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Abstract

This dissertation studies how misinformation, bias, and strategic information provision affect learning and decision-making in operations and management contexts. These challenges commonly arise in healthcare, digital platforms, and revenue management, where decisions depend on complex information-sharing environments. To address them, we develop Bayesian control models to design information provision and robust learning strategies, examining the problems from complementary perspectives. The first chapter focuses on dynamic manipulation by a strategic firm that seeks to influence public beliefs through costly dissemination and distortion. We model the problem as a Bayesian dynamic program, where the firm balances immediate persuasion and future uncertainty. Using a variational representation, we derive a closed-form optimal policy that involves threshold-based dissemination and dynamic mean boosts to belief distributions, and characterize when and how manipulation is most effective over time. The second chapter extends this framework by incorporating social learning. Instead of a single aggregated learner, we model the public as a pair of partially-Bayesian social learners who update beliefs based on both private signals and each other’s opinions. We show that, although social learning may amplify manipulation locally, it guarantees asymptotic convergence to the truth. This highlights the long-run benefits of belief diversification in networked environments, even under manipulation. The third chapter shifts to the learner’s perspective, introducing a hierarchical Bayesian network model where a decision-maker optimally acquires signals from multiple biased sources. We derive the optimal acquisition strategy under general earning objectives. We show that biased sources are complementary, and derive closed-form solutions for the optimal acquisition strategy, which diversifies across biased sources to mitigate bias under budget constraints. Simulation studies using healthcare nowcasting and demand forecasting data confirm this diversified learning approach’s advantages in efficiency and reliability.