We examine the microeconomics of using algorithms to nudge decision-makers toward particular social outcomes. We refer to this as "algorithmic social engineering." In this article, we apply classic strategic communication models to this strategy. Manipulating predictions to express policy preferences strips the predictions of informational content and can lead decision-makers to ignore them. When social problems stem from decision-makers' objectives (rather than their information sets), algorithmic social engineering exhibits clear limitations. Our framework emphasizes separating preferences and predictions in designing algorithmic interventions. This distinction has implications for software architecture, organizational structure, and regulation.

Bo Cowgill & Megan T. Stevenson, Algorithmic Social Engineering, 110 AEA Papers & Proceedings 96–100 (2020).