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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n Pritchard, G.; and Wilson, M. C\n\n\n \n \n \n \n \n Multi-district preference modelling.\n \n \n \n \n\n\n \n\n\n\n Quality & Quantity, 57(1): 587–613. 2023.\n \n\n\n\n
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@article{pritchard2023multi,\n  title={Multi-district preference modelling},\n  author={Pritchard, Geoffrey and Wilson, Mark C},\n  journal={Quality \\& Quantity},\n  volume={57},\n  keywords={electoral systems},\n  number={1},\n  pages={587--613},\n  year={2023},\n  url_Paper={},\n  abstract   = {Generating realistic artificial preference distributions is an important\npart of any simulation analysis of electoral systems. While this has\nbeen discussed in some detail in the context of a single electoral\ndistrict, many electoral systems of interest are based on districts.\nNeither treating preferences between districts as independent nor\nignoring the district structure yields satisfactory results. We present\na model based on an extension of the classic Eggenberger-P\\'{o}lya urn,\nin which each district is represented by an urn and there is correlation\nbetween urns. We show in detail that this procedure has a small number\nof tunable parameters, is computationally efficient, and produces\n{``}realistic-looking{"} distributions. We present applications to\nretrospective analysis and forecasting of real elections, and intend to\nuse the methodology to help set optimal parameters for electoral\nsystems. (Subsumes SocArXiv paper xpb8w from 2018)}\n}\n\n
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\n Generating realistic artificial preference distributions is an important part of any simulation analysis of electoral systems. While this has been discussed in some detail in the context of a single electoral district, many electoral systems of interest are based on districts. Neither treating preferences between districts as independent nor ignoring the district structure yields satisfactory results. We present a model based on an 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 applications to retrospective analysis and forecasting of real elections, and intend to use the methodology to help set optimal parameters for electoral systems. (Subsumes SocArXiv paper xpb8w from 2018)\n
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n Grofman, B. N.; and Wilson, M. C.\n\n\n \n \n \n \n \n Models of inter-election change in partisan vote share.\n \n \n \n \n\n\n \n\n\n\n J. Theoretical Politics, 34: 481–498. 2022.\n \n\n\n\n
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@Article{GrWi2020,\n  author     = {Grofman, Bernard N. and Wilson, Mark C.},\n  title      = {Models of inter-election change in partisan vote share},\n  pages      = {481--498},\n  volume\t = {34},\n  issue      = {4},\n  journal    = {J. Theoretical Politics},\n  abstract   = {Consider an election of a given type (say a legislative or presidential\nelection) involving some set of districts for which we have data at two\ndistinct points in time, time 1 and time 2. If there are only two\npolitical parties, A and B, we may think of the overall difference in\nthe mean party vote share of party A between the two elections as the\naggregate inter-election swing between the two parties.  If we know this\nmean inter-election swing, the question we seek to answer is: ``How do\nwe expect the aggregate swing to be distributed across the districts or\nstates (or smaller units such as counties) as a function of previous\nvote share (and perhaps, other factors)?''\n\nIn the electoral systems and political party literatures there have been\ntwo main answers to that question: uniform swing and  proportional\nswing. Our main theoretical contributions are (a) to provide an\naxiomatic foundation  for desirable properties of a model of\ninter-election changes in vote shares in a districted legislature; (b)\nto use those axioms to demonstrate why using uniform swing or\nproportional swing is a bad idea, (c) to provide a reasonably simple\nswing model that does satisfy the axioms, and (d) to show how to\nintegrate a reversion to the mean effect into models of inter-election\nswing.\n\nOur main empirical contributions address the question of why, despite\ntheir theoretical flaws, there is strong evidence that the two standard\nmodels, especially uniform swing, provide a very good fit to empirical\ndata.  We show that these models can be expected to work well when (a)\nelections are close, or (b) when we restrict ourselves to data where\nswing is low, or  (c) when we eliminate the cases where the model is\nmost likely to go wrong. In particular, sometimes the model tested is\nnot the standard model; in that either (c1) a piecewise or truncated\nvariant of the model is being used,  or (c2) there is a correction\n(usually 75\\%) for districts that are uncontested.  As we show\nempirically with data from U.S. congressional elections, either of these\ncorrections will at least marginally  improve fit on one or more of five\nindicators: mistakes about directionality of change, mistakes in winner,\nestimates that are outside the [0..1] bounds, mean-square error, and\ncorrelation between actual and predicted values. We also show that our\nnew model provides an equal or better fit to U.S. congressional data\nthan the traditional models, while having much nicer axiomatic\nproperties.},\n  keywords   = {electoral systems},\n  url_paper  = {https://markcwilson.site/Research/Outputs/GrWi2020.pdf},\n  url_slides = {https://www.youtube.com/watch?v=C0eIF1oAk-U},\n  year       = {2022},\n}\n\n
