Learning and Decision-Making with Strategic Feedback (StratML)

Classical treatments of machine learning rely on the assumption that the data, after deployment, resembles the data the model was trained on. However, as machine learning models are increasingly used to make consequential decisions about people, individuals often react strategically to the deployed model. These strategic behaviors---which effectively invalidate the predictive models---have opened up new avenues of research and added new challenges to the deployment of machine learning algorithms in the real world.

Different aspects of strategic behavior have been studied by several communities both within and outside of machine learning. For example, the growing literature on strategic classification studies algorithms for finding strategy-robust decision rules, as well as the properties of such rules. Behavioral economics aims to understand and model people’s strategic responses. Recent works on learning in games study optimization algorithms for finding meaningful equilibria and solution concepts in competitive environments.

This workshop aims to create a dialogue between these different communities, all studying aspects of decision-making and learning with strategic feedback. The goal is to identify common points of interest and open problems in the different subareas, as well as to encourage cross-disciplinary collaboration.