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Voting Questions that Might Interest Computing Specialists

Explore the frequency of cycles in voting, susceptibility to strategic voting, and the impact of voting rules on outcomes using spatial models. Learn how to test and predict spatial models in elections, simulate election data effectively, and challenge yourself to distinguish between real and simulated data. Improve estimation speed, extend models, and delve into computational aspects like STV and CPO-STV methods. Collaborate with experts in the field and access valuable resources for further exploration.

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Voting Questions that Might Interest Computing Specialists

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  1. Voting Questions that Might Interest Computing Specialists

  2. Things to Explore • Frequency of cycles (a function of the number of candidates and the number of voters) • Susceptibility to strategic voting • Frequency with which the choice of a voting rule makes a difference in the outcome Such analyses require • either lots of voting data • or data simulated with a statistical model of the process that generates voting outcomes.

  3. A Spatial Model of Elections • Candidates have known locations in “attribute space.” • Every voter has spherical preferences in attribute space (or indifference contours are concentric hyper-ellipses of the same shape and orientation) • Voters’ ideal points are drawn independently from a multivariate normal distribution in attribute space.

  4. How many rankings can be observed? • If there are n candidates, then all rankings can be observed if and only if the dimensionality of the space of ideal points is greater than or equal to n – 1. • For the number of rankings that can be observed in other cases, see Good and Tideman, “Sterling Numbers and a Geometric Structure from Voting Theory” Journal of Combinatorial Theory 23 (July 1977), 34‑45.

  5. Test of the Spatial Model • Use ranking data from three-candidate elections (1) 913 “elections” compiled from the American National Election Survey (ANES) (2) 883 three-candidate comparisons from elections counted by the UK Electoral Reform Society (ERS) (3) 82,754 “elections” compiled from the German Politbarometer(1977 – 2008) • Five degrees of freedom per election in the • data, four degrees of freedom in the model.

  6. Prediction of the Spatial Model pCAB = 0.1, pCBA = 0.1 pABC pBCA = 0.1 (scaled) … Along each curve: Vary pBAC and find the combination of pABC and pACB that satisfy the spatial model. pBCA = 0.6 pACB (scaled) 6

  7. Results – Distance from model(.061 for random points)

  8. Simulations: The Frequencies of Cycles

  9. What might account for excess distance? • Lack of agreement about locations? • Non-normality of ideal points? • Non-independence of probabilities? • All invite using the • Pólya (Dirichlet-multinomial) distribution

  10. Things to do • Get more data—particularly on elections • Replicate our results with additional data • Estimate parameters for simulating elections Challenge:Can you simulate elections so well that I can’t tell the difference between real data and simulated data?

  11. More Things to Do • Improve the speed of spatial model estimation(help us develop a new program for Gaussian quadrature) • Extend the model to four candidates in three dimensions (quadrature in three dimensions) (co-author and programmer for the effort: Florenz Plassmann, fplass@binghamton.edu) Would you like to help?

  12. Quadrature in Three Dimensions Slice the tri-variate normal distribution into • 24 central triangular cones • 36 central planar wedges • 36 (?) triangular columns • x tetrahedrons Put them together in the right combinations Would you like to help?

  13. Conjecture • If one wants to know the integrals (of any point-symmetric function) over each of the n! cones into which an (n-1) space is divided by the bisecting hyperplanes of the line segments connecting n points, then it is sufficient to compute the (n-1)! integrals in (n-1)-space associated with the cones where one of the points is closest or farthest, supplemented by various integrals in (n-2)-space or lower. Would you like to prove it?

  14. Another Line of Inquiry: STV • Counting votes by STV • Compute the quota: n/(k+1) • Allocate each vote to the candidate ranked first • Distribute surpluses • Delete the candidate with the fewest votes • Start again • Problems of sequential elimination • Alternative of global comparisons: CPO-STV

  15. The Computational Problem of CPO-STV • Counting votes for a CPO-STV comparison • Compute the quota: n/(k+1) • Allocate each vote to the candidate ranked first • Distribute surpluses – Stop. • Evaluate the matrix of paired comparisons of outcomes by your favorite matrix evaluation rule • There is a potential problem of a combinatorial explosion. How serious is it? • What approximations are interesting? Would you like to explore it?

  16. Are you interested? • Florenz and I will provide our data and programs to anyone who is interested • If you would like to collaborate, get in touch. • If you would like to buy my book, Collective Decisions and Voting (Ashgate, 2006)  use promotion code S1DTS20 at the UK Ashgate Web site during the next three months, and you should get it for 20% off (£52 rather than £65).

  17. Thank you! Nicolaus Tideman Virginia Tech ntideman@vt.edu Florenz Plassmann Binghamton University fplass@binghamton.edu

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