PrefLib

The community

We want to thank all who participated in the development of PrefLib. This list may not be exhaustive, but we hope it is. Contact us if you feel unjustly treated.

• Haris Aziz and Omer Lev: who provided great help at the launch of the website, discussing the design of the first version of the website, and troubleshooting it.
• Niclas Boehmer and Nathan Schaar: who contributed a large amount of data, as summarized on this page.
• Dominik Peters: who provided useful feedback, created the amazing new logo, committed some code, and contributed the Breakfast dataset.
• Jeffrey O'Neill: maintainer of the website OpenSTV.org. (now OpaVote) who contributed a lot of datasets: irish, Burlington, Glasgow, Aspen, Berkley, Minneapolis, Oakland, Pierce, San Francisco, San Leandro, Takoma Park.
• Robert Bredereck: who contributed the F1 and Skiing, Web Search, and the Web Search Clean datasets.
• Jean-Francois Laslier: who contributed the datasets French Presidental Election, Proto French Election, and its rating counterpart; together with Karine Van der Straeten, and Michel Balinski.
• Andrew Mao: who contributed the Dots, and Puzzle datasets.
• Nicolaus Tideman and Florenz Plassmann: who contributed the ERS dataset.
• Piotr Faliszewski: who contributed the AGH dataset.
• Carleton Coffrin: who contributed the T-Shirt dataset.
• Lihi Dery: who contributed the Social Recommendation dataset.
• Toshihiro Kamishima: who contributed the Sushi dataset.
• Michal Regenwetter and Anna Popova: who contributed the APA dataset.
• Jeremy A. Hansen: who contributed the Vermont District Races dataset.
• Alejandro Rosete Suarez and Milton Garcia Borroto: who contributed the Cujae dataset.
• Ulle Endriss: who contributed the Poster Competition dataset.
• Ioannis Caragiannis: who contributed the Cities Survey dataset.
• John P. Dickerson: who contributed the Kidney dataset.
• Rafael Bordini, Edith Elkind, John Thangarajah, and David Shield: who contributed the AAMAS dataset.
• David Manlove: who contributed the Project Bidding dataset.
• Hongning Wang: who contributed the Trip Advisor dataset.

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.

• Investigating the characteristics of one-sided matching mechanisms under various preferences and risk attitudes. H Hosseini, K Larson, R Cohen, Autonomous Agents and Multi-Agent Systems (2018).
• Learning mixtures of random utility models. Z Zhao, T Villamil, L Xia, Proceedings of the AAAI Conference on Artificial Intelligence (2018).
• On recognising nearly single-crossing preferences. F Jaeckle, D Peters, E Elkind, Proceedings of the AAAI Conference on Artificial Intelligence (2018).
• Optimal Multi-Attribute Decision Making in Social Choice Problems.. S Sikdar, Proceedings of the International Joint Conference on Artificial Intelligence (2018).
• Preferences and ethical principles in decision making. A Loreggia, N Mattei, F Rossi, KB Venable, AAAI Spring Symposium (2018).
• Smart Machines ARE a Threat to Humanity. K Warwick, Artificial Intelligence Safety and Security (2018).
• The conference paper assignment problem: Using order weighted averages to assign indivisible goods. JW Lian, N Mattei, R Noble, T Walsh, Proceedings of the AAAI Conference on Artificial Intelligence (2018).
• [CITATION][C] On Level-1 Consensus Ensuring Stable Social Choice. M Nitzan, S Nitzan, E Segal-Halevi, arXiv preprint (2017).
• A differential evolution algorithm for finding the median ranking under the Kemeny axiomatic approach. A D'Ambrosio, G Mazzeo, C Iorio, R Siciliano, Computers & Operations Research (2017).
• A reward-based approach for preference modeling: A case study. E Armengol, J Puyol-Gruart, Journal of Applied Logic (2017).
• A semantic loss function for deep learning with symbolic knowledge. J Xu, Z Zhang, T Friedman, Y Liang et al., arXiv preprint (2017).
• APreflib. ORG Retrospective: Lessons Learned and New Directions. N Mattei, T Walsh, Trends in Computational Social Choice (2017).
• How hard is control in single-crossing elections?. K Magiera, P Faliszewski, Autonomous Agents and Multi-Agent Systems (2017).
• How to form winning coalitions in mixed human-computer settings. M Mash, Y Bachrach, Y Zick, Proceedings of the International Joint Conference on Artificial Intelligence (2017).
• Learning tree-structured CP-nets with local search. TE Allen, C Siler, J Goldsmith, Proceedings of the International Flairs Conference (2017).

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.

Learn more.

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.