Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning in embedded systems
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Computational organization theory
Management Science - Special issue on frontier research in manufacturing and logistics
Adaptation on rugged landscapes
Management Science
Organizational Learning: Creating, Retaining, and Transferring Knowledge
Organizational Learning: Creating, Retaining, and Transferring Knowledge
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Dynamic Programming
Imitation of Complex Strategies
Management Science
From T-Mazes to Labyrinths: Learning from Model-Based Feedback
Management Science
Human Problem Solving
Simple Models of Discrete Choice and Their Performance in Bandit Experiments
Manufacturing & Service Operations Management
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The classic trade-off between exploration and exploitation reflects the tension between gaining new information about alternatives to improve future returns and using the information currently available to improve present returns. By considering these issues in the context of a multistage, as opposed to a repeated, problem environment, we show that exploratory behavior has value quite apart from its role in revising beliefs. We show that even if current beliefs provide an unbiased characterization of the problem environment, maximizing with respect to these beliefs may lead to an inferior expected payoff relative to other mechanisms that make less aggressive use of the organization's beliefs. Search can lead to more robust actions in multistage decision problems than maximization, a benefit quite apart from its role in the updating of beliefs.