These Papers are Using PrefLib
Below is a list of papers that have made use of or directly referenced data stored here at PrefLib. The papers have been automatically added from Google Scholar, if there is a problem with a paper or if you want to add one paper, please contact us.
- A Simple Experiment to Guide the Design of a Preference Model.. E Armengol, J Puyol-Gruart, CCIA (2015).
- A study of human behavior in online voting. M Tal, R Meir, Y Gal, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (2015).
- Achieving fully proportional representation: Approximability results. P Skowron, P Faliszewski, A Slinko, Artificial Intelligence (2015).
- Ballot-Level Observations about Vermont's 2014 General Election.. JA Hansen, New England Journal of Political Science (2015).
- Beyond theory and data in preference modeling: Bringing humans into the loop. TE Allen, M Chen, J Goldsmith, N Mattei et al., Proceedings of the International Conference on Algorithmic Decision Theory (2015).
- Complexity of manipulative actions when voting with ties. Z Fitzsimmons, E Hemaspaandra, Proceedings of the International Conference on Algorithmic Decision Theory (2015).
- Computing Optimal Bayesian Decisions for Rank Aggregation via MCMC Sampling.. D Hughes, K Hwang, L Xia, Proceedings of The Conference on Uncertainty in Artificial Intelligence (2015).
- Conventional machine learning for social choice. JA Doucette, K Larson, R Cohen, Proceedings of the AAAI Conference on Artificial Intelligence (2015).
- Democratix: A declarative approach to winner determination. G Charwat, A Pfandler, Proceedings of the International Conference on Algorithmic Decision Theory (2015).
- Egalitarianism of random assignment mechanisms. H Aziz, J Chen, A Filos-Ratsikas, S Mackenzie et al., arXiv preprint (2015).
- Elections with few candidates: Prices, weights, and covering problems. R Bredereck, P Faliszewski, R Niedermeier et al., Proceedings of the International Conference on Algorithmic Decision Theory (2015).
- Empirical analysis of a Food Bank problem. M Aleksandrov, S Gaspers et al., Workshop on Exploring Beyond the Worst Case in Computational Social Choice (2015).
- Equilibria under the probabilistic serial rule. H Aziz, S Gaspers, S Mackenzie, N Mattei et al., Proceedings of the International Joint Conference on Artificial Intelligence (2015).
- Generating single peaked votes. T Walsh, arXiv preprint (2015).
- Multi-round Vote Elicitation for Manipulation under Candidate Uncertainty. M Ayadi, NB Amor, Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (2015).
Even more references, papers, and tutorials can be found in the proceedings of the EXPLORE Workshops:
Some Tools to Work with Preferences
Empirical experiments with real data are becoming a more fundamental part of work in computational social choice. In addition to a lightweight set of tools for working with data from PrefLib we also host documentation for several of these project. Please contact Nick if you have code that you would like to share with the community.
This is a voting simulator built for the paper A Local-Dominance Theory of Voting Equilibria. We are releasing its source code to be expanded and enhanced by the community. However, it is quite versatile in its current construction, and can be used for various simulations "as is".
Kidney failure is a life-threatening health issue that affects hundreds of thousands of people worldwide. In the US alone, the waitlist for a kidney transplant has over 100,000 patients. This list is growing: demand far outstrips supply.This codebase includes: structural elements of kidney exchange like "pools", "hospitals", and "pairs", a couple of kidney exchange graph generators, a couple of kidney exchange solvers (max weight, failure-aware, fairness-aware, individually rational), and a dynamic kidney exchange simulator.
CRISNER stands for Conditional and Relative Importance Statement Network PrEference Reasoner. It can reason about ceteris paribus preference languages such as CP-nets, TCP-nets and CP-theories. Given a preference specification (a set of preference statements) in one of these languages, CRISNER succinctly encodes its induced preference graph (IPG) into a Kripke structure model in the language of the NuSMV model checker. This Kripke model is reachability-equivalent to the induced preference graph. CRISNER generates the model only once, and then translates each query posed against this preference specification into a temporal logic formula in computation-tree temporal logic (CTL) such that the formula is verified in the Kripke model if and only if the query holds true according to the ceteris paribus semantics of the preference language. The model checker either affirms the query or returns with a counterexample. For answering queries related to equivalence and subsumption checking of two sets of preferences, CRISNER constructs a combined IPG and uses temporal queries in CTL to identify whether every dominance that holds in one also holds in the other, and vice-versa.