Satisficing in Time-Sensitive Bandit Learning

Daniel Russo () and Benjamin Van Roy ()
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Daniel Russo: Columbia Business School, Columbia University, New York, New York 10027
Benjamin Van Roy: Department of Electrical Engineering and Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Mathematics of Operations Research, 2022, vol. 47, issue 4, 2815-2839

Abstract: Much of the recent literature on bandit learning focuses on algorithms that aim to converge on an optimal action. One shortcoming is that this orientation does not account for time sensitivity, which can play a crucial role when learning an optimal action requires much more information than near-optimal ones. Indeed, popular approaches, such as upper-confidence-bound methods and Thompson sampling, can fare poorly in such situations. We consider instead learning a satisficing action , which is near-optimal while requiring less information, and propose satisficing Thompson sampling , an algorithm that serves this purpose. We establish a general bound on expected discounted regret and study the application of satisficing Thompson sampling to linear and infinite-armed bandits, demonstrating arbitrarily large benefits over Thompson sampling. We also discuss the relation between the notion of satisficing and the theory of rate distortion, which offers guidance on the selection of satisficing actions.

Keywords: Primary: 68T05; secondary: 62C10; online optimization; bandit learning; Thompson sampling; satisficing; information theory; rate-distortion theory (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:47:y:2022:i:4:p:2815-2839

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