Optimal Collective Decision-Making in Social Insects

群居昆虫是研究集体决策、分散控制和自我组织的模型系统。我们自己最近的理论工作奠定了基础，在移民过程中的最佳决策分析：我们是第一个展示如何简单的模型可以参数化，以实现统计最优决策的社会昆虫殖民地，在速度和准确性之间的最佳折衷的决定是实现。这项建议有两个目的：第一，用蚂蚁和蜜蜂来验证这些预测，使用最新的跟踪个体的技术进行实验。其次，将模型及其最优性分析扩展到更复杂的决策任务，包括以前没有得到理论处理的新决策问题：特别是，社会昆虫群体必须同时尝试对其环境进行最佳采样，以获得关于可用替代品的不确定信息，并同时在这些替代品之间进行最佳决策。这些理论研究将为蚂蚁和蜜蜂的新实验提供信息。这两个物种已经聚集在几乎相同的集体决策机制，但在其实施的微妙但重要的细节不同。这项建议将雇用两名博士后研究助理，一名在詹姆斯·马歇尔博士的指导下在布里斯托从事理论工作，一名在奈杰尔·弗兰克斯教授的指导下在布里斯托从事蚂蚁实验工作，一名在安娜·多恩豪斯博士的指导下在亚利桑那州从事蜜蜂实验工作，并听取汤姆·塞利教授的建议。该项目也是一个更大的研究计划的一部分：我们的新理论观点，激发了这一提议，是通过综合社会昆虫行为，进化心理学和脊椎动物神经科学的想法来实现的。人类和其他脊椎动物的决策研究有着坚实的理论基础，基于可证明的最佳决策程序。正如我们之前的工作所表明的那样，灵长类大脑中的神经决策回路模型与社会性昆虫群体中的集体决策模型之间存在惊人的相似之处。这使得非常相似的最优性分析技术可以应用于两者。然而，大脑和群居昆虫群体之间也存在有趣的差异：特别是，如上所述，群居昆虫群体必须积极地从环境中采集信息，而不是像一些神经模型所假设的那样，以平等和不变的速度接收关于可用替代品的信息。当然，这实际上是一个错误的二分法：大脑中的一些决策任务，如主动感知，也需要同时采样和决策的策略。因此，我们认为这个研究项目在集体行为和大脑行为的界面上是一个真正的综合，每个领域都能够为另一个领域提供基本的见解。然而，就本提案的目的而言，与神经系统相比，使用社会性昆虫群体的一个特别好处是它们易于实验观察和操作。

Social insects are model systems for studying collective decision-making, decentralised control, and self-organisation. Our own recent theoretical work has laid the foundations for analysis of optimal decision-making during emigration: we were the first to show how simple models can be parameterised to implement statistically optimal decision-making by social insect colonies, in which an optimal compromise between speed and accuracy of decisions is achieved. This proposal has two aims: first, to validate empirically these predictions with ants and honeybees, using experiments with the latest technology for tracking individuals. Second, to extend the models and their optimality analysis to more sophisticated decision-tasks, including novel decision-problems that have not received theoretical treatment before: in particular, a social insect colony must simultaneously attempt to sample its environment optimally for uncertain information on available alternatives, and at the same time optimally decide between these alternatives. These theoretical investigations will inform and be informed by novel experiments with ants and honeybees. These two species have converged on almost identical collective decision-making mechanisms, that differ however in subtle but important details in their implementation. This proposal will hire two post-doctoral research assistants, one to undertake theoretical work in Bristol under the direction of Dr James Marshall, and one to undertake experimental work with ants in Bristol under the direction of Prof Nigel Franks, and with honeybees in Arizona under the direction of Dr Anna Dornhaus and with the advice of Prof Tom Seeley. This project also forms part of a larger research programme: our novel theoretical perspective, which motivates this proposal, was achieved by synthesising ideas from social insect behaviour, evolutionary psychology and vertebrate neuroscience. The study of decision-making in humans and other vertebrates enjoys a sound theoretical footing, based on provably optimal decision-making procedures. As our previous work has shown, there are striking parallels between models of neural decision-making circuits in the primate brain, and models of collective decision-making in social insect colonies. This allows very similar optimality analysis techniques to be applied to both. There are also interesting differences, however, between brains and social insect colonies: in particular, as stated above, social insect colonies must actively sample information from their environment, rather than receiving information on available alternatives at equal and unvarying rates, as assumed by some neural models. Of course, this is actually a false dichotomy: some decision-making tasks in the brain, such as active perception, also require strategies for simultaneous sampling and decision-making. Thus we see this research programme at the interface of collective behaviour and brain behaviour as a genuine synthesis, with each field able to provide fundamental insights to the other. For the purposes of this proposal, however, one particular benefit of working with social insect colonies, compared to neural systems, is their ease of experimental observation and manipulation.