https://macropaperwarehouse.com/authors/joshua-e.-blumenstock/Joshua E. BlumenstockHugo -- gohugo.ioen-ushttps://macropaperwarehouse.com/papers/manipulation-robust-prediction/https://macropaperwarehouse.com/papers/manipulation-robust-prediction/<p>This paper addresses the problem of algorithmic manipulation: when consequential decisions are encoded in machine learning algorithms, individuals strategically alter their behavior to achieve desired outcomes, undermining the predictive validity of the algorithm. The authors develop a &ldquo;strategy-robust&rdquo; approach to training decision rules that explicitly models the incentives and costs of manipulation, producing rules that remain stable even when fully transparent. They then deploy and evaluate this approach in a large field experiment in Kenya — the first real-world implementation and evaluation of such a strategy-robust empirical decision rule.</p>