To properly analyze the advantages and disadvantages of voting rules, and how well the outcomes that they yield reflect voters' preferences, one needs very large data sets, since paradoxes that occur very rarely may have large impacts. Since such amounts of election data are currently unavailable, it is important to be able to use random procedures to generate data that have the same statistical characteristics as real election data. It is the purpose of this work to identify a statistical characterization of voting data, to empower researchers to use random procedures to generate data that is statistically indistinguishable from real voting data. / Ph. D. / Democracies use various rules to determine the winners of elections. The plurality rule, under which each voter votes for one candidate and the candidate with the most votes wins, is one example. One can add a specification that when no candidate receives a majority of the votes there will be a run-off, which will sometimes change the outcome. There are many possible voting rules; all have their benefits and limitations. Some rules can yield unsatisfying anomalies, possibly with very small probability. Since such anomalies might occur very rarely, to estimate their frequency one needs data from a substantial number of elections, more elections than are available from historical experience. Thus to undertake research on voting rules, one needs a procedure for generating data that have the same statistical characteristics as real election data. The purpose of this work is to identify enough of the statistical properties of realistic voting data (from surveys) to permit researchers to generate an unlimited amount of simulated election data, so that they can analyze the frequency of various anomalies under different voting rules.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/73579 |
| Date | 06 December 2016 |
| Creators | Matje, Thorsten |
| Contributors | Economics, Science, Tideman, Nicolaus, Plassman, Florenz, Ball, Sheryl B., Bahel, Eric A., Tsang, Kwok Ping |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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