotional contagion
• nonlinear amplification