Connecting Empirical and Mathematical Approaches to Collective Behaviour

将经验方法和数学方法与集体行为联系起来

• 批准号: RGPIN-2017-06094
• 负责人: Lukeman, Ryan
• 金额: $ 1.02万
• 依托单位: St. Francis Xavier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675392
• 关键词: Connecting; Empirical; Mathematical; Approaches; Collective

Across scales, biological aggregates form striking patterns, display coordinated, cohesive motion, and function via principles of self-organization, yet complexity at the group level can obscure the underlying mechanisms. Determining how such group behaviour emerges and is sustained involves both direct observation of such groups, and mathematical models to test hypothetical behaviour regimes. ******My research program lies at this interface. I design and implement observational and experimental studies of collectives, from birds to humans. I develop algorithms to process and analyze these data, to summarize important statistical markers of interaction, and provide tests for comparison across species and condition. I develop, test, and simulate individual based differential-equation models to evaluate hypotheses of what individual interactions explain the observed group-level structure. By informing the construction and validation of these models via empirical data, I tie model predictions directly to the natural phenomena. I have used this empirical-theoretical approach to study large flocks of surf scoters in the field, to infer individual rules of interaction from reconstructed trajectories. Further analysis has revealed insights into the dynamics of order/disorder transitions, patterns of predation avoidance, and collective response to the environment.******I will extend my analytical methods to new datasets of collective motion. With a new collaborator in Sydney, Australia, I will analyze a collection of trajectory-based datasets of fish, both in the field and in the lab. These datasets include the collective defensive response to risk in damselfish in the Great Barrier Reef, the effects of inter-fish familiarity on collective behaviour of mosquitofish, and how behavioural parameters are shaped by speciation and environment within the family of Rainbowfish. The degree of control permitted by the laboratory data will allow for the development of an updated, stepwise modelling approach from individuals, to pairs, up to large collectives.******In the second main direction, I will study human collective behaviour via aural interactions. This research, based on pilot work already completed, uses an experimental system of humans clapping synchronously (groups of 2 to hundreds) to study what type of interactions allow synchronous clapping to arise, and how order parameters (group frequency, group synchrony) evolve after synchrony is achieved, via a coupled-oscillator modelling framework. This research extends work on human sensorimotor synchronization from the individual to the collective. Furthermore, effects such as group size, rhythmic initialization, and spatial information transfer will be studied.******These initiatives contribute to my overall research goal of measuring, and explaining pattern in organized collectives.

在范围内，生物骨料形成引人注目的模式，通过自组织的原理显示协调的，有凝聚力的运动和功能，但是小组级别的复杂性可以掩盖潜在的机制。确定这种群体行为是如何出现和持续的，既涉及对此类群体的直接观察，也涉及数学模型来检验假设行为制度。 ******我的研究计划在于这个接口。我设计并实施了从鸟类到人类的集体的观察和实验研究。我开发了处理和分析这些数据的算法，总结了相互作用的重要统计标记，并提供了跨物种和条件进行比较的测试。我开发，测试和模拟基于个体的差分方程模型，以评估单个相互作用解释观察到的群体级结构的假设。通过通过经验数据告知这些模型的构建和验证，我将模型预测直接与自然现象联系起来。我已经使用了这种经验理论方法来研究该领域的大量冲浪型苏格兰群，从而从重建的轨迹中推断出单个相互作用的规则。进一步的分析揭示了对秩序/障碍过渡的动态，避免捕食模式以及对环境的集体反应的见解。******我将把我的分析方法扩展到集体运动的新数据集。与澳大利亚悉尼的新合作者一起，我将在现场和实验室中分析基于轨迹的鱼类数据集的集合。这些数据集包括大障碍礁中对大坝的风险的集体防御反应，熟悉程度对蚊子集体行为的影响以及行为参数如何通过彩虹鱼家族中的形态和环境来塑造。实验室数据允许的控制程度将允许开发更新的，逐步建模的方法从个人到成对，直至大型集体。******在第二个主要方向上，我将通过听觉相互作用来研究人类的集体行为。这项基于已经完成的试验工作的这项研究使用了人类同步拍手的实验系统（2至数百个组）来研究哪种类型的相互作用允许同步拍照，以及通过coupled-oscill-osciller-oscillator建模框架实现同步后的订单参数（组频率，组同步）如何进化。这项研究将人类感觉运动同步的工作从个人到集体扩展。此外，将研究诸如组大小，节奏初始化和空间信息转移之类的效果。