Political discourse is the verbal interaction between political actors in a policy domain. This article explains the formation of polarized advocacy or discourse coalitions in this complex phenomenon by presenting a dynamic, stochastic, and discrete agent-based model based on graph theory and local optimization. In a series of thought experiments, actors compute their utility of contributing a specific statement to the discourse by following ideological criteria, preferential attachment,

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... setting strategies, governmental coherence, or other mechanisms. The evolving macro-level discourse is represented as a dynamic network and evaluated against arguments from the literature on the policy process. A simple combination of four theoretical mechanisms is already able to produce artificial policy debates with theoretically plausible properties. Any sufficiently realistic configuration must entail innovative and path-dependent elements as well as a blend of exogenous preferences and endogenous opinion formation mechanisms. Background Political discourse is a complex phenomenon. Despite its intriguing prevalence in everyday politics, there have been only few attempts at developing explanations of how discourse works, and even fewer attempts at modeling this apparently ill-defined phenomenon in a formal way. Political discourse, as it shall be analyzed here, is the verbal interaction between political actors in a policy domain. For example, in the debate on nuclear energy policy, a number of state and non-state actors speak up in the media and call for specific policy instruments or make specific claims on the basis of their causal beliefs. This phenomenon is based on a complex set of properties: First, political discourse is dynamic because political actors repeatedly participate in a policy debate. Consecutive statements of actors rather than simultaneous moves constitute the essence of a discourse. Second, political discourse is relational. Neither are actors insulated when they make their claims in a debate nor are their statements randomly distributed across actors in the policy domain or over time [1, 2] . There is rather an interaction in which actors frequently refer to what others said before. For this reason, social network analysis [3, 4] is an appropriate tool for the measurement of empirical discourses. Ward et al. [5] conclude in their