An engine not a camera: Measuring performative power of online search
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Abstract (arXiv)
The power of digital platforms is at the center of major ongoing policy and regulatory efforts. To advance existing debates, we designed and executed an experiment to measure the performative power of online search providers. Instantiated in our setting, performative power quantifies the ability of a search engine to steer web traffic by rearranging results. To operationalize this definition we developed a browser extension that performs unassuming randomized experiments in the background. These randomized experiments emulate updates to the search algorithm and identify the causal effect of different content arrangements on clicks. Analyzing tens of thousands of clicks, we discuss what our robust quantitative findings say about the power of online search engines, using the Google Shopping antitrust investigation as a case study. More broadly, we envision our work to serve as a blueprint for how the recent definition of performative power can help integrate quantitative insights from online experiments with future investigations into the economic power of digital platforms.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 29 May 2024.
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Citing papers per year: 2024: 1; 2025: 5; 2026: 3.
9 papers.
- 2025The Lock-in Hypothesis: Stagnation by Algorithmcited by 11
- 2025Performative Validity of Recourse Explanationscited by 8
- 2024Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entrycited by 4
- 2025Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmapcited by 2
- 2026With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systemscited by 2
- 2025What's in a Query: Polarity-Aware Distribution-Based Fair Rankingcited by 1
- 2025PAC Learnability in the Presence of Performativitycited by 1
- 2026Retraining Seeks Stable Signalscited by 1
- 2026Stabilizing Performative Feedback Loops with Minimal Model Deploymentscited by 0
- 2026Stabilizing Performative Feedback Loops with Minimal Model Deployments12 Sep 2026
- 2026Retraining Seeks Stable Signals17 Jul 2026
- 2026With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems30 Mar 2026
- 2025PAC Learnability in the Presence of Performativity9 Oct 2025
- 2025Performative Validity of Recourse Explanations18 Jun 2025
- 2025The Lock-in Hypothesis: Stagnation by Algorithm6 Jun 2025
- 2025Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap17 Feb 2025
- 2025What's in a Query: Polarity-Aware Distribution-Based Fair Ranking17 Feb 2025
- 2024Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry5 Sep 2024