https://openreview.net/group?id=ijcai.org/IJCAI-ECAI/2026/Workshop/CFD
All dates are 11:59 pm, Anywhere on Earth (AoE):
Submission Deadline: May 4th 2026
Notification of Acceptance: June 6, 2026
Papers should be submitted in IJCAI format, with a 7 page limit. References and supplementary material can be included in the main paper body, and will not count towards the 7 page limit, but reviewers will not be required to read past 7 pages.
Note that (1) author names may be included in the submissions and (2) the workshop is non-archival.
Fair division answers the question of how to allocate resources to agents with competing preferences, typically with some fairness, economic efficiency, and incentive-compatibility guarantees. The field has grown rapidly in recent years, and we now have developed theories that cover many important areas of interest. However, many intriguing, theoretical open questions remain, and even mature algorithms from fair division typically require subtle modifications in practice. The study of fair allocation is defined more by a class of problems rather than specific types of solutions, making it a welcoming home for researchers in many fields. Combinatorial optimization, approximation algorithms, mechanism design, machine learning, and operations research have all been historical workhorses, but new approaches are always needed. This workshop brings together fair division researchers from all walks of life; theoretical, empirical, and applied; to discuss how to apply fair division to the challenges of modern society.
Fair division has been a subject of sustained interest at IJCAI. Many recent winners of the IJCAI Computer and Thought Award have worked on fair division and matching, including Sarit Kraus (1995), Nicholas Jennings (1999), Tuomas Sandholm (2003), Peter Stone (2007), Vincent Conitzer (2011), Ariel Procaccia (2015), Piotr Skowron (2020), and Fei Fang (2021).
Multiple tutorials at previous IJCAI conferences covered topics related to fair division (Constraints in Fair Division, Distortion in Social Choice and Beyond, and Mechanism Design without Money), and the M-PREF workshop at IJCAI covers preference learning - an important input to fair division.
List of relevant topic areas:
Classic fair allocation of indivisible items
Resource allocation problems (e.g., cake cutting, house allocation, matching, or apportionment)
Constrained fair division
Uncertainty & distortion in fair division
Fair division in social networks
Budget allocation
Market design
Competitive/market equilibria
Combinatorial auctions or optimization with fairness consideration
Perceived fairness; fairness in collective decision-making
Proportional representation
Apportionment methods
Fair representation
Fairness in cooperative game theory
Incentives in fair division
Automated theorem proving/SAT solving approaches for fair division
Empirical analysis of resource allocation problems
Datasets for and tools demonstrating practical implementation of fair division algorithms
ML approaches to fair division (e.g., learned preferences or on-line procedures)
Cooperative AI, Agentic AI, and LLM approaches to fair division
Applications of fair division approaches to other algorithmic fairness problems (e.g. ranking, fair LLMs, etc)
Task allocation in multi-robotic systems