Composite collective decision making

Individual animals are adept at making decisions and have cognitive abilities, such as memory, which allow them to hone their decisions. Social animals can also share information. This allows social animals to make adaptive group-level decisions. Both individual and collective decision-making systems also have drawbacks and limitations, and while both are well studied, the interaction between them is still poorly understood. Here, we study how individual and collective decision-making interact during ant foraging. We first gathered empirical data on memory-based foraging persistence in the ant Lasius niger. We used these data to create an agent-based model where ants may use social information (trail pheromones), private information (memories) or both to make foraging decisions. The combined use of social and private information by individuals results in greater efficiency at the group level than when either information source was used alone. The modelled ants couple consensus decision-making, allowing them to quickly exploit high-quality food sources, and combined decision-making, allowing different individuals to specialize in exploiting different resource patches. Such a composite collective decision-making system reaps the benefits of both its constituent parts. Exploiting such insights into composite collective decision-making may lead to improved decision-making algorithms.

个体动物具备卓越的决策能力与认知机能（如记忆），借此可优化自身决策行为。社会性动物还可开展信息共享，借此实现适配环境的群体层面决策。个体与群体决策系统均存在缺陷与局限性；尽管二者均已得到充分研究，但二者间的交互机制仍鲜为人知。本研究以蚂蚁觅食行为为对象，探讨个体与群体决策的交互机制。我们首先收集了黑毛蚁（Lasius niger）基于记忆的觅食持续性相关实证数据，并基于上述数据构建了基于智能体的模型：模型中的蚂蚁可借助社交信息（踪迹信息素（trail pheromones））、私有信息（记忆），或二者结合来制定觅食决策。个体同时使用社交与私有信息时，群体层面的决策效率要高于仅使用单一信息源的场景。该模型中的蚂蚁兼具共识决策与组合决策能力：前者可使其快速开发优质食物源，后者则能让不同个体专门开发不同的资源斑块。这类复合群体决策系统可充分汲取其组成部分的双重优势。对复合群体决策机制的此类深入研究，有望推动更优化的决策算法的研发。