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.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Bayesian learners can drive out no-regret learners despite logarithmic regret in stochastic markets, but no-regret is more robust; hybrids are proposed to combine strengths.
Misspecified explore-then-exploit pricing with monopoly-style demand estimation leads to supra-competitive prices when firms explore similar price ranges on the same side of the Nash price.
citing papers explorer
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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.
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Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners
Bayesian learners can drive out no-regret learners despite logarithmic regret in stochastic markets, but no-regret is more robust; hybrids are proposed to combine strengths.
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Misspecified Explore-then-Exploit Leads to Supra-Competitive Prices
Misspecified explore-then-exploit pricing with monopoly-style demand estimation leads to supra-competitive prices when firms explore similar price ranges on the same side of the Nash price.