The decentralized cognition of animal groups is both a challenging biological problem and a potential basis for bio-inspired design. The understanding of these systems and their application can benefit from modeling and analysis of the underlying algorithms. In this study, we define a modeling framework that can be used to formally represent all components of such algorithms. As an example application of the framework, we adapt to it the much-studied house-hunting algorithm used by emigrating colonies of Temnothorax ants to reach consensus on a new nest. We provide a Python simulator that encodes accurate individual behavior rules and produces simulated behaviors consistent with empirical observations, on both the individual and group levels. Our model successfully reproduces experimental results showing the high cognitive capacity of colonies, their rational time investment during decision-making, and their ability to avoid and repair splits with the help of social information. We also use the model to make predictions about several unstudied aspects of emigration behavior. The results suggest the value of individual sensitivity to site population for ensuring consensus, and they indicate a more complex relationship between individual behavior and the speed/accuracy trade-off than previously appreciated. The model proved relatively weak at resolving colony divisions among multiple sites, suggesting either limits to the ants' ability to reach consensus, or an aspect of their behavior not captured in our model. It is our hope that these insights and predictions can inspire further research from both the biology and computer science community.

中文翻译：

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社会信息在蚁群房屋狩猎中的力量：一种计算建模方法

动物群的去中心化认知既是一个具有挑战性的生物学问题，也是生物启发性设计的潜在基础。对这些系统及其应用的理解可以从基础算法的建模和分析中受益。在本研究中，我们定义了一个建模框架，该框架可用于正式表示此类算法的所有组件。作为该框架的一个示例应用程序，我们对其进行了广泛研究，以将移出Temnothorax的殖民地所使用的房屋狩猎算法进行改编。蚂蚁就新巢达成共识。我们提供了一个Python模拟器，该模拟器对准确的个人行为规则进行编码，并在个人和小组级别上产生与经验观察一致的模拟行为。我们的模型成功地再现了实验结果，这些结果表明了殖民地的高认知能力，在决策过程中的合理时间投入以及借助社交信息避免和修复分裂的能力。我们还使用该模型对移民行为的几个尚未研究的方面做出预测。结果表明，个体对站点人口的敏感性对于确保共识具有价值，并且它们表明个体行为与速度/准确性权衡之间的关系比以前所理解的更为复杂。该模型在解决多个站点之间的群体划分方面被证明相对较弱，表明要么限制了蚂蚁达成共识的能力，要么表明了模型中未捕获到的行为。我们希望这些见解和预测能够激发生物学和计算机科学界的进一步研究。