The paper establishes equilibrium existence and uniqueness for nonlinear utility consumer networks under contraction conditions and proposes a shape-constrained isotonic regression approach with strict no-regret convergence for learning utilities in targeted monopoly pricing.
cc/paper_files/paper/2019/file/ 263fc48aae39f219b4c71d9d4bb4aed2-Paper
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
DARLING augments RL with change detection to match minimax lower bounds on dynamic regret for piecewise stationary tabular and linear MDPs under separability and reachability conditions.
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Equilibrium and Pricing in Consumer Networks with Nonlinear Utilities: An Online Shape-Constrained Learning Approach
The paper establishes equilibrium existence and uniqueness for nonlinear utility consumer networks under contraction conditions and proposes a shape-constrained isotonic regression approach with strict no-regret convergence for learning utilities in targeted monopoly pricing.
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DARLING: Detection Augmented Reinforcement Learning with Non-Stationary Guarantees
DARLING augments RL with change detection to match minimax lower bounds on dynamic regret for piecewise stationary tabular and linear MDPs under separability and reachability conditions.