Call For papers

Call for Papers

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

• House allocation

• Constrained fair division

• Uncertainty & distortion in fair division

• Practical applications of fair division (healthcare, education, sustainability, etc. )

• Empirical analysis of resource allocation problems

• Fair division with learned preferences

• Fair division in social networks

• Online fair division and matching

• Fairness in other resource allocation problems, e.g., matching, apportionment, etc.

• Resource allocation in multi-agent systems

• Budget allocation

• Market design

• Combinatorial auctions

• Incentives in fair division

• Competitive/market equilibria

• Cake cutting

• Automated theorem proving/SAT solving approaches for fair division

• Datasets for and practical implementation of fair division algorithms

• Agentic AI, allocation of resources, LLM approaches to fair division

• Multi-robotic systems task division, combinatorial opt, or other operations research problems with fairness considerations