I use mathematical and computational methods to address questions at the interface of evolution and ecology, computational social science and network science. I am interested in how microscopic interactions scale up to produce macroscopic features of living systems. This includes such diverse topics as understanding how regulatory interactions between individual genes translate into cell-wide patterns of gene expression, and how repeated social interactions among groups of individuals result in the emergence of social norms and collective identities. I see everything through the lens of evolution. I always try to understand when and how microscopic variations will arise and spread under natural selection to produce macroscopic changes. Most fascinating of all is to ask these types of questions about our own hyper-social, hyper-complex species: To try and use evolutionary ideas to understand the dynamics of social norms, behavior and identity that we see all around us today. To try and build a statistical mechanics of crowds. My current research falls roughly into three categories:
Dynamics of social behavior
I use game theory as a framework for modeling social interactions between pairs or groups of individuals. I try to bring as much realism to the models as possible, allowing for complex strategies and repeated interactions, memories of past interactions, arbitrary action spaces, social signaling and identity, dynamic social networks and complex, multi-dimensional utilities. My work in this area focuses first on developing tools to analyze the outcome of repeated social interactions (in terms of payoff or utility), and then, given knowledge of these outcomes, figuring out how natural selection acts to produce change in social behaviors, strategies, identities and social networks.
The genetic architecture underlying any trait under selection is important because it constrains the type and frequency of mutations that arise and change that trait. I study genetic architecture very mechanistically, for example by looking at how the structure of gene regulatory networks impacts the evolution of gene expression. I also study it more heuristically, for example by conducting social experiments to determine how people learn by trying out new behavioral strategies in various social contexts (of course in this latter case reference to “genetic architecture” is not to be taken literally). We can also turn the problem on its head, and study how genetic architecture itself evolves.
Evolvability is the ability of a population to produce a new adaptation in response to a new selective pressure. Complex adaptations are those requiring multiple (perhaps rare, perhaps individually disadvantageous) mutations. The classic example of a complex adaptation is the vertebrate eye, whose evolution has been debated since Darwin. Assessing the evolvability of complex adaptations requires us to understand both the genetic architecture underlying traits, and how macroscopic properties of a population impact the potential for individual mutations to stick around once they arise. I study how social interactions effect the evolvability of complex traits, either promoting or suppressing them, through mechanisms such as cooperation, division of labor or sexual reproduction.