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03648nam a22005655i 4500 |
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978-3-319-08248-6 |
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DE-He213 |
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20151204190658.0 |
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cr nn 008mamaa |
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140620s2014 gw | s |||| 0|eng d |
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|a 9783319082486
|9 978-3-319-08248-6
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|a 10.1007/978-3-319-08248-6
|2 doi
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|a COM004000
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|2 23
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|a C. Casey, Peter.
|e author.
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|a Fuzzy Social Choice Models
|h [electronic resource] :
|b Explaining the Government Formation Process /
|c by Peter C. Casey, Michael B. Gibilisco, Carly A. Goodman, Kelly Nelson Pook, John N. Mordeson, Mark J. Wierman, Terry D. Clark.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2014.
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|a XIII, 183 p. 29 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a Studies in Fuzziness and Soft Computing,
|x 1434-9922 ;
|v 318
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|a A Fuzzy Public Choice Model -- Fuzzy Preferences: Extraction from Data and Their Use in Public Choice Models -- Fuzzy Single-Dimensional Public Choice Models -- Fuzzy Single-Dimensional Models -- Multi-Dimensional Models -- Government Formation Process -- The Beginnings of a Weighted Model & New Frontiers.
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|a This book explores the extent to which fuzzy set logic can overcome some of the shortcomings of public choice theory, particularly its inability to provide adequate predictive power in empirical studies. Especially in the case of social preferences, public choice theory has failed to produce the set of alternatives from which collective choices are made. The book presents empirical findings achieved by the authors in their efforts to predict the outcome of government formation processes in European parliamentary and semi-presidential systems. Using data from the Comparative Manifesto Project (CMP), the authors propose a new approach that reinterprets error in the coding of CMP data as ambiguity in the actual political positions of parties on the policy dimensions being coded. The range of this error establishes parties’ fuzzy preferences. The set of possible outcomes in the process of government formation is then calculated on the basis of both the fuzzy Pareto set and the fuzzy maximal set, and the predictions are compared with those made by two conventional approaches as well as with the government that was actually formed. The comparison shows that, in most cases, the fuzzy approaches outperform their conventional counterparts. .
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|a Engineering.
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| 650 |
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|a Political science.
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| 650 |
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|a Mathematics.
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| 650 |
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|a Social sciences.
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|a Computational intelligence.
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|a Engineering.
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| 650 |
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|a Computational Intelligence.
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|a Political Science.
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| 650 |
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|a Mathematics in the Humanities and Social Sciences.
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| 700 |
1 |
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|a B. Gibilisco, Michael.
|e author.
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| 700 |
1 |
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|a A. Goodman, Carly.
|e author.
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| 700 |
1 |
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|a Pook, Kelly Nelson.
|e author.
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| 700 |
1 |
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|a N. Mordeson, John.
|e author.
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| 700 |
1 |
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|a J. Wierman, Mark.
|e author.
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| 700 |
1 |
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|a D. Clark, Terry.
|e author.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783319082479
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| 830 |
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|a Studies in Fuzziness and Soft Computing,
|x 1434-9922 ;
|v 318
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-319-08248-6
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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