Social Foundations of Computation

Performative Power

2022

Conference Paper

sf


We introduce the notion of performative power, which measures the ability of a firm operating an algorithmic system, such as a digital content recommendation platform, to cause change in a population of participants. We relate performative power to the economic study of competition in digital economies. Traditional economic concepts struggle with identifying anti-competitive patterns in digital platforms not least due to the complexity of market definition. In contrast, performative power is a causal notion that is identifiable with minimal knowledge of the market, its internals, participants, products, or prices.We study the role of performative power in prediction and show that low performative power implies that a firm can do no better than to optimize their objective on current data. In contrast, firms of high performative power stand to benefit from steering the population towards more profitable behavior. We confirm in a simple theoretical model that monopolies maximize performative power. A firm's ability to personalize increases performative power, while competition and outside options decrease performative power. On the empirical side, we propose an observational causal design to identify performative power from discontinuities in how digital platforms display content. This allows to repurpose causal effects from various studies about digital platforms as lower bounds on performative power. Finally, we speculate about the role that performative power might play in competition policy and antitrust enforcement in digital marketplaces.

Author(s): Hardt, M., and Jagadeesan, M., and Mendler-Dünner, C.
Book Title: Advances in Neural Information Processing Systems 35 (NeurIPS)
Year: 2022
Publisher: Curran Associates Inc.

Department(s): Social Foundations of Computation
Bibtex Type: Conference Paper (inproceedings)

State: Published
URL: https://proceedings.neurips.cc/paper_files/paper/2022/file/90e73f3cf1a6c84c723a2e8b7fb2b2c1-Paper-Conference.pdf

BibTex

@inproceedings{hardt2022performativepower,
  title = {Performative Power},
  author = {Hardt, M. and Jagadeesan, M. and Mendler-D{\"u}nner, C.},
  booktitle = {Advances in Neural Information Processing Systems 35 (NeurIPS)},
  publisher = {Curran Associates Inc.},
  year = {2022},
  doi = {},
  url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/90e73f3cf1a6c84c723a2e8b7fb2b2c1-Paper-Conference.pdf}
}