情绪如何影响群体决策?该研究用模拟蜜蜂行为的模型揭示了情绪对共识形成的关键作用。
Emotional Modulation in Swarm Decision Dynamics
- 引入情绪维度(正负、高低唤醒)调节个体间互动速率,改变决策过程中的招募与抑制参数。
- 情绪可加速或延迟共识形成,且在对称情绪条件下仍能引发决定性结果。
- 适用于研究生物群体、社交网络及人机协作中的情感驱动决策机制。
生物与人类群体的集体决策常源于简单交互规则,使微小差异放大为共识。蜂群决策方程最初用于描述蜜蜂选巢行为,通过招募与抑制过程体现这一动态。本文将其拓展为基于智能体的模型,引入情绪效价(正/负)和唤醒度(低/高)作为交互速率的调节因子,有效改变招募与交叉抑制参数。智能体根据其情绪状态生成模拟面部表情,用于研究情绪传染在共识形成中的作用。研究探索三种情景:(1) 效价与唤醒度共同影响共识结果与速度;(2) 唤醒度在效价相同时打破僵局的作用;(3) 当支持率突破中间阈值后出现的“雪球效应”——共识加速形成。结果表明,情绪调节可偏移决策结果并改变收敛时间,通过调整有效招募与抑制速率实现。同时,内在非线性放大机制可在完全对称情绪条件下仍产生决定性胜利。研究将经典群体决策理论与情感及社会建模结合,揭示情绪不对称与结构性临界点如何共同塑造集体结果。所提框架为研究自然与人工系统中集体选择的情感维度提供了灵活工具。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。