Party competition for votes in free and fair elections involves complex
interactions by multiple actors in political landscapes that are
continuously evolving, yet classical theoretical approaches to the
subject leave many important questions unanswered. Here Michael Laver
and Ernest Sergenti offer the first comprehensive treatment of party
competition using the computational techniques of agent-based modeling.
This exciting new technology enables researchers to model competition
between several different political parties for the support of voters
with widely varying preferences on many different issues. Laver and
Sergenti model party competition as a true dynamic process in which
political parties rise and fall, a process where different politicians
attack the same political problem in very different ways, and where
today's political actors, lacking perfect information about the
potential consequences of their choices, must constantly adapt their
behavior to yesterday's political outcomes.
Party Competition shows how agent-based modeling can be used to
accurately reflect how political systems really work. It demonstrates
that politicians who are satisfied with relatively modest vote shares
often do better at winning votes than rivals who search ceaselessly for
higher shares of the vote. It reveals that politicians who pay close
attention to their personal preferences when setting party policy often
have more success than opponents who focus solely on the preferences of
voters, that some politicians have idiosyncratic "valence" advantages
that enhance their electability--and much more.
