---
title: "Automated Mechanism Design without Money via Machine Learning"
authors:
  - "Harikrishna Narasimhan"
  - "Shivani Agarwal"
  - "David C. Parkes"
doi: ""
url: "https://ijcai.org/proceedings/2016/68"
pdf: "https://ijcai.org/proceedings/2016/0068.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "433-438"
track: ""
keywords:
---

# Automated Mechanism Design without Money via Machine Learning

Harikrishna Narasimhan, Shivani Agarwal, David C. Parkes

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 433-438.

## Abstract

We use statistical machine learning to develop methods for automatically designing mechanisms in domains without money. Our goal is to find a mechanism that best approximates a given target function subject to a design constraint such as strategy-proofness or stability. The proposed approach involves identifying a rich parametrized class of mechanisms that resemble discriminant-based multiclass classifiers, and relaxing the resulting search problem into an SVM-style surrogate optimization problem. We use this methodology to design strategy-proof mechanisms for social choice problems with single-peaked preferences, and stable mechanisms for two-sided matching problems. To the best of our knowledge, ours is the first automated approach for designing stable matching rules. Experiments on synthetic and real-world data confirm the usefulness of our methods.