Emotional Modulation in Swarm Decision Dynamics

2026-03-11 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

David Freire-Obregón from SIANI, Universidad de Las Palmas de Gran Canaria, developed an agent-based model that extends the "bee equation" to incorporate emotional valence (positive-negative) and arousal (low-high) as modulators of interaction rates in collective decision-making. This model simulates how emotional states, represented by facial expressions, influence recruitment and cross-inhibition parameters, thereby affecting consensus formation. The study explored three scenarios: the combined effect of valence and arousal on consensus outcomes and speed, arousal's role in breaking ties when valence is matched, and the "snowball" effect where consensus accelerates after intermediate support thresholds are surpassed. Results indicate that emotional modulation can bias decision outcomes and alter convergence times by shifting effective recruitment and inhibition rates, while intrinsic non-linear amplification can also drive decisive wins even in emotionally symmetric conditions. The framework bridges swarm decision theory with affective and social modeling.

Key takeaway

For AI Researchers and Research Scientists developing collective intelligence systems, understanding how emotional valence and arousal modulate decision dynamics is crucial. Your models should consider integrating affective states to predict and influence group outcomes more accurately, especially in scenarios requiring rapid consensus or tie-breaking. This approach can inform strategies for distributed robotics or online community management, where emotional cues can act as effective control levers.

Key insights

Emotional valence and arousal significantly modulate collective decision dynamics by altering recruitment and inhibition rates.

Principles

• Higher arousal amplifies persuasive impact.
• Valence and arousal interact non-linearly.
• Small advantages can snowball into dominance.

Method

An agent-based model extends the bee equation, mapping agent valence-arousal states to simulated facial expressions that modulate recruitment and inhibition rates, and includes emotional contagion.

In practice

• Adjust emotional profiles to bias group outcomes.
• Accelerate or delay consensus via affective states.
• Influence stability of competing states emotionally.

Topics

• Swarm Decision Dynamics
• Emotional Modulation
• Agent-Based Models
• Bee Equation
• Valence-Arousal Model

Best for: AI Researcher, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.