My mission as a researcher is to develop, test, and present my best theories about emergence and complexity in political science. As an undergraduate studying Politics, Philosophy, and Economics I saw how ideas like linearity, homogeneous actors, equilibrium, and rationality had gone from useful simplifying assumptions to dogma. The method and theory of “complex-systems” moves beyond these assumptions to contend that surprising phenomena can emerge from interactions among simple parts.
Interweaving my concerns about justice with my substantive interests in politics and economics, classes like Contracts and Torts fulfilled my hopes for an interdisciplinary education in law school. And, success with a federal jury trial on a constitutional issue at age twenty-three led me to try law as a career. However, two years in civil litigation changed my mind.
Fortunately, in my first year of graduate school I started working on two projects that will engage me for a lifetime in academia. John Zaller’s contention that political novices and sophisticate’s “think differently” made me wonder if I could test his theory using brain imaging. Reading Scientific American as a law student introduced me to brain imaging, complex-systems theory, and a tool called agent-based computer modeling that I began using at UCLA.
My model of political party formation uses five simple rules that reflect traditional assumptions from formal theory. I have unified a number of classic results, like the emergence of two parties, the tendency of parties to move their platforms to the median voter, and the realignment of political parties to changing issues, into a single framework. This agent-based computer model embodied my ambitions for a scientifically rigorous way of exploring non-linear, non-equilibrium models with heterogeneous actors. However, it still assumes rationality.
I am currently developing a theory of political cognition using brain-imaging techniques from cognitive neuroscience. Ultimately, I will follow the lead of computational neuroscience and operationalize that theory in a computer model. Formal models in political science assume elites respond to voter preferences and empirical models show voters as following elite opinion. My long-range goal is to extend my agent-based framework so that decision making reflects the model of political cognition developed in my brain imaging studies and my framework demonstrates that ideology is an emergent result of mass/elite interaction. Although a political theory that goes from neural components to national ideology sounds farfetched, I have acquired a unique set of skills in cognitive neuroscience, computational modeling, statistics, law, philosophy, and American politics that will enable me to demonstrate the emergence of political attitudes in a complex world of interacting political novices and elites and to consider the legal and normative implications.