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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. 

Nonmonetary mechanisms for repeated allocation and decision making are gaining widespread use in many real-world settings. Our aim in this work is to study the performance and incentive properties of simple mechanisms based on artificial currencies in such settings. To this end, we make the following contributions: For a general allocation setting, we provide two black-box approaches to convert any one-shot monetary mechanism to a dynamic nonmonetary mechanism using an artificial currency that simultaneously guarantees vanishing gains from nontruthful reporting over time and vanishing losses in performance. The two mechanisms trade off between their applicability and their computational and informational requirements. Furthermore, for settings with two agents, we show that a particular artificial currency mechanism also results in a vanishing price of anarchy. 

We consider an ad network’s problem of allocating the auction for each individual impression to an optimal subset of advertisers with the goal of revenue maximization. This is a variant of bipartite matching except that advertisers may strategize by choosing their bidding profiles and their total budget. Because the ad network’s allocation rule affects the bidders’ strategies, equilibrium analysis is challenging. We show that this analysis is tractable when advertisers face a linear budget cost r_j. In particular, we show that the strategy in which advertisers bid their valuations shaded by a factor of 1 + r_j is an approximate equilibrium with the error decreasing with market size. This equilibrium can be interpreted as one in which a bidder facing an opportunity cost rj is guaranteed a return on investment of at least rj per dollar spent. Furthermore, in this equilibrium, the optimal allocation for the ad network, as determined from a linear program (LP), is greedy with high probability. This is in contrast with the exogenous budgets case, in which the LP optimization is challenging at practical scales. These results are evidence that, although in general such bipartite matching problems may be challenging to solve because of their high dimensionality, the optimal solution is remarkably simple at equilibrium. 

Motivated by practical concerns in applying information design to markets and service systems, we consider a persuasion problem between a sender and a receiver where the receiver may not be an expected utility maximizer. In particular, the receiver’s utility may be non-linear in her belief; we deem such receivers as risk-conscious. Such utility models arise, for example, when the receiver exhibits sensitivity to the variability and the risk in the payoff on choosing an action (e.g., waiting time for a service). In the presence of such non-linearity, the standard approach of using revelation-principle style arguments fails to characterize the set of signals needed in the optimal signaling scheme. Our main contribution is to provide a theoretical framework, using results from convex analysis, to overcome this technical challenge. In particular, in general persuasion settings with risk-conscious agents, we prove that the sender’s problem can be reduced to a convex optimization program. Furthermore, using this characterization, we obtain a bound on the number of signals needed in the optimal signaling scheme. We apply our methods to study a specific setting, namely binary per-suasion, where the receiver has two possible actions (0 and 1), and the sender always prefers the receiver taking action 1. Under a mild convexity assumption on the receiver’s utility and using a geometric approach,we show that the convex program can be further reduced to a linear program. Furthermore, this linear program yields a canonical construction of the set of signals needed in an optimal signaling mechanism. In particular, this canonical set of signals only involves signals that fully reveal the state and signals that induce uncertainty between two states.We illustrate our results in the setting of signaling wait time information in an unobservable queue with customers whose utilities depend on the variance of their waiting times. 

We consider information design in spatial resource competition, motivated by ride sharing platforms sharing information with drivers about rider demand. Each of N co-located agents (drivers) decides whether to move to another location with an uncertain and possibly higher resource level (rider demand), where the utility for moving increases in the resource level and decreases in the number of other agents that move. A principal who can observe the resource level wishes to share this information in a way that ensures a welfare-maximizing number of agents move. Analyzing the principal’s information design problem using the Bayesian persuasion framework, we study both private signaling mechanisms, where the principal sends personalized signals to each agent, and public signaling mechanisms, where the principal sends the same information to all agents. We show: 1) For private signaling, computing the optimal mechanism using the standard approach leads to a linear program with 2 N variables, rendering the computation challenging. We instead describe a computationally efficient two-step approach to finding the optimal private signaling mechanism. First, we perform a change of variables to solve a linear program with O(N^2) variables that provides the marginal probabilities of recommending each agent move. Second, we describe an efficient sampling procedure over sets of agents consistent with these optimal marginal probabilities; the optimal private mechanism then asks the sampled set of agents to move and the rest to stay. 2) For public signaling, we first show the welfare-maximizing equilibrium given any common belief has a threshold structure. Using this, we show that the optimal public mechanism with respect to the sender-preferred equilibrium can be computed in polynomial time. 3) We support our analytical results with numerical computations that show the optimal private and public signaling mechanisms achieve substantially higher social welfare when compared with no-information and full-information benchmarks.