Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

A testbed to enable comparisons between competing approaches for computational social choice

1. University of Waterloo & New College of Florida 5800 Bayshore Road Sarasota, FL 34234, USA;
2. University of Waterloo 200 University Avenue West Waterloo, ON N2L 3G1, Canada

Within artificial intelligence, the field of computational social choice studies the application of AI techniques to the problem of group decision making, especially through systems where each agent submits a vote taking the form of a total ordering over the alternatives (a preference). Reaching a reasonable decision becomes more difficult when some agents are unwilling or unable to rank all the alternatives, and appropriate voting systems must be devised to handle the resulting incomplete preference information. In this paper, we present a detailed testbed which can be used to perform information analytics in this domain. We illustrate the testbed in action for the context of determining a winner or putting candidates into ranked order, using data from realworld elections, and demonstrate how to use the results of the testbed to produce effective comparisons between competing algorithms.
  Figure/Table
  Supplementary
  Article Metrics

References

[1] H. Azari, D. Parkes and L. Xia, Random Utility Theory for Social Choice, in Advances in Neural Information Processing Systems, NIPS Foundation, 2012, 126-134.

[2] A. Balz and R. Senge, WEKA-LR:A Label Ranking Extension for WEKA, URL http://www.uni-marburg.de/fb12/kebi/research/software/labelrankdoc.pdf.

[3] S. Bouveret, Whale3-WHich ALternative is Elected, URL http://strokes.imag.fr/whale3/.

[4] F. Brandt, G. Chabin and C. Geist, Pnyx:A Powerful and User-friendly Tool for Preference Aggregation, in Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2015, 1915-1916.

[5] C.-C. Chang and C.-J. Lin, LIBSVM:a library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST), 2(2011), 27-66.

[6] G. Charwat and A. Pfandler, Democratix:A Declarative Approach to Winner Determination, in Algorithmic Decision Theory, Springer, 2015, 253-269.

[7] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, 20(1995), 273-297.

[8] J. Dean and S. Ghemawat, MapReduce:simplified data processing on large clusters, Communications of the ACM, 51(2008), 107-113.

[9] P. Diaconis and R. L. Graham, Spearman's footrule as a measure of disarray, Journal of the Royal Statistical Society. Series B (Methodological), 262-268.

[10] J. P. Dickerson, A. D. Procaccia and T. Sandholm, Price of fairness in kidney exchange, in Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems, 2014, 1013-1020.

[11] E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer and A. Weingessel, Misc functions of the Department of Statistics (e1071), TU Wien, R package, 1(2008), 5-24.

[12] J. A. Doucette, K. Larson and R. Cohen, Conventional machine learning for social choice, in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Press, 2015.

[13] G. H. Dunteman, Principal Components Analysis, no. 69 in Quantitative Applications in the Social Sciences, Sage, 1989.

[14] V. E. Farrugia, H. P. Martínez and G. N. Yannakakis, The preference learning toolbox, arXiv preprint arXiv:1506.01709.

[15] M. Gebser, B. Kaufmann, R. Kaminski, M. Ostrowski, T. Schaub and M. Schneider, Potassco:The Potsdam answer set solving collection, AI Communications, 24(2011), 107-124.

[16] M. Gelfond and V. Lifschitz, Classical negation in logic programs and disjunctive databases, New Generation Computing, 9(1991), 365-385.

[17] J. Goldman and A. D. Procaccia, Spliddit:Unleashing fair division algorithms, ACM SIGecom Exchanges, 13(2015), 41-46.

[18] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The WEKA data mining software:an update, ACM SIGKDD explorations newsletter, 11(2009), 10-18.

[19] L. Hatton, The T-experiments:errors in scientific software, in Quality of Numerical Software, Springer, 1997, 12-31.

[20] L. Hatton and A. Roberts, How accurate is scientific software?, IEEE Transactions on Software Engineering, 20(1994), 785-797.

[21] M. R. Hestenes and E. Stiefel, Methods of Conjugate Gradients for Solving Linear Systems1, Journal of Research of the National Bureau of Standards, 49.

[22] E. Hüllermeier and J. Fürnkranz, Learning from label preferences, in Proceedings of the 14th International Conference on Discovery Science, Springer, 2011, 2-17.

[23] T. Joachims, Optimizing search engines using clickthrough data, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2002, 133-142.

[24] T. Joachims, Training linear SVMs in linear time, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2006, 217-226.

[25] D. Kelly and R. Sanders, The challenge of testing scientific software, in In Proceedings of the 2008 Conference for the Association for Software Testing, AST, 2008, 30-36.

[26] M. G. Kendall, A new measure of rank correlation, Biometrika, 30(1938), 81-93.

[27] S. Kullback and R. A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics, 22(1951), 79-86.

[28] T. Lu and C. Boutilier, Learning Mallows models with pairwise preferences, in Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011, 145-152.

[29] T. Lu and C. Boutilier, Robust approximation and incremental elicitation in voting protocols, in Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, AAAI Press, 2011, 287-213.

[30] R. D. Luce, Individual Choice Behavior:A Theoretical Analysis, Wiley, 1959.

[31] C. L. Mallows, Non-null ranking models. I, Biometrika, 44(1957), 114-130.

[32] N. Mattei and T. Walsh, Preflib:A library for preferences http://www.preflib.org, in Algorithmic Decision Theory, Springer, 2013, 259-270.

[33] N. Matti, PrefLib-Tools:A small and lightweight set of Python tools for working with and generating data from www.PrefLib.org., URL https://github.com/nmattei/PrefLib-Tools.

[34] R. Rifkin and A. Klautau, In defense of one-vs-all classification, The Journal of Machine Learning Research, 5(2004), 101-141.

[35] B. Ripley and W. Venables, nnet:Feed-forward neural networks and multinomial log-linear models, R package version, 7.

[36] P. Romanski, FSelector:Selecting attributes, Vienna:R Foundation for Statistical Computing.

[37] C. Spearman, 'Footrule' for measuring correlation, British Journal of Psychology, 1904-1920, 2(1906), 89-108.

[38] J. E. Stone, D. Gohara and G. Shi, OpenCL:A parallel programming standard for heterogeneous computing systems, Computing in Science & Engineering, 12(2010), 66-73.

[39] R. C. Team, R:A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013, ISBN 3-900051-07-0, 2014.

[40] L. Xia and V. Conitzer, Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders., in Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI Press, 2008, 196-201.

Copyright Info: © 2016, John A. Doucette, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved