Working Paper: NBER ID: w28535
Authors: John Asker; Chaim Fershtman; Ariel Pakes
Abstract: The behavior of artificial intelligences algorithms (AIAs) is shaped by how they learn about their environment. We compare the prices generated by AIAs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AIA only learns about the return from the action it took. Synchronous learning occurs when the AIA conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AIA, synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AIA places a weight on future profits.
Keywords: Artificial Intelligence; Pricing Algorithms; Market Competition; Reinforcement Learning
JEL Codes: C72; C73; D43; D82; K21; L1; L13; L4; L51; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| synchronous learning (C92) | competitive pricing (L11) |
| asynchronous learning (C91) | monopoly pricing (D42) |
| asynchronous learning (C91) | prices above marginal costs (D40) |
| synchronous learning (C92) | Nash equilibrium prices (D41) |
| asynchronous updating (C69) | supracompetitive pricing (L11) |