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How Can Platforms Learn to Make Persuasive Recommendations? Many online platforms make recommendations to users on content from creators or products from sellers. The motivation underlying such recommendations is to persuade users into taking actions that also serve system-wide goals. To do this effectively, a platform needs to know the underlying distribution of payoff-relevant variables (such as content or product quality). However, this distributional information is often lacking—for example, when new content creators or sellers join a platform. In “Learning to Persuade on the Fly: Robustness Against Ignorance,” Zu, Iyer, and Xu study how a platform can make persuasive recommendations over time in the absence of distributional knowledge using a learning-based approach. They first propose and motivate a robust-persuasiveness criterion for settings with incomplete information. They then design an efficient recommendation algorithm that satisfies this criterion and achieves low regret compared with the benchmark of complete distributional knowledge. Overall, by relaxing the strong assumption of complete distributional knowledge, this research extends the applicability of information design to more practical settings. 

We consider a dynamic Bayesian persuasion setting where a single long-lived sender persuades a stream of ``short-lived'' agents (receivers) by sharing information about a payoff-relevant state. The state transitions are Markovian and the sender seeks to maximize the long-run average reward by committing to a (possibly history-dependent) signaling mechanism. While most previous studies of Markov persuasion consider exogenous agent beliefs that are independent of the chain, we study a more natural variant with endogenous agent beliefs that depend on the chain's realized history. A key challenge to analyze such settings is to model the agents' partial knowledge about the history information. We analyze a Markov persuasion process (MPP) under various information models that differ in the amount of information the receivers have about the history of the process. Specifically, we formulate a general partial-information model where each receiver observes the history with an l period lag. Our technical contribution start with analyzing two benchmark models, i.e., the full-history information model and the no-history information model. We establish an ordering of the sender's payoff as a function of the informativeness of agent's information model (with no-history as the least informative), and develop efficient algorithms to compute optimal solutions for these two benchmarks. For general l, we present the technical challenges in finding an optimal signaling mechanism, where even determining the right dependency on the history becomes difficult. To bypass the difficulties, we use a robustness framework to design a "simple" \emph{history-independent} signaling mechanism that approximately achieves optimal payoff when l is reasonably large.