Algorithmic Pricing, Competition, and Consumer Welfare

This policy review examines algorithmic pricing, competition, and consumer welfare. The organizing question is how policy should distinguish efficiency-enhancing adaptation from conduct that weakens rivalry or exploits consumers. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to inferring collusion or welfare improvement from price correlation alone. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in digital and data-intensive markets.

1. Assad, S., Clark, R., Ershov, D., & Xu, L. (2023). Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market. Journal of Political Economy, 132(3), 723-771. https://doi.org/10.1086/726906 DOI
2. Brown, Z. Y., & MacKay, A. (2023). Competition in Pricing Algorithms. American Economic Journal Microeconomics, 15(2), 109-156. https://doi.org/10.1257/mic.20210158 DOI
3. Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial Intelligence, Algorithmic Pricing, and Collusion. American Economic Review, 110(10), 3267-3297. https://doi.org/10.1257/aer.20190623 DOI
4. Do, C. T., Tran, N. H., Huh, E. N., Hong, C. S., Niyato, D., & Han, Z. (2016). Dynamics of service selection and provider pricing game in heterogeneous cloud market. Journal of Network and Computer Applications, 69, 152-165. https://doi.org/10.1016/j.jnca.2016.04.012 DOI
5. Gorodnichenko, Y., & Weber, M. (2015). Are Sticky Prices Costly? Evidence from the Stock Market. American Economic Review, 106(1), 165-199. https://doi.org/10.1257/aer.20131513 DOI
6. Harrington, J. E. (2022). The Effect of Outsourcing Pricing Algorithms on Market Competition. Management Science, 68(9), 6889-6906. https://doi.org/10.1287/mnsc.2021.4241 DOI
7. Kastius, A., & Schlosser, R. (2021). Dynamic pricing under competition using reinforcement learning. Journal of Revenue and Pricing Management, 21(1), 50-63. https://doi.org/10.1057/s41272-021-00285-3 DOI
8. Ma, R. T. B. (2015). Usage-Based Pricing and Competition in Congestible Network Service Markets. IEEE/ACM Transactions on Networking, 24(5), 3084-3097. https://doi.org/10.1109/tnet.2015.2500589 DOI
9. Shah-Mansouri, H., Wong, V. W. S., & Huang, J. (2017). An Incentive Framework for Mobile Data Offloading Market Under Price Competition. IEEE Transactions on Mobile Computing, 16(11), 2983-2999. https://doi.org/10.1109/tmc.2017.2688402 DOI
10. Zha, L., Yin, Y., & Du, Y. (2017). Surge Pricing and Labor Supply in the Ride-Sourcing Market. Transportation research procedia, 23, 2-21. https://doi.org/10.1016/j.trpro.2017.05.002 DOI