Emotional Modulation in Swarm Decision Dynamics
2026-03-10 • Multiagent Systems
Multiagent SystemsArtificial Intelligence
AI summaryⓘ
The authors expanded a mathematical model originally used to explain how honeybee swarms choose new nests by adding emotions like feelings and excitement to the mix. In their simulation, agents show emotions through facial expressions that influence how strongly they convince others or block opposing views. They tested how these emotions affect how fast and which option a group agrees on, finding that feelings can tip decisions and speed up agreement, but sometimes the group's own dynamics make decisions clear even without emotional differences. This work connects ideas about swarm behavior with how emotions influence group choices.
Collective decision-makingBee equationAgent-based modelEmotional valenceArousalRecruitmentCross-inhibitionEmotional contagionConsensus formationNon-linear amplification
Authors
David Freire-Obregón
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.