Motivated by the proliferation of online platforms that collect and disseminate consumers' experiences with alternative substitutable products/services, we investigate the problem of optimal information provision when the goal is to maximize aggregate consumer surplus. We develop a decentralized multi-armed bandit framework where a forward-looking principal (the platform designer) commits upfront to a policy that dynamically discloses information regarding the history of outcomes to a series of short-lived rational agents (the consumers). We demonstrate that consumer surplus is non-monotone in the accuracy of the designer's information-provision policy. Because consumers are constantly in "exploitation" mode, policies that disclose accurate information on past outcomes suffer from inadequate "exploration." We illustrate how the designer can (partially) alleviate this inefficiency by employing a policy that strategically obfuscates the information in the platform's possession -interestingly, such a policy is beneficial despite the fact that consumers are aware of both the designer's objective and the precise way by which information is being disclosed to them.More generally, we show that the optimal information-provision policy can be obtained as the solution of a large-scale linear program. Noting that such a solution is typically intractable, we use our structural findings to design an intuitive heuristic that underscores the value of information obfuscation in decentralized learning. We further highlight that obfuscation remains beneficial even if the designer can directly incentivize consumers to explore through monetary payments.At any time, the history of service outcomes (i.e., the system state x t ) is not directly observable to the consumers. Instead, there is a platform designer who commits upfront to a "messaging policy" that acts as an instrument of information-provision to the consumers. 7 This policy specifies the message that is displayed on the platform, given any underlying system state; in §7.2, we extend 4 The general analysis in §6 can be readily extended to the case of |S| > 2 providers. 5 The probability density function of a Beta(s, f ) random variable is given by g(x; s, f ) = x s−1 (1−x) f −1 B(s,f ), for x ∈ [0, 1]. 6 The platform and the consumers hold the same prior belief, so that platform actions (e.g., choice of informationprovision policy) do not convey any additional information on provider quality to the consumers (e.g., Bergemann and Välimäki 1997, Bose et al. 2006, Papanastasiou and Savva 2016.7 Commitment is a reasonable assumption in the context of online platforms, where information provision occurs on the basis of pre-decided algorithms and the large volume of products/services hosted renders ad-hoc adjustments of the automatically-generated content prohibitively costly (see also §5.4, where this assumption is relaxed).