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.
- Empirical evaluation of real world tournaments. N Mattei, T Walsh, arXiv preprint (2016).
- Finding a collective set of items: From proportional multirepresentation to group recommendation. P Skowron, P Faliszewski, J Lang, Artificial Intelligence (2016).
- Generating CP-nets uniformly at random. TE Allen, J Goldsmith, HE Justice, N Mattei et al., Proceedings of the AAAI Conference on Artificial Intelligence (2016).
- Judgment aggregation under issue dependencies. M Costantini, C Groenland, U Endriss, Proceedings of the AAAI Conference on Artificial Intelligence (2016).
- Modeling single-peakedness for votes with ties. Z Fitzsimmons, E Hemaspaandra, arXiv preprint (2016).
- On the discriminative power of tournament solutions. F Brandt, HG Seedig, Operations Research Proceedings 2014 (2016).
- Online Fair Division Redux.. M Aleksandrov, Proceedings of the International Joint Conference on Artificial Intelligence (2016).
- Optimal aggregation of uncertain preferences. AD Procaccia, N Shah, Proceedings of the AAAI Conference on Artificial Intelligence (2016).
- Position-indexed formulations for kidney exchange. JP Dickerson, DF Manlove, B Plaut et al., Proceedings of the International Joint Conference on Artificial Intelligence (2016).
- Proportional rankings. P Skowron, M Lackner, M Brill, D Peters et al., arXiv preprint (2016).
- Proxy voting for better outcomes. G Cohensius, S Manor, R Meir, E Meirom et al., arXiv preprint (2016).
- Solving hard control problems in voting systems via integer programming. S Polyakovskiy, R Berghammer, F Neumann, European Journal of Operational Research (2016).
- Strategyproof peer selection: Mechanisms, analyses, and experiments. H Aziz, O Lev, N Mattei, JS Rosenschein et al., Proceedings of the AAAI Conference on Artificial Intelligence (2016).
- SVVAMP: simulator of various voting algorithms in manipulating populations. F Durand, F Mathieu, L Noirie, Proceedings of the AAAI Conference on Artificial Intelligence (2016).
- The random pairs voting rule: Introduction and evaluation with a large dataset. J Hansen, Proceedings of the Workshop on Computational Social Choice (2016).
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.