The 1st Computational Social Choice Competition COMPSOC 2023

In conjunction with IJCAI 2023 in Macao, S.A.R


[August 22] Congratulations to the top 4 winning teams!

1. LanLanCat2023

Roy Fairstein, Georgios Papasotiropoulos, and Bin Sun

Ben-Gurion University, Athens University of Economics & Business, and RWTH Aachen University

2. BinaryTrio

Barna Pasztor, Ermis Soumalias, and Paul Friedrich

ETH AI Center and University of Zurich

3. SY_2023

Soumyajit Pyne and Yeshwant Pandit

Tata Institute of Fundamental Research

4. Bell

Dean Ninalga

University of Toronto

The results will be announced on August 23rd, 15:30-16:50, at Almaty 6104.


[August 16] Final runners displayed on the COMPSOC 2023 Leaderboard!!

[July 27] The deadline for submitting the final version of your rule is approaching. Please keep in mind that you will not be able to update your code afterward. After August 1st, we will start running the finals with newly generated profiles.

[July 1You can access all your submitted reports on your "Rules" page.

[June 29The SDK was updated to support distorted profiles. You can test it using the modified sample rules.

[June 28]  Distorted profiles are on. Any cast vote can contain one or more missing candidates.

[June 27] The profiles are dynamically generated every 3 hours. They are randomly drawn from Random, Gaussian, and Dirichlet-Multinomial distributions of the ballots.

[June 26] The profiles are dynamically generated every 3 hours. They are drawn from Random, Gaussian, and Dirichlet-Multinomial distributions of the ballots.

[June 17] The computational backend was upgraded to increase the performance of the rules and enhance the renderings.

[June 16]  Current execution time allotted per rule is 3 minutes.

[June 16] From July 1st, the profiles will become more dynamic and complex.

[June 13] Pytorch and Tensorflow are now supported in the rules. Let us know if you need additional libraries.

[June 13]  Below the “Current Voting Rules” field on the rule page, you can see the debugging messages (errors, timeouts, etc.).


The field of computational social choice (COMSOC) combines ideas, techniques, and models from computer science and social choice theory for aggregating collective preferences. This thriving and multidisciplinary field of research has numerous applications to group decision-making, resource allocation, fair division, and election systems. One of the most well-studied problems in COMSOC focuses on designing voting mechanisms for selecting the winning candidates for an election. Paradoxes and impossibility results are commonly encountered when implementing voting rules in electoral systems. Researchers are therefore exploring alternatives to classical voting mechanisms by incorporating, for instance, principles and techniques from Machine Learning. Agent-based simulations can also tackle such challenges, as evidenced by their successful applications in negotiation research, supply chain management, and energy markets. In line with this vision, The 1st Computational Social Choice Competition (IJCAI-COMPSOC 2023) capitalizes on the progress in agent research and computational social choice to drive the development of inclusive, robust, and fair election systems.

What is COMPSOC 2023?

The 1st Computational Social Choice Competition (COMPSOC 2023) aims to advance research in computational social choice by leveraging multiagent simulations and machine learning techniques. The competition will focus on the principled evaluation and analysis of voting rules in a competitive setting. The competitors will develop and submit the code of their voting rules, which will then be compared in a tournament based on social welfare and axiomatic satisfiability (anonymity, neutrality, monotonicity, Pareto optimality, unanimity, and non-imposition). The competition aims at providing valuable insights into the performances of voting mechanisms defined over parametrically generated voting problems, alternatives, and voters. COMPSOC will bring together researchers from computational social choice, social sciences, political sciences, multiagent systems, and machine learning and provide a unique benchmark for evaluating voting mechanisms in various synthetic (or real) problem domains. The competition also aims to advance the field by providing a systematic approach to designing and assessing voting mechanisms in the absence of established theoretical results. This advancement will help bridge the gap between axiomatic and experimental analysis of voting systems, ultimately leading to improved explainability.

Flow of the Competition

Your objective as a participant is to design rules that yield maximal social welfare while being robust against any variability in the profiles: number of voters, number of candidates, ballot distributions, and the possibility of having distorted preferences.  When optimizing the rules, you should consider the following (possibly unknown) parameters: TopN, distortion ratio, number of candidates, number of voters, etc.

The top four winning competitors are the competitors with the voting rules that yield the highest social welfare for the multiagent voters (given the baseline ballots of the competition) while satisfying the properties mentioned above. Various sample codes of well-known voting rules will be provided to the participants to guide their implementations (including Borda, Copeland, Dowdall, etc.).

In addition to submitting the Python code of their voting mechanisms, the participants are expected to submit a report describing their mechanism, implementation, and expected results. This will help disseminate the lessons learned from running the competition to the community and set the direction for future tournaments.

The detailed guidelines for the competitions can be found here.

For questions or requests, contact Rafik Hadfi (rafik.hadfi [at]