Multiscale Modeling of Division of Labor in Social Insects

群居昆虫，包括蚂蚁和白蚁，以及许多蜜蜂和黄蜂，是地球上最多样化和最具生态意义的生物之一。这些物种生活在复杂的社会中，其治理不是来自一个中央权力机构，而是来自其个人与彼此以及与环境的互动。从这种分散的系统中，出现了任务分配、分工和资源开发的一致模式。对这些模式出现的多尺度研究既给研究和教育带来了挑战，也带来了机遇。这项跨学科研究的目标是开发一个通用的集成的多尺度社会性昆虫分工动态网络建模框架。严谨的数学将与广泛的野外和实验室数据相结合，以研究社会昆虫社会的复杂适应系统。研究小组正在探索：1.群体相互作用网络的基本拓扑是如何在组织的不同规模上进化和适应的；2.如何表征动态环境中连接分工结构和动态的关键反馈机制；3.基于许多独立和简单的个体相互作用的分散的社会昆虫系统如何导致高度复杂的动力学，具有很好的网络特性，如可扩展性、健壮性/灵活性和简单性。研究人员使用非线性微分方程组、空间随机过程和动力学方程分别在群体水平、个体水平、多水平以及空间和任务连续体水平进行建模。这项研究项目是社会科学、生命科学和应用数学的交汇点。这种群居昆虫的建模框架不仅为研究网络动力学如何有助于复杂生物系统的进化提供了一个强大的系统，而且也为探索复杂系统中的行为如何进化提供了一个很好的机会。所开发的方法可以应用于生物学以外的许多领域，包括网络路由、优化理论和机器人学。将通过本科生和/或研究生的共享研究项目，为社会科学学生、生命科学学生和应用数学学生开发一个整合跨学科学习的模板。

Social insects, including ants and termites, as well as many bees and wasps, are among the most diverse and ecologically significant organisms on earth. These species live in complex societies whose governance comes not from a central authority but from the interactions of their individuals with each other and with the environment. From this decentralized system, there emerge consistent patterns of task allocation, division of labor, and resource exploitation. The multiscale study of the emergence of these patterns presents both challenges and opportunities for research and education. The goal of this interdisciplinary research is to develop a general integrated multi-scale dynamical network modeling framework of division of labor in social insects. Rigorous mathematics will be integrated with extensive field and laboratory data to study the complex adaptive system of social insect societies. The research team is exploring: 1. How the underlying topology of the interaction network of a colony evolves and adapts at different scales of the organization; 2. How to characterize the crucial feedback mechanisms linking both structure and dynamics of the division of labor in a dynamical environment and 3. How the decentralized social insect system based on many independent and simple individual interactions leads to highly complex dynamics with great network properties such as scalability, robustness/flexibility and simplicity. The investigators use nonlinear differential equations, spatial stochastic processes and kinetic equations to model at the colony level, the individual level, multilevel and at a spatial and task continuum level, respectively. This research project lives at the intersection of social science, life sciences and applied mathematics. This modeling framework of social insects provides not only a powerful system for examining how network dynamics contribute to the evolution of complex biological systems but also a great opportunity to explore how behavior evolves within complex systems in general. The methods developed may apply in many domains outside of biology, including network routing, optimization theory and robotics. A template integrating interdisciplinary learning for social sciences students, life sciences students and applied mathematics students will be developed through shared research projects at the undergraduate and/or graduate level.