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1. On the Benefits of Being Constrained When Receiving Signals

   https://doi.org/10.1007/978-3-030-94676-0_10  

   Su, ST.; Kempe, D.; Subramanian, V.G. (January 2022, Web and Internet Economics. WINE 2021. Lecture Notes in Computer Science())

   Feldman, M. (Ed.)

   We study a Bayesian persuasion setting in which the receiver is trying to match the (binary) state of the world. The sender’s utility is partially aligned with the receiver’s, in that conditioned on the receiver’s action, the sender derives higher utility when the state of the world matches the action. Our focus is on whether in such a setting, being constrained helps a receiver. Intuitively, if the receiver can only take the sender’s preferred action with smaller probability, the sender might have to reveal more information, so that the receiver can take the action more specifically when the sender prefers it. We show that with a binary state of the world, this intuition indeed carries through: under very mild non-degeneracy conditions, a more constrained receiver will always obtain (weakly) higher utility than a less constrained one. Unfortunately, without additional assumptions, the result does not hold when there are more than two states in the world, which we show with an explicit example. 

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2. Algorithmic Cheap Talk

   Babichenko, Yakov; Talgam-Cohen, Inbal; Xu, Haifeng; Zabarnyi, Konstantin (July 2024, Proceedings of 25th ACM Conference on Economics and Computation, EC 2024)

   The literature on strategic communication originated with the influential cheap talk model, which precedes the Bayesian persuasion model by three decades. This model describes an interaction between two agents: sender and receiver. The sender knows some state of the world which the receiver does not know, and tries to influence the receiver’s action by communicating a cheap talk message to the receiver. This paper initiates the algorithmic study of cheap talk in a finite environment (i.e., a finite number of states and receiver’s possible actions). We first prove that approximating the sender-optimal or the welfare-maximizing cheap talk equilibrium up to a certain additive constant or multiplicative factor is NP-hard. Fortunately, we identify three naturally-restricted cases that admit efficient algorithms for finding a sender-optimal equilibrium. These include a state-independent sender’s utility structure, a constant number of states or a receiver having only two actions. 

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3. Persuading Risk-Conscious Agents: A Geometric Approach

   https://doi.org/10.1007/978-3-030-35389-6  

   Anunrojwong, Jerry; Iyer, Krishnamurthy; Lingenbrink, David. (December 2019, Lecture notes in computer science)

   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. 

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4. Leakage-Robust Bayesian Persuasion

   Haghtalab, Nika; Qiao, Mingda; Yang, Kunhe (July 2025, Twenty-Sixth ACM Conference on Economics and Computation (EC 2025))

   This paper introduces the concept of leakage-robust Bayesian persuasion. Situated between public Bayesian persuasion and private Bayesian persuasion, leakage-robust persuasion considers a setting where one or more signals privately communicated by a sender to the receivers may be leaked. We study the design of leakage-robust Bayesian persuasion schemes and quantify the price of robustness using two formalisms: - The first notion, k-worst-case persuasiveness, requires a signaling scheme to remain persuasive as long as each receiver observes no more than k leaked signals from other receivers. We quantify the Price of Robust Persuasiveness (PoRPk)— i.e., the gap in sender's utility as compared to the optimal private persuasion scheme—as Θ(min{2k,n}) for supermodular sender utilities and Θ(k) for submodular or XOS sender utilities, where n is the number of receivers. This result also establishes that in some instances, Θ(log k) leakages are sufficient for the utility of the optimal leakage-robust persuasion to degenerate to that of public persuasion. - The second notion, expected downstream utility robustness, relaxes the persuasiveness requirement and instead considers the impact on sender's utility resulting from receivers best responding to their observations. By quantifying the Price of Robust Downstream Utility (PoRU) as the gap between the sender's expected utility over the randomness in the leakage pattern as compared to private persuasion, our results show that, over several natural and structured distributions of leakage patterns, PoRU improves PoRP to Θ(k) or even Θ(1), where k is the maximum number of leaked signals observable to each receiver across leakage patterns in the distribution. En route to these results, we show that subsampling and masking serve as general-purpose algorithmic paradigms for transforming any private persuasion signaling scheme to one that is leakage-robust, with minmax optimal loss in sender's utility. A full version of this paper can be found at https://arxiv.org/abs/2411.16624. 

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5. Markov Persuasion Processes with Endogenous Agent Beliefs

   Iyer, Krishnamurthy; Xu, Haifeng Xu; Zu, You (December 2023, The 19th Conference On Web And InterNet Economics (WINE 2023))

   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. 

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