Publication

Robust Multi-agent Counterfactual Prediction

Neural Information Processing Systems (NeurIPS)


Abstract

We consider the problem of using logged data to make predictions about what would happen if we changed the ‘rules of the game’ in a multi-agent system. This task is difficult because in many cases we observe actions individuals take but not their private information or their full reward functions. In addition, agents are strategic, so when the rules change, they will also change their actions. Existing methods (e.g. structural estimation, inverse reinforcement learning) assume that agents’ behavior comes from optimizing some utility or that the system is in equilibrium. They make counterfactual predictions by using observed actions to learn the underlying utility function (a.k.a. type) and then solving for the equilibrium of the counterfactual environment. This approach imposes heavy assumptions such as the rationality of the agents being observed and a correct model of the environment and agents’ utility functions. We propose a method for analyzing the sensitivity of counterfactual conclusions to violations of these assumptions, which we call robust multi-agent counterfactual prediction (RMAC). We provide a first-order method for computing RMAC bounds. We apply RMAC to classic environments in market design: auctions, school choice, and social choice.

Related Publications

All Publications

Weak-Attention Suppression For Transformer Based Speech Recognition

Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer

Interspeech - October 26, 2020

Machine Learning in Compilers: Past, Present, and Future

Hugh Leather, Chris Cummins

FDL - September 14, 2020

Unsupervised Cross-Domain Singing Voice Conversion

Adam Polyak, Lior Wolf, Yossi Adi, Yaniv Taigman

Interspeech - August 8, 2020

TTS Skins: Speaker Conversion via ASR

Adam Polyak, Lior Wolf, Yaniv Taigman

Interspeech - August 9, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy