Rule-Based Pricing Algorithms and Market Outcomes: An Experimental Study

Adrian Hillenbrand, Hans-Theo Normann, Matthias Potarca and Tobias Werner

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Abstract: Rule-based pricing tools are widespread in digital commerce, yet we know little about how their design shapes market outcomes. In a controlled market experiment, participants use dashboards to build pricing algorithms competing in a sequential Bertrand game over multiple periods. We vary design features commonly found in commercial repricing tools: warnings about price wars, pre-configured strategies, and advice from a large language model. Most treatment variations raise market prices with effects driven by an increase in starting prices and more cooperative algorithm designs. The results matter for competition policy, platform regulation and current discussions on regulating algorithm design tools.

Date: 2026-09
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