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Uncertainty and Exploration in a Restless Bandit Problem
Author(s) -
Speekenbrink Maarten,
Konstantinidis Emmanouil
Publication year - 2015
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/tops.12145
Subject(s) - task (project management) , action (physics) , computer science , balance (ability) , order (exchange) , artificial intelligence , multi armed bandit , cognitive psychology , machine learning , psychology , economics , regret , physics , management , finance , quantum mechanics , neuroscience
Decision making in noisy and changing environments requires a fine balance between exploiting knowledge about good courses of action and exploring the environment in order to improve upon this knowledge. We present an experiment on a restless bandit task in which participants made repeated choices between options for which the average rewards changed over time. Comparing a number of computational models of participants’ behavior in this task, we find evidence that a substantial number of them balanced exploration and exploitation by considering the probability that an option offers the maximum reward out of all the available options.