Exploration-Free Policies in Dynamic Pricing and Online Decision-Making

Growing availability of data has enabled practitioners to tailor decisions at the individuallevel. This involves learning a model of decision outcomes conditional on individual-specific covariates or features. Recently, contextual bandits have been introduced as a framework to study these online and sequential decision making problems. This literature predominantly focuses on algorithms that balance an exploration-exploitation tradeoff, since greedy policies that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy policies are desirable in many practical settings where experimentation may be prohibitively costly or unethical.