Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of the best-of-n problem in dynamic environments, in the presence of two options (\(n=2\)). Site qualities can be directly measured by agents, and we introduce abrupt changes to these qualities. We introduce two adaptation mechanisms to deal with dynamic site qualities: stubborn agents and spontaneous opinion switching. Using both computer simulations and ordinary differential equation models, we show that: (i) The mere presence of the stubborn agents is enough to achieve adaptability, but increasing its number has detrimental effects on the performance; (ii) the system adaptation increases with increasing swarm size, while it does not depend on agents’ density, unless this is below a critical threshold; (iii) the spontaneous switching mechanism can also be used to achieve adaptability to dynamic environments, and its key parameter, the probability of switching, can be used to regulate the trade-off between accuracy and speed of adaptation.

中文翻译：

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动态环境中的集体决策

集体决策是个人在没有任何中央领导的情况下共同做出决策的能力，而只能依靠本地互动。一个特殊的例子是由最佳OF-表示Ñ问题，由此群必须选择一组中的最佳选项Ñ离散的替代品。在本文中，我们执行最佳OF-的透彻研究Ñ在动态环境中的问题，在两个选项（存在\（n = 2的\））。站点质量可以由代理直接测量，我们会对这些质量进行突然更改。我们介绍了两种适应机制来处理动态站点质量：顽固的代理人和自发的意见交换。使用计算机仿真和常微分方程模型，我们表明：（i）仅顽固代理的存在就足以实现适应性，但数量的增加会对性能产生不利影响；（ii）系统适应性随着群体规模的增加而增加，而它并不取决于代理人的密度，除非这低于临界阈值；（iii）自发切换机制还可以用于实现对动态环境的适应性，其关键参数，切换概率，