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.
- On the complexity of bribery and manipulation in tournaments with uncertain information. N Mattei, J Goldsmith, A Klapper, M Mundhenk, Journal of Applied Logic (2015).
- Online fair division: analysing a food bank problem. MD Aleksandrov, H Aziz, S Gaspers, T Walsh, Proceedings of the International Joint Conference on Artificial Intelligence (2015).
- Pnyx: a powerful and user-friendly tool for preference aggregation. F Brandt, G Chabin, C Geist, Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (2015).
- Privacy in Elections: k-Anonymizing Preference Orders. N Talmon, International Symposium on Fundamentals of Computation Theory (2015).
- Realistic assumptions for attacks on elections. Z Fitzsimmons, Proceedings of the AAAI Conference on Artificial Intelligence (2015).
- The complexity of recognizing incomplete single-crossing preferences. E Elkind, P Faliszewski, M Lackner et al., Proceedings of the AAAI Conference on Artificial Intelligence (2015).
- What do we elect committees for? A voting committee model for multi-winner rules. PK Skowron, Proceedings of the International Joint Conference on Artificial Intelligence (2015).
- Who is watching you eat? a noir preferences thriller. J Goldsmith, N Mattei, RH Sloan, AI Matters (2015).
- Approximate winner selection in social choice with partial preferences. JA Doucette, K Larson, R Cohen, Workshop on Exploring Beyond the Worst Case in Computational Social Choice (2014).
- Computational aspects of multi-winner approval voting. H Aziz, S Gaspers, J Gudmundsson et al., Workshops at the AAAI Conference on Artificial Intelligence (2014).
- Controlling elections by replacing candidates: Theoretical and experimental results. A Loreggia, N Narodytska, F Rossi, KB Venable et al., Workshops at the AAAI Conference on Artificial Intelligence (2014).
- Counting, ranking, and randomly generating CP-nets. TE Allen, J Goldsmith, N Mattei, Workshops at the AAAI Conference on Artificial Intelligence (2014).
- Election attacks with few candidates. Y Yang, arXiv preprint (2014).
- From Sentiment Analysis to Preference Aggregation.. U Grandi, A Loreggia, F Rossi, VA Saraswat, ISAIM (2014).
- Incomplete preferences in single-peaked electorates. M Lackner, Proceedings of the AAAI Conference on Artificial Intelligence (2014).
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.