Multiscale Modeling of Division of Labor in Social Insects

Arizona State University, Scottsdale AZ

Investigators

Abstract

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.