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The use of complexity science and agent based models has been growing within social science, in general, and specifically within political science. The tools of complexity have been used to examine a wide range of social science issues; collective action, elections, political economy, macroeconomics, and international relations. As of yet, though, there has been very little application of the tools of complexity science to issues associated with American political institutions. This project seeks to begin that process by taking a complex adaptive systems approach to the United States Congress. Traditionally formal models of Congress have used a game theoretic approach. However we believe that there are analytical gains to be made by using the agent based approach. Systems that have dynamics, heterogeneous agents, non-linear interactions, adaptation, positive and negative feedback, and externalities are prime candidates for an application of the complex adaptive systems approach. We make the argument in this paper that each of these properties is an important (and often core) attribute of the congressional policy making system. In order to illustrate the analytical leverage of the complex systems approach we first build a set of models that replicate the most prominent models of congressional policy making. To these we add a model using the complex adaptive systems approach. We are then able to simulate a simple public goods allocation problem in each model and view the various policy outcomes. The core feature of our model is that it allows the pivotal agent to arise endogenously. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]

A complex adaptive systems approach to modeling the United States Congress. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]