Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
Adaptation as Information Restriction: The Hot Stove Effect
Organization Science
Simple Models of Discrete Choice and Their Performance in Bandit Experiments
Manufacturing & Service Operations Management
Multi-armed bandit algorithms and empirical evaluation
ECML'05 Proceedings of the 16th European conference on Machine Learning
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A common justification for organizational change is that the circumstances in which the organization finds itself have changed, thereby eroding the value of utilizing existing knowledge. On the surface, the claim that organizations should adapt by generating new knowledge seems obvious and compelling. However, this standard wisdom overlooks the possibility that the reward to generating new knowledge may itself be eroded if change is an ongoing property of the environment. This observation in turn suggests that environmental change is not a self-evident call for strategies of greater exploration. Indeed, under some conditions the appropriate response to environmental change is a renewed focus on exploiting existing knowledge and opportunities. We develop a computational model based on the canonical multiarmed bandit formulation of exploration and exploitation. We endeavor to understand the mechanisms by which environmental change acts to make purposeful efforts at organizational adaptation less (or more) valuable. This paper was accepted by Jesper Sørensen, organizations.