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:
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.