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Spatial models are widely used by political scientists to analyze decision making in a wide range of settings, from elections and legislatures, to government formation, to international organizations, to the U.S. Supreme Court, and many other things. Spatial models provide what has proved to be a fruitful way to think rigorously about politics, grounded in a simple intuition with a reach far beyond the narrow confines of professional political science. At least since the days of the French Revolution, indeed, references to “left” and “right” and to changing policy “positions” have been a part of day-to-day political discourse. The general public generally understands what it means to talk about politics using “spatial” language, as such language is not just a conceit of political scientists. In this entry, mathematical and empirical approaches to spatial modeling and their applications to various forms of political decision making are discussed.

The words left and right refer in this context to directions along a “dimension” of political preference. But one dimension is rarely enough for a good description of how real people think in a given setting. It is easy to imagine two people who are both on the economic right, with similar views about government intervention in the economy, who nonetheless have very different views about government intervention in matters of personal morality. One can think of views on matters such as abortion and same-sex marriage, for example, in terms of a liberal–conservative dimension that is quite independent from—in spatial language orthogonal to—the left–right dimension. If economic and social issues are all that concern political scientists, then they can consider political preferences to be well described using a two-dimensional space, spanned by a left–right economic policy dimension and a liberal–conservative social policy dimension. Building systematically on this spatial metaphor for describing political preferences, a metaphor that does seem to be deeply rooted in real politics, political scientists tend to take one of two distinct but related approaches when they analyze political decision making. These approaches can be thought of as mathematical and empirical.

Mathematical and Empirical Approaches

One can, as a mathematical construct, think of an abstract space of possible outcomes. An integral part of the formal definition of individual rationality is having preferences that rank such outcomes in a transitive way: Preferring outcome A to B and preferring B to C implies preferring A to C. Many cost–benefit calculations involve specifying “how much” A is preferred to B, relative to how much B is preferred to C. Information or assumptions about such matters can be expressed as perceived “distances” between A, B, and C in an assumed “metric” space comprising a set of possible outcomes and a measure of the distance between these.

One key distinction between mathematical and empirical approaches to spatial modeling arises as soon as the metric used to measure distances between outcomes is considered. A typical approach by mathematical modelers is to assume a distance measure that is analytically tractable. By far, the most common assumption is the Euclidean metric, familiar to all who know elementary geometry. The Euclidean metric uses Pythagoras's theorem to measure the distance between two points in a multidimensional space as the square root of the sum of the squares of interpoint distances on each dimension. If individual i, with ideal point xid on dimension d, evaluates outcome j, perceived as having a position xjd on dimension d, then i's perceived Euclidean distance from j, DEij,

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