Problem
Research questions and friction points this paper is trying to address.
learning agents
algorithmic pricing
Bertrand duopoly
Q-learning
tacit collusion
Persistent Partners Raise Prices Among Learning Agents
🤖 AI Summary
This study investigates how platform matching mechanisms influence the pricing learning and collusive behavior of algorithmic agents. Employing a preregistered randomized controlled experiment within a Bertrand duopoly framework, we systematically examine the effects of partner fixation, information visibility, and communication on pricing strategies using tabular Q-learning and large language models (Qwen). Our findings reveal that fixed-partner mechanisms elevate profits by approximately 0.27 times the competitive-monopoly gap. Furthermore, when opponents' prices are concealed, elevated price levels can be sustained even without explicit punishment strategies. While certain large language models replicate this effect, notable heterogeneity is observed across architectures. This work provides novel empirical evidence for understanding and governing algorithmic collusion.
📝 Abstract
When pricing agents meet repeatedly on a platform, the platform decides who faces whom. We ask whether that choice moves the prices the agents learn, and whether a rise comes with learned punishment. In a pre-registered randomised experiment in the Bertrand duopoly of Calvano et al., each agent's price is set by a tabular Q-learning module, not by the small language model attached to it, and we randomise whether each agent keeps its partner, sees its rival's prices and can send messages. Keeping the same partner raises the level of profits, averaged over training, by 0.27 of the gap between competitive and monopoly profit (95% CI 0.20 to 0.35, all twenty paired runs positive), our registered primary result, and the resting price by 0.17 of the Nash-to-monopoly range (post hoc). A plain tabular learner reproduces the effect in all 25 further blocks, and there one permanent partner raises the level more than about three do (+0.23 against +0.05, exploratory). Where rival prices are hidden, the price-setting module cannot see a cut, so cannot punish it, yet the resting price rises as much and the rise lasts to the end of training, while with visible rivals it shrinks with longer training (post hoc). Where the rival is visible, a static best responder accounts for a third to a half of what a forced-deviation probe reads as punishment, on the starts where the rival can see the cut, and net of it the registered test of learned punishment is inconclusive. A test that looks only for punishment would thus miss the rise where the rival is hidden, while a check for profitable deviations flags most of those prices (post hoc). In an exploratory extension, untrained Qwen2.5 7B and 14B models under one prompt show the effect when the rival's price is left out of the prompt and inconsistently when it is shown, the 7B result replicating on fresh blocks, while two other model families show none.
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