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We study the relationship between voters' preferences and the emergence of party platforms in two-party democratic elections with adaptive parties. In the model, preferences of voters and the opposition party's platform determine an electoral landscape on which the challenging party must adaptively search for votes. We show that changes in the underlying distribution of voters' preferences result in different electoral landscapes which can be characterized by a measure of ruggedness. We find that locally adapting parties converge to moderate platforms regardless of the landscape's ruggedness. Greater ruggedness, however, tempers a party's ability to find such platforms. Thus, we are able to establish a link between the distribution of voters' preferences and the responsiveness of adaptive parties. (British Journal of Political Research / FUB)

We develop a model of two-party spatial elections that departs from the standard model in three respects: parties' information about voters' preferences is limited to polls; parties can be either office-seeking or ideological; and parties are not perfect optimizers, that is, they are modelled as boundedly rational adaptive actors. We employ computer search algorithms to model the adaptive behavior of parties and show that three distinct search algorithms lead to similar results. Our findings suggest that convergence in spatial voting models is robust to variations in the intelligence of parties. We also find that an adaptive party in a complex issue space may not be able to defeat a well-positioned incumbent.