---
title: "Budgeted Social Choice: From Consensus to Personalized Decision Making"
authors:
  - "Tyler Lu"
  - "Craig Boutilier"
doi: "10.5591/978-1-57735-516-8/IJCAI11-057"
url: "https://ijcai.org/proceedings/2011/57"
pdf: "https://ijcai.org/proceedings/2011/0057.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "280-285"
track: ""
keywords:
---

# Budgeted Social Choice: From Consensus to Personalized Decision Making

Tyler Lu, Craig Boutilier

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 280-285. DOI: [10.5591/978-1-57735-516-8/IJCAI11-057](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-057)

## Abstract

We develop a general framework for social choice problems in which a limited number of alternatives can be recommended to an agent population. In our budgeted social choice model, this limit is determined by a budget, capturing problems that arise naturally in a variety of contexts, and spanning the continuum from pure consensus decision making (i.e., standard social choice) to fully personalized recommendation. Our approach applies a form of segmentation to social choice problems— requiring the selection of diverse options tailored to different agent types—and generalizes certain multi-winner election schemes. We show that standard rank aggregation methods perform poorly, and that optimization in our model is NP-complete; but we develop fast greedy algorithms with some theoretical guarantees. Experiments on real-world datasets demonstrate the effectiveness of our algorithms.