arXiv:2606. 05363v1 Announce Type: cross Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand?
arXiv:2610.09985v1 Announce Type: cross Abstract: We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The sel...
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:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.
arXiv:2608. 08268v1 Announce Type: cross Abstract: As firms increasingly deploy machine learning for strategic decision-making, understanding algorithmic interactions has become central to operations research and economics.
The study investigates how large language models (LLMs) perform in a double auction market, a common economic mechanism. By replacing human participants with LLM agents, the authors find that markets with LLMs converge more slowly or not at all, leading to less efficient resource allocations. Analysis of trading decisions reveals significant variation across model families and roles, and a lexical study of Chain-of-Thought traces links trade execution to a shift from strategic thinking to urgency.
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:2610.18354v1 Announce Type: new Abstract: Game-theoretic benchmarks claim that large language model (LLM) strategic decision-making varies across game types, interaction structures, and capabil...
arXiv:2609.28322v1 Announce Type: new Abstract: Benchmarking and routing platforms increasingly act as intermediaries connecting large language model providers with end-users. However, providers on t...
arXiv:2601. 01279v3 Announce Type: replace-cross Abstract: When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with performance-driven updates aggregating seller feedback raise a key question: can standard AI deployment practices inadvertently produce supracompetitive pricing?
The paper introduces Market Signal Injection (MSI), an attack that alters how market data is formatted or described—without changing its numerical values—to influence large language model (LLM) pricing agents. Experiments on nine open‑weight and three proprietary models in simulated duopoly and triopoly markets show that sentiment‑based formatting changes cause significant shifts in firm behavior, profits, and consumer surplus. The study also demonstrates that model susceptibility varies across families, that larger models are not always more robust, and that techniques such as input canonicalization and decision boundary anchoring can partially mitigate these attacks.
arXiv:2106.06060v4 Announce Type: replace-cross Abstract: Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as th...