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Explore the Euclidean Spatial Model in political science, dimension meanings, and conditions for stable points. Discover the workhorse of empirical studies with expert domain knowledge and data fitting. Learn about the complexity of recognizing stable points.
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PART II SPATIAL (EUCLIDEAN) MODEL
Definition of Spatial Model • Voter i has ideal (bliss) point xi2<k • Each alternative is represented by a point in <k • A1¸i A2 iff ||xi-A1|| · || xi – A2|| • Can use norms other than Euclidean e.g. ellipsoidal indifference curves
Spatial Model • Largely descriptive role rather than normative • The workhorse of empirical studies in political science • k=1,2 are the most popular # of dimensions • In U.S. k=2 gives high accuracy (~90%) , k=1 also very accurate since 1980s, and 1850s to early 20th century.
What do the dimensions mean? Different schools of thought • Use expert domain knowledge or contextual information to define dimensions and/or place alternatives • Fit data (e.g. roll call) to achieve best fit • Maximize data fit in 1st dimension, then 2nd • Impute meaning to fitted model
2D is qualitatively richer than 1D x1 A1 A2 x3 x2 A3 A1>A2>A3>A1
Condorcet’s voting paradox in Euclidean model x1 A1 A2 x3 x2 A3 Hyperplane normal to and bisecting line segment A1A2
Even if all points in <2 are permitted alternatives, no Condorcet winner exists x1 A1 A2 x3 x2
Major Question: Conditions for Existence of Stable Point (Undominated, Condorcet Winner) • Plott (67) For case all xi distinct • Slutsky(79) General case, not finite • Davis, DeGroot, Hinich (72) Every hyperplane through x is median, i.e. each closed halfspace contains at least half the voter ideal points. • McKelvey, Schofield (87) More general, finite, but exponential. Are there better conditions?
Recognizing a Stable (Undominated) Point is co-NP-complete Theorem: Given x1…xn and x0 in <k, determining whether x0 is dominated is NP-complete. Proof: Johnson & Preparata 1978. Algorithm: In O(kn) given x_1…x_n can find x_0 which is undominated if any point is. Corollary: Majority-rule stability is co-NP-complete.
Implications • Puts to rest efforts to find simpler necessary and sufficient conditions • Computing the radius of the yolk is NP-hard • Computing any other solution concept that coincides with Condorcet winner when it exists, is NP-hard