The spatial model of voting is a benchmark in theories purporting to explain political behavior. Yet, evidence against the accuracy of both the assumptions and predictions of the spatial model is accumulating in the scholarly literature. One long-held result of spatial theory is that incumbents always lose elections to challengers. Despite the fact that empirical returns fail to confirm the finding, political science has not been able to explain why there exists such a glaring difference between theory and actual returns. In large part, spatial theory's failure to illuminate problems of this kind stems from its reliance upon an unrealistic model of human cognition: substantive rationality. By assuming that political actors possess complete information, formal theoretic approaches have discarded the central dynamic of potential choice. This proposal will offer an alternative approach that seeks to model agents (parties and voters) as limited information processors. Complexity theory provides a framework to model the use of information by political actors and has been utilized in this paper to build a simulation of elections that provides an explanation for why incumbents fare so well on election day. The results not only illuminate the nature of the incumbency, but also argue for a new focus on the role of information in political choice.
