Two themes unite my research: a commitment to methodological creativity and innovation as expressed in my work with computational social sciences, and an interest in the political economy of “globalization,” particularly its implications for the ontological claims of international relations theory.

I have demonstrated how the methods of computational social sciences can model bargaining and social choice problems for which traditional game theory has found only indeterminate and multiple equilibria. My June 2008 article in International Studies Quarterly (“Coordination in Large Numbers,” vol. 52, no. 2) illustrates that, contrary to the expectation of collective action theory, large groups may enjoy informational advantages that allow players with incomplete information to solve difficult three-choice coordination games. I extend this analysis in my 2009 paper at the International Studies Association annual convention, in which I apply ideas from evolutionary game theory to model learning processes among players faced with coordination and commitment problems. Currently I am extending this research to include social network theory as a means of modeling explicitly the patterns of interaction in large-n (i.e. greater than two) player coordination and cooperation games. I argue in my paper at the 2009 American Political Science Association annual convention that computational social science—the synthesis of agent-based modeling, social network analysis and evolutionary game theory—empowers scholars to analyze a broad range of previously indeterminate bargaining problems. I also argue this synthesis gives researchers purchase on two of the central debates in international political economy scholarship. By modeling explicitly processes of preference formation, computational social science moves beyond the rational actor model and endogenizes the processes of learning that constructivists have identified as essential to understanding change in the international system. This focus on the micro foundations of international political economy in turn allows researchers to understand how social structural features emerge and constrain actor choices. Computational social science thus allows IPE to formalize and generalize our understandings of mutual constitution and systemic change, an observation that explains the paradoxical interest of constructivists like Ian Lustick and Matthew Hoffmann in the formal methods of computational social science. Currently I am writing a manuscript that develops these ideas and applies them to several challenges of globalization: developing institutions to manage common pool resources; reforming capital adequacy standards for banks; and understanding cascading failures in global networks.

While computational social science increasingly informs my research, I have also contributed to debates about the epistemological claims of computational social science. My chapter with James N. Rosenau in Complexity in World Politics (ed. by Neil E. Harrison, SUNY Press 2006) argues that agent-based modeling suffers from underdeveloped and hidden epistemological and ontological commitments. On a more light-hearted note, my article in PS: Political Science and Politics (“Clocks, Not Dartboards,” vol. 39, no. 3, July 2006) discusses problems with pseudo-random number generators and illustrates how they can surprise unsuspecting teachers and researchers.

I have been researching in synchronization between agent-based-models (ABM) and multi robot systems used in logistic and manufacturing. I use Netlogo as ABM.
I develop and agile methodology to use the same ABM as supervisory control and data aquisition (SCADA). The framework works fine and I test it in two SCADAs, which you can see in my youtube channel (http://www.youtube.com/channel/UCJIb_UL-ak98F5OZxOHL0FQ).

Science & Technology Policy, Collaborative Innovation, Technological Diffusion and Convergence, Agent-Based Modelling, and Social Simulation.

My profound interest in networks convinced me to work in these subjects and start my master project on an application of social network analysis for detecting organized fraud in Automobile insurance, which helps to flag groups of fraudsters. The key point of this project is simply to find fraudulent rings, while the most of traditional methods have only taken opportunistic fraud into consideration. My duty in research is to design an algorithm for identifying cyclic components, then to be compared with theoretical ones. This project showed me how networks are used in the analysis of relations.

Computational Modeling of knowledge diffusion in organizational contexts.