My research primarily focuses on game theory and mathematical modeling of social behavior.  I have written papers on the evolution of language, social norms, optimal social structures for science, and the evolution of learning.  All of these share a common thread of using modeling techniques from the social sciences to analyze problems of philosophical interest.  I think that having philosophers use and discuss these modeling techniques will produce many interesting results both for the philosophy of science and for social science modeling.   I am also interested in more general problems in philosophy of science, philosophy of biology, and philosophical naturalism.  

My Curriculum Vitae (odt)


The Social Structure of Science

My dissertation focuses on the optimal design for scientific institutions.  Here I look at network structure (the collection of connections amongst individuals) and attempt to determine what is the best network structure for groups of learners. Interestingly, I find that in some circumstances it is best for people to only interact with a small number of others, even when there is no cost to interaction.  This result is described in the first two papers listed below.  

This holds only for a certain type of learning situation but not for another.  This leads me to conclude that the practice social epistemology should not attempt to come up with general conclusions about optimal social structure, but should instead analyze optimal structure for particular learning situations.  

The Communication Structure of Epistemic Communities (ps)

The Epistemic Benefit of Transient Diversity

Social Structure and the Effects of Conformity (ps)

Network Epistemology (ps)

Evolution of Learning

Rory Smead and I have investigated the relationship between strategic interaction, evolution and phenotypic plasticity (or learning).  We have investiaged whether or not evolution should facilitate the emergence of plastic behaviors when individuals are playing repeated games against one another.  In addition, we investigate the relationship between these effects and a oft-debated theoretical effect called the Baldwin effect.

The Stability of Strategic Plasticity

Evolution of Signaling/Language

David Lewis in his book Convention introduced a simple signaling game as a model for the emergence of language.  This work was extended by Brian Skyrms, and others, to include an analysis of the initial evolution of language.  While it was initially presumed that evolution always resulted in maximally efficient languages, it now appears that this is not the case.  Recent work by several scholars has shown some interesting results for these relatively simple games.  I have coauthored one such paper.

Evolutionary Dynamics of Lewis Signaling Games

My recent work with Jeff Barrett analyzes the relative efficiency of a few common learning rules and contrast those with others which have previously been analyzed. One common feature, we find, is that learning rules which allow for some form of forgetting result in the evolution of more efficient languages.

The Role of Forgetting in the Evolution and Learning of Language (ps)

I have also analyzed the effect that spatial organization has on the learning of language in two signaling games.  One is the same Lewis-Skyrms signaling game analyzed above, the other is a game where cooperation and safety are put into conflict (the Stag Hunt).  Here I found that spatial structure and signaling together both substantially helped the evolution of cooperation, but also that the evolved language had some interesting features.

Talking to Neighbors: The Evolution of Regional Meaning (odt)

The Evolution of Cooperation

The paper provided above ("Talking to Neighbors") analyzes the co-evolution of cooperation and communication.  In that paper I found that cooperation and communication do, in fact co-evolve and that this evolution is not hampered by the introduction of additional realistic assumptions in the model.

In addition to this, I have written a paper on the evolution of norms of fairness.  This paper looks at the, widely studied, ultimatum game.  Here I test a widely held belief that the evolution of fairness in the ultimatum game is the result of an evolved heuristic which came about in other contexts.  I find that this does provide an explanation for the evolution of fairness, and in addition I discover an interesting formal result.  This formal result shows that in contexts where individuals are evolving heuristics for multiple situations, the evolutionary dynamics can be radically different than the evolutionary dynamics for each situation taken alone.  This speaks to the generality of models that consider specific types of social interaction individually and also provides a caution for social science modelers.

Explaining Fairness in a Complex Environment (odt)