An engine not a camera: Measuring performative power of online search
Celestine Mendler-Dünner, Gabriele Carovano, Moritz Hardt
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 7f7b887d-0dc8-4b1c-acd6-920b7ed44b13Cited by top-tier papers2
- What's in a Query: Polarity-Aware Distribution-Based Fair RankingAparna Balagopalan, Kai Wang, Olawale Salaudeen, Asia Biega et al.WWW 2025 · 1 citation
- The Lock-in Hypothesis: Stagnation by AlgorithmTianyi Qiu, Zhonghao He, Tejasveer Chugh, Max Kleiman-WeinerICML 2025
Builds on3
- Performative PredictionJuan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz HardtICML 2020 · 422 citations
- Problematic Machine Behavior: A Systematic Literature Review of Algorithm AuditsJack BandyCSCW 2021 · 190 citations
- Scalar is Not Enough: Vectorization-based Unbiased Learning to RankMouxiang Chen, Chenghao Liu, Zemin Liu, Jianling SunKDD 2022 · 3 citations
Related papers
- Performative PowerMoritz Hardt, Meena Jagadeesan, Celestine Mendler-DünnerNeurIPS 2022 · 5 citations
- Understanding the Performance Costs and Benefits of Privacy-focused Browser ExtensionsKevin Borgolte, Nick FeamsterWWW 2020 · 21 citations
- Query Reformulation in E-Commerce SearchSharon Hirsch, Ido Guy, Alexander Nus, Arnon Dagan et al.SIGIR 2020 · 29 citations
- This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated TopicsTim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon et al.SIGIR 2021 · 36 citations
- Dissecting users' needs for search result explanationsPrerna Juneja, Wenjuan Zhang, Alison Marie Smith-Renner, Hemank Lamba et al.CHI 2024 · 4 citations
