How do groups of individuals achieve consensus in movement decisions? Do individuals follow their friends, the one predetermined leader, or whomever just happens to be nearby? To address these questions computationally, we formalize "Coordination Strategy Inference Problem". In this setting, a group of multiple individuals moves in a coordinated manner towards a target path. Each individual uses a specific strategy to follow others (e.g. nearest neighbors, pre-defined leaders, preferred friends). Given a set of time series that includes coordinated movement and a set of candidate strategies as inputs, we provide the first methodology (to the best of our knowledge) to infer whether each individual uses local-agreement-system or dictatorship-like strategy to achieve movement coordination at the group level. We evaluate and demonstrate the performance of the proposed framework by predicting the direction of movement of an individual in a group in both simulated datasets as well as two real-world datasets: a school of fish and a troop of baboons. Moreover, since there is no prior methodology for inferring individual-level strategies, we compare our framework with the state-of-the-art approach for the task of classification of group-level-coordination models. The results show that our approach is highly accurate in inferring the correct strategy in simulated datasets even in complicated mixed strategy settings, which no existing method can infer. In the task of classification of group-level-coordination models, our framework performs better than the state-of-the-art approach in all datasets. Animal data experiments show that fish, as expected, follow their neighbors, while baboons have a preference to follow specific individuals. Our methodology generalizes to arbitrary time series data of real numbers, beyond movement data.

中文翻译：

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从运动数据的时间序列推断跟随策略的框架

个体群体如何在运动决策中达成共识？个人是跟随他们的朋友、一位预定的领导者还是碰巧在附近的任何人？为了在计算上解决这些问题，我们形式化了“协调策略推理问题”。在这种情况下，一组多人以协调的方式朝着目标路径移动。每个人都使用特定的策略来跟随他人（例如最近的邻居、预定义的领导者、首选朋友）。给定一组包括协调运动和一组候选策略作为输入的时间序列，我们提供了第一种方法（据我们所知）来推断每个人是使用本地协议系统还是类似独裁的策略来实现小组层面的运动协调。我们通过在模拟数据集和两个真实世界数据集（一群鱼和一群狒狒）中预测一个群体中个体的运动方向来评估和证明所提出框架的性能。此外，由于没有用于推断个人级别策略的先验方法，我们将我们的框架与最先进的方法进行比较，以完成组级别协调模型的分类任务。结果表明，即使在复杂的混合策略设置中，我们的方法在推断模拟数据集中的正确策略方面也非常准确，这是现有方法无法推断的。在组级协调模型的分类任务中，我们的框架在所有数据集中的表现都优于最先进的方法。动物数据实验表明，正如预期的那样，鱼，跟随他们的邻居，而狒狒更喜欢跟随特定的个体。我们的方法可以推广到任意的实数时间序列数据，而不是运动数据。