000 03647nam a22005775i 4500
001 978-3-319-08248-6
003 DE-He213
005 20200421112546.0
007 cr nn 008mamaa
008 140620s2014 gw | s |||| 0|eng d
020 _a9783319082486
_9978-3-319-08248-6
024 7 _a10.1007/978-3-319-08248-6
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aC. Casey, Peter.
_eauthor.
245 1 0 _aFuzzy Social Choice Models
_h[electronic resource] :
_bExplaining the Government Formation Process /
_cby Peter C. Casey, Michael B. Gibilisco, Carly A. Goodman, Kelly Nelson Pook, John N. Mordeson, Mark J. Wierman, Terry D. Clark.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIII, 183 p. 29 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v318
505 0 _aA 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.
520 _aThis 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. .
650 0 _aEngineering.
650 0 _aPolitical science.
650 0 _aMathematics.
650 0 _aSocial sciences.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aPolitical Science.
650 2 4 _aMathematics in the Humanities and Social Sciences.
700 1 _aB. Gibilisco, Michael.
_eauthor.
700 1 _aA. Goodman, Carly.
_eauthor.
700 1 _aPook, Kelly Nelson.
_eauthor.
700 1 _aN. Mordeson, John.
_eauthor.
700 1 _aJ. Wierman, Mark.
_eauthor.
700 1 _aD. Clark, Terry.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319082479
830 0 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v318
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-08248-6
912 _aZDB-2-ENG
942 _cEBK
999 _c58599
_d58599