I spent a decade working at the interface with logic and philosophy. Then I spent a decade working at the interface with game theory and economics. I am now pulling back and to some extent fusing these directions. Our group is looking at a variety of problems which don't all fall into tidy buckets other than the broad umbrella of multiagent systems (and sometimes even that is too tight a circumscription). That said, here are two broad directions which are are beginning to focus on:

• Algorithmic Institutional Design: This is a novel term I coined to describe the algorithmic study of many institutions employed by groups and organizations, including the well-studied voting but also many less studied ones such as conducting qualifying exams, running tournaments, and many others. 
• Formal and useful models of motivational mental attitudes: Informational relations between agents and propositions - notions such as knowledge, certainty and belief are well studies in AI, philosophy, game theory, and many others. In comparison, motivational relations - notions such as goal, desire, intention, want, and others - are much less well studied. We are after formal models - whether logical, Bayesian, or other - that are rigorous, intuitive, and useful. 

We have three ongoing NSF grants, each exploring different interdisciplinary issues concerning multiagent systems. 

• ITR grant entitled "Non-Cooperative Computing Foundational Problems at the Interface of Computer Science and Game Theory." Co-principal investors on this project are Prof. Daphne Koller from Computer Science, and Profs. Bob Wilson and Yossi Feinberg from the Graduate School of Business, all at Stanford. The project is multi-faceted, but one component is computing Nash equilibria, and creating a universal computational testbed for game theory (a system called GAMUT). 
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