OxTalks will soon be transitioning to Oxford Events (full details are available on the Staff Gateway). A two-week publishing freeze is expected in early Hilary to allow all events to be migrated to the new platform. During this period, you will not be able to submit or edit events on OxTalks. The exact freeze dates will be confirmed as soon as possible.
If you have any questions, please contact halo@digital.ox.ac.uk
An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual’s observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimator that is stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches.
Link to paper: arxiv.org/abs/2004.03865
Please sign up for meetings here: docs.google.com/spreadsheets/d/1GRwPBmtpUwstC4fdLZrnxfnARNYHedHykoRZG4Xq2Bo/edit#gid=0
