Introduces coherent swap regret against local CPTP maps and proves a three-level landscape where non-unital measurement-preparation channels force Theta(sqrt(d T log d)) minimax regret while unital channels have zero regret.
Econometrica , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper maps social norms over signal-reward space in Markov games and supplies a general solution plus analysis of the replicator dynamics by which such norms can become dominant.
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Coherent Swap Regret and Channel-Proof Learning
Introduces coherent swap regret against local CPTP maps and proves a three-level landscape where non-unital measurement-preparation channels force Theta(sqrt(d T log d)) minimax regret while unital channels have zero regret.
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The fitness landscape of social norms in social dilemmas
The paper maps social norms over signal-reward space in Markov games and supplies a general solution plus analysis of the replicator dynamics by which such norms can become dominant.