Emotional Modulation in Swarm Decision Dynamics

TL;DR

This paper extends the classical swarm decision model by incorporating emotional states, demonstrating how emotions influence consensus speed, bias outcomes, and create non-linear amplification effects in collective decision-making.

Contribution

It introduces an agent-based model that integrates emotional valence and arousal into swarm decision dynamics, linking affective states with recruitment and inhibition processes.

Findings

Emotional modulation biases decision outcomes.

Arousal influences the speed of consensus formation.

Non-linear effects can produce decisive outcomes even with symmetric emotions.

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.