Current Research in Evolutionary Sociobiology

Our lab is interested in self-organizing systems and the complexity of emergent social systems governed by individual behavior. We use mathematical modeling to investigate possible mechanisms for the evolution of sociality. Additionally, we work to characterize traits which increase both the success and stability of organizational structures and strategies, especially those that make populations best able to survive group-level threats, such as those posed by epidemics.

Our interests in sociobiology are primarily focused on the evolution of sociality and social complexity.

  1. Understanding the evolution and maintenance of observed behaviors in real-world systems: These interests focus empirically on social insects and to understand what real-world experiments tell us about these systems we use tools from game theory, linear programming, optimization theory, and dynamical systems.
  2. Abstract and theoretical characterizations of the evolution and maintenance of social complexity: Rather than focusing on particular systems, we are interested in general principles of self-organization and distributed decision making under evolutionary pressures (especially from disease risks). We use simulation methods and agent based models in addition to analytic game theory, complexity theory, and analytic network theory to explore how group-level outcomes can be built by evolutionary variation in traits and selective pressure acting on individual fitness.