Multi-district preference modelling

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dc.contributor.author Pritchard, Geoffrey
dc.contributor.author Wilson, Mark C
dc.date.accessioned 2022-05-10T02:56:42Z
dc.date.available 2022-05-10T02:56:42Z
dc.date.issued 2022-03-31
dc.identifier.citation (2022). Quality and Quantity: international journal of methodology, 1-27.
dc.identifier.issn 0033-5177
dc.identifier.uri https://hdl.handle.net/2292/59125
dc.description.abstract Generating realistic artificial preference distributions is an important part of any simulation analysis of electoral systems, for example in trading off decisiveness and proportionality, or in the study of gerrymandering. While preference generation has been discussed in some detail in the context of a single electoral district, many electoral systems of interest are based on multiple districts. Neither treating preferences between districts as independent nor ignoring the district structure yields satisfactory results. We present a model based on a multi-urn extension of the classic Eggenberger-Pólya urn, in which each district is represented by an urn and there is correlation between urns. We show in detail that this procedure has a small number of tunable parameters, is computationally efficient, and produces “realistic-looking" distributions. We present several applications to retrospective analysis and forecasting of real elections.
dc.language en
dc.publisher Springer Science and Business Media LLC
dc.relation.ispartofseries Quality & Quantity
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.subject 0104 Statistics
dc.subject 1608 Sociology
dc.subject 1701 Psychology
dc.title Multi-district preference modelling
dc.type Journal Article
dc.identifier.doi 10.1007/s11135-022-01377-x
pubs.begin-page 1
dc.date.updated 2022-04-25T23:45:30Z
dc.rights.holder Copyright: The author en
pubs.end-page 27
pubs.publication-status Published online
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Journal Article
pubs.elements-id 896511
pubs.org-id Science
pubs.org-id School of Computer Science
pubs.org-id Statistics
dc.identifier.eissn 1573-7845
pubs.record-created-at-source-date 2022-04-26
pubs.online-publication-date 2022-03-31


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