Nonlinear Network Dynamics for Bio-Inspired Collective Decision-Making

这个项目将建立在对动物群体决策的基于模型的调查的基础上，为多智能体系统的生物启发控制创建一种严格的方法学。众所周知，鸟群和鱼群显示出在相互排斥的竞争选项之间进行选择的非凡能力，这些选项具有速度、准确性、健壮性和适应性。这个项目将通过将奇点和分叉理论应用到网络系统动力学的基于图形的表示中，得出一个通用的和灵活的建模集体决策的框架。数学框架将基于蜂群和鱼群观察的实验数据进行验证。由此产生的方法将被应用于例如移动传感网络、交通网络和电力网络的设计和控制。这些应用在环境监测、灾害应对、交通运输、能源、医疗保健和制造业方面具有重大的潜在社会影响。利用这一令人兴奋的跨学科主题，该项目将通过研究人员访问学校以及学生和教师访问首席调查员实验室来吸引初中和高中女孩。将使用一种可分析易处理的、基于抽象代理的集体决策动态模型，该模型捕捉在动物群体中观察到的非线性现象。这将使协调决策的新方法能够扩展动态代理网络的当前能力，以便在复杂、不断变化的环境中可靠地执行要求苛刻的任务。一种基于奇点理论的原则性方法--一种稳健的分叉理论--将用于该模型，将生物学与工程设计联系起来。将通过建立对价值敏感的决策和解决整个集团内偏好的异质性，来实现备选方案之间的集体决策，并证明高绩效的条件。探索将超越生物学上看似合理的参数范围，以确定和证明高性能工程化集体决策动态的条件。

This project will build upon model-based investigation of group decision-making in animals, to create a rigorous methodology for bio-inspired control of multi-agent systems. It is well known that bird flocks and fish schools show a remarkable ability to choose between mutually exclusive competing options with speed, accuracy, robustness and adaptability. This project will derive a general and flexible framework for modeling collective decision making, by applying singularity and bifurcation theory to graph-based representations of the dynamics of networked systems. The mathematical framework will be validated on experimental data from observations of swarming honeybees and schooling fish. The resulting methodology will find application to design and control of, for example, mobile sensing networks, transportation networks, and power networks. These applications have significant potential societal impact in environmental monitoring, hazard response, transportation, energy, healthcare, and manufacturing. Leveraging this exciting, cross-disciplinary topic, the project will engage middle and high school girls through researcher visits into schools, and student and teacher visits to the Principle Investigator's lab.An analytically tractable, abstract agent-based model for collective decision-making dynamics that captures nonlinear phenomena observed in animal groups will be used. This will enable a new methodology for coordinated decision-making that extends current capabilities of networks of dynamic agents to reliably perform demanding tasks in complex, changing environments. A principled approach, based on singularity theory -- a robust bifurcation theory -- will be used for this model to connect biology to engineering design. Realizations of collective decision-making between alternatives will be derived and conditions for high performance proved by building in value-sensitive decision-making and addressing heterogeneity in preferences across the group. Explorations will go beyond biologically plausible parameter ranges to identify and prove conditions for high-performing engineered collective decision-making dynamics.