In stylized competitive markets with noisy demand and iterated least squares learning, oblivious demand models yield transient collusive patterns that dissipate under sufficient exploration, informed sellers strictly outperform, and the modeling choice has a unique Nash equilibrium at the all-inform
arXiv preprint arXiv:2202.05946 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.GT 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
No-swap-regret players frequently receive lower utilities than no-regret players in two-player games due to slower effective learning rates, though the reverse holds in some random 7-action games.
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
-
Should Demand Models Incorporate Competitor Prices? Oblivious Learning and Algorithmic Collusion
In stylized competitive markets with noisy demand and iterated least squares learning, oblivious demand models yield transient collusive patterns that dissipate under sufficient exploration, informed sellers strictly outperform, and the modeling choice has a unique Nash equilibrium at the all-inform
-
Hierarchies of No-regret Algorithms
No-swap-regret players frequently receive lower utilities than no-regret players in two-player games due to slower effective learning rates, though the reverse holds in some random 7-action games.
-
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.