Performative Power
Authors: Moritz Hardt, Meena Jagadeesan, Celestine Mendler-Dünner
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | Moritz Hardt Max-Planck Institute for Intelligent Systems, Tübingen hardt@is.mpg.de Meena Jagadeesan UC Berkeley mjagadeesan@berkeley.edu Celestine Mendler-Dünner Max-Planck Institute for Intelligent Systems, Tübingen cmendler@tuebingen.mpg.de |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical contributions and proposes an observational design. It does not conduct its own experiments using specific datasets, and thus does not provide access information for a public or open dataset used in its own analysis. |
| Dataset Splits | No | The paper focuses on theoretical contributions and proposes an observational design. It does not conduct its own experiments, and therefore, no specific dataset split information for training, validation, or testing is provided by the authors for their own work. |
| Hardware Specification | No | The paper focuses on theoretical contributions and proposes an observational design. It does not conduct its own experiments, and therefore, no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper focuses on theoretical contributions and proposes an observational design. It does not conduct its own experiments, and therefore, no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper focuses on theoretical contributions and proposes an observational design. It does not conduct its own experiments, and therefore, no specific experimental setup details like hyperparameters or training configurations are provided. |