Derives first lower bound on γ_t for mean-based algorithms in unknown-horizon bandit settings, proposes two new algorithms, and shows some are also no-regret.
Online optimization algorithms in repeated price competition: Equilibrium learning and algorithmic collusion.arXiv preprint arXiv:2412.15707, 2024.https://arxiv.org/abs/2412.15707
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Domination-Avoiding agents provably avoid collusion in repeated price-competition markets and avoid playing strategies eliminated by iterated elimination of dominated strategies in any game.
Misspecified estimate-then-optimize pricing converges to supra-competitive prices when initial random explorations occur in similar ranges, reaching monopoly levels under symmetry.
citing papers explorer
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Mean-based algorithms: A lower bound and regret
Derives first lower bound on γ_t for mean-based algorithms in unknown-horizon bandit settings, proposes two new algorithms, and shows some are also no-regret.
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Domination-Avoiding Learning Agents Cannot Collude
Domination-Avoiding agents provably avoid collusion in repeated price-competition markets and avoid playing strategies eliminated by iterated elimination of dominated strategies in any game.
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Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices
Misspecified estimate-then-optimize pricing converges to supra-competitive prices when initial random explorations occur in similar ranges, reaching monopoly levels under symmetry.