---
title: "Fair and Efficient Resource Allocation with Partial Information"
authors:
  - "Daniel Halpern"
  - "Nisarg Shah"
doi: "10.24963/ijcai.2021/32"
url: "https://ijcai.org/proceedings/2021/32"
pdf: "https://ijcai.org/proceedings/2021/0032.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "224-230"
track: "Main Track"
keywords:
  - "Agent-based and Multi-agent Systems: Resource Allocation"
  - "Agent-based and Multi-agent Systems: Computational Social Choice"
  - "AI Ethics, Trust, Fairness: Fairness"
---

# Fair and Efficient Resource Allocation with Partial Information

Daniel Halpern, Nisarg Shah

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 224-230. DOI: [10.24963/ijcai.2021/32](https://doi.org/10.24963/ijcai.2021/32)

## Abstract

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.