arXiv Machine Learning
Price of Anarchy of First-Price Auctions in Agentic Markets
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Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systems
arXiv:2608. 07532v1 Announce Type: new Abstract: Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast.
arXiv Machine Learning
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Should Demand Models Incorporate Competitor Prices? Oblivious Learning and Algorithmic Collusion
arXiv:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?
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Decentralized LLM Workflows via Sequential Auctions
The paper introduces a scalable framework that uses decentralized markets of self‑interested agents to solve multi‑step tasks. It shows that when agents compete through sequential auctions, perfect competition fails because clones drive profits to zero, while no competition also collapses value extraction. By allowing heterogeneous agents to shade bids strategically, the authors demonstrate that imperfect competition can sustain profitable margins while still achieving high task success rates at low costs, evaluating four bidding algorithms across three auction protocols.
arXiv Machine Learning
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Oblivious Learning and Collusive Pricing
The paper investigates whether pricing algorithms on multi‑seller platforms should incorporate competitors’ prices when learning demand. It compares two strategies: informed sellers that use competitor prices in their learning models, and oblivious sellers that ignore them. The study finds that oblivious sellers must explore prices more aggressively to offset missing competitor information; when all sellers are oblivious, prices eventually converge to the competitive outcome, but insufficient exploration can create many pseudo‑equilibria. In mixed markets, informed sellers earn more, and the unique Nash equilibrium is a fully informed market where prices efficiently converge to the competitive outcome, showing that oblivious modeling does not reliably produce collusion.
arXiv Machine Learning
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Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond
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