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Titlebook: Applying Fuzzy Mathematics to Formal Models in Comparative Politics; Terry D. Clark,Jennifer M. Larson,Mark J. Wierman Book 2008 Springer-

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期刊全稱Applying Fuzzy Mathematics to Formal Models in Comparative Politics
影響因子2023Terry D. Clark,Jennifer M. Larson,Mark J. Wierman
視頻videohttp://file.papertrans.cn/161/160242/160242.mp4
發(fā)行地址Applications of fuzziness to politics
學(xué)科分類Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Applying Fuzzy Mathematics to Formal Models in Comparative Politics;  Terry D. Clark,Jennifer M. Larson,Mark J. Wierman Book 2008 Springer-
影響因子.This book explores the intersection of fuzzy mathematics and the spatial modeling of preferences in political science. Beginning with a critique of conventional modeling approaches predicated on Cantor set theoretical assumptions, the authors outline the potential benefits of a fuzzy approach to the study of ambiguous or uncertain preference profiles. While crisp models assume that ambiguity is a form of confusion emerging from imperfect information about policy options, the authors argue instead that some level of ambiguity is innate in human preferences and social interaction. What fuzzy mathematics offers the researcher, then, is a precise tool with which he can model the inherently imprecise dimensions of nuanced empirical reality. Moving beyond the limited treatment fuzzy methodologies have received in extant political science literature, this book develops single- and multidimensional models of fuzzy preference landscapes and characterizes the surprisingly high levels of stability that emerge from interactions between players operating within these models. The material presented makes it a good text for a graduate seminar in formal modeling. It is also suitable as an introdu
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Fuzzy One-Dimensional Models, over any two distinct policies however similar, the model yields predictions about when a president would accept a proposal and when she would exercise a veto. We conclude the crisp section by explaining limitations implicit in such assumptions, limitations that a fuzzy reformulation can bypass..Th
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Estimating Fuzzy Policy Preferences, we leave the issue open and instead use an understanding of intensity to guide our choice of aggregation operators, discussed in the next section..Spatial models are useful because they provide a visual representation of preferences. To derive predictions from a spatial model, we need to know how a
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