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\n Consider an election of a given type (say a legislative or presidential election) involving some set of districts for which we have data at two distinct points in time, time 1 and time 2. If there are only two political parties, A and B, we may think of the overall difference in the mean party vote share of party A between the two elections as the aggregate inter-election swing between the two parties. If we know this mean inter-election swing, the question we seek to answer is: ``How do we expect the aggregate swing to be distributed across the districts or states (or smaller units such as counties) as a function of previous vote share (and perhaps, other factors)?'' In the electoral systems and political party literatures there have been two main answers to that question: uniform swing and proportional swing. Our main theoretical contributions are (a) to provide an axiomatic foundation for desirable properties of a model of inter-election changes in vote shares in a districted legislature; (b) to use those axioms to demonstrate why using uniform swing or proportional swing is a bad idea, (c) to provide a reasonably simple swing model that does satisfy the axioms, and (d) to show how to integrate a reversion to the mean effect into models of inter-election swing. Our main empirical contributions address the question of why, despite their theoretical flaws, there is strong evidence that the two standard models, especially uniform swing, provide a very good fit to empirical data. We show that these models can be expected to work well when (a) elections are close, or (b) when we restrict ourselves to data where swing is low, or (c) when we eliminate the cases where the model is most likely to go wrong. In particular, sometimes the model tested is not the standard model; in that either (c1) a piecewise or truncated variant of the model is being used, or (c2) there is a correction (usually 75%) for districts that are uncontested. As we show empirically with data from U.S. congressional elections, either of these corrections will at least marginally improve fit on one or more of five indicators: mistakes about directionality of change, mistakes in winner, estimates that are outside the [0..1] bounds, mean-square error, and correlation between actual and predicted values. We also show that our new model provides an equal or better fit to U.S. congressional data than the traditional models, while having much nicer axiomatic properties.\n
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\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n Wilson, M. C.; and Pritchard, G.\n\n\n \n \n \n \n \n Simulating the 2011 Referendum in New Zealand.\n \n \n \n \n\n\n \n\n\n\n Parliamentary Affairs, 67(4): 969-980. 2014.\n \n\n\n\n
\n\n\n\n \n \n \"Simulating paper\n  \n \n \n \"Simulating slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{wilson2014simulating,\n  title={Simulating the 2011 Referendum in New Zealand},\n  author={Wilson, Mark C. and Pritchard, Geoffrey},\n  journal={Parliamentary Affairs},\n  volume={67},\n  number={4},\n  pages={969-980},\n  year={2014},\n  publisher={Oxford University Press},\n  keywords={electoral systems, simulation},\n  url_Paper={https://markcwilson.site/Research/Outputs/PrWi2013.pdf},\n  url_Slides={https://www.stat.auckland.ac.nz/~geoff/voting/},\n  abstract={On 26 November 2011, an indicative referendum was held in New Zealand\nwith the aim of gauging public support for a change from the current\nparliamentary electoral system (Mixed Member Proportional) to one of\nfour alternatives. In order to understand the consequences (in terms of\nthe seat distribution of parties in Parliament) of a change in electoral\nsystem, we created an online simulator several months before the\nreferendum date. Several interesting research issues arose in this work,\nwhich in our opinion deserve greater analysis. We describe the\nassumptions made in order to create such a simulator, and their\nconsequences.}\n}\n\n
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\n On 26 November 2011, an indicative referendum was held in New Zealand with the aim of gauging public support for a change from the current parliamentary electoral system (Mixed Member Proportional) to one of four alternatives. In order to understand the consequences (in terms of the seat distribution of parties in Parliament) of a change in electoral system, we created an online simulator several months before the referendum date. Several interesting research issues arose in this work, which in our opinion deserve greater analysis. We describe the assumptions made in order to create such a simulator, and their consequences.\n
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\n  \n 2012\n \n \n (1)\n \n \n
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\n \n\n \n \n Wilson, M. C.; and Fowlie, M.\n\n\n \n \n \n \n \n Submission to the Electoral Commission review of MMP.\n \n \n \n \n\n\n \n\n\n\n 2012.\n \n\n\n\n
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@misc{WiFo2012,\n  title={Submission to the Electoral Commission review of MMP},\n  author={Wilson, Mark C. and Fowlie, Michael},\n  year={2012},\n  publisher={Electoral Commission of New Zealand},\n   keywords={electoral systems},\n  url_Paper={https://elections.nz/assets/2012-report-of-the-Electoral-Commission-on-the-review-of-mmp.pdf},\n  abstract={},\n}\n\n
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