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Managing Algorithm Development among Third Party Contractors
The use of autonomous pricing algorithms has grown across markets in recent years, and many firms outsource their pricing algorithms to third-party developers. While recent evidence highlights the potential for pricing algorithms to influence competition, the design of these algorithms and how the risks of these algorithms can be managed is less clear. We collect pricing algorithms from both programmers, via an RCT on Upwork.com, and from an LLM to characterize how programmers think about pricing algorithms, and the extent to which third-party programmer decisions can be adjusted using simple non-technical prompts. We show that, on average, programmer and LLM-written algorithms are less sophisticated than the Q-learning algorithms used in the theoretical literature. We also show that a prompt aimed to focus programmer attention on economic fundamentals can help human programmers to produce algorithms that better match competitive prices. Finally, we find the biggest threat of supra-competitive prices is generated via mis-specification of demand models.
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