Daniel Björkegren | Macro Paper Warehouse

Manipulation-Robust Prediction

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 “strategy-robust” 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.

What Do Policies Value?

This paper asks a fundamental question about policy design: when a program prioritizes one group over another, is that because the group benefits more from the intervention, or because the policy assigns them higher intrinsic welfare weight? Björkegren, Blumenstock, and Knight develop a two-stage method to decompose observed allocation decisions into their underlying components: (i) welfare weights assigned to different types of people, (ii) heterogeneous treatment effects of the intervention, and (iii) relative weights on different outcomes. The key insight is that the same allocation rule can be consistent with very different value systems depending on how much each group actually benefits.