PrefLib

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.

• Measuring Diversity of Preferences in a Group.. V Hashemi, U Endriss, Proceedings of the European Conference on Artificial Intelligence (2014).
• Single-peaked consistency for weak orders is easy. Z Fitzsimmons, arXiv preprint (2014).
• Testing top monotonicity. H Aziz, arXiv preprint (2014).
• Voting with rank dependent scoring rules. J Goldsmith, J Lang, N Mattei, P Perny, Proceedings of the AAAI Conference on Artificial Intelligence (2014).
• Who is Watching You Eat?. J Goldsmith, N Mattei, RH Sloan, Workshops at the AAAI Conference on Artificial Intelligence (2014).
• How hard is it to control an election by breaking ties?. N Mattei, N Narodytska, T Walsh, arXiv preprint (2013).

Even more references, papers, and tutorials can be found in the proceedings of the EXPLORE Workshops:

• EXPLORE 2017 - Brazil
• EXPLORE 2016 - Singapore
• EXPLORE 2015 - Istanbul
• EXPLORE 2014 - Paris

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.

Iterative Voting Simulator

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".

Learn more.

Kidney Dataset Generator

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.

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CRISNER: A Qualitative Preference Reasoner for CP-nets, TCP-nets, CP-Theories

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.

Learn more.