Fair and Efficient Resource Allocation with Partial Information

We study the fundamental problem of allocating indivisible goods to agents with additive preferences. We consider eliciting from each agent only a ranking of her k most preferred goods instead of her full cardinal valuations. We characterize the amount of preference information that must be elicited in order to satisfy envy-freeness up to one good and approximate maximin share guarantee, two widely studied fairness notions. We also analyze the multiplicative loss in social welfare incurred due to the lack of full information with and without fairness requirements.

Authors

• Daniel Halpern Harvard University
• Nisarg Shah University of Toronto

Keywords

• Agent-based and Multi-agent Systems: Computational Social Choice
• Agent-based and Multi-agent Systems: Resource Allocation
• AI Ethics, Trust, Fairness: Fairness