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.
more »
« less
On the Benefits of Being Constrained When Receiving Signals
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.
more »
« less
- NSF-PAR ID:
- 10332951
- Editor(s):
- Feldman, M.
- Date Published:
- Journal Name:
- Web and Internet Economics. WINE 2021. Lecture Notes in Computer Science()
- Volume:
- 13112
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Private Set Union (PSU) allows two players, the sender and the receiver, to compute the union of their input datasets with- out revealing any more information than the result. While it has found numerous applications in practice, not much research has been carried out so far, especially for large datasets. In this work, we take shuffling technique as a key to design PSU protocols for the first time. By shuffling receiver’s set, we put forward the first protocol, denoted as $\Pi^R_{PSU}$, that eliminates the expensive operations in previous works, such as additive homomorphic encryption and repeated operations on the receiver’s set. It outperforms the state-of-the-art design by Kolesnikov et al. (ASIACRYPT 2019) in both efficiency and security; the unnecessary leakage in Kolesnikov et al.’s design, can be avoided in our design. We further extend our investigation to the application scenarios in which both players may hold unbalanced input datasets. We propose our second protocol $\Pi^S_{PSU}$, by shuffling the sender’s dataset. This design can be viewed as a dual version of our first protocol, and it is suitable in the cases where the sender’s input size is much smaller than the receiver’s. Finally, we implement our protocols $\Pi^R_{PSU}$ and $\Pi^S_{PSU}$ in C++ on big datasets, and perform a comprehensive evaluation in terms of both scalability and parallelizability. The results demonstrate that our design can obtain a 4-5X improvement over the state-of-the-art by Kolesnikov et al. with a single thread in WAN/LAN settings.more » « less
-
Private Set Union (PSU) allows two players, the sender and the receiver, to compute the union of their input datasets with- out revealing any more information than the result. While it has found numerous applications in practice, not much re- search has been carried out so far, especially for large datasets. In this work, we take shuffling technique as a key to de- sign PSU protocols for the first time. By shuffling receiver’s set, we put forward the first protocol, denoted as ΠRPSU, that eliminates the expensive operations in previous works, such as additive homomorphic encryption and repeated operations on the receiver’s set. It outperforms the state-of-the-art design by Kolesnikov et al. (ASIACRYPT 2019) in both efficiency and security; the unnecessary leakage in Kolesnikov et al.’s design, can be avoided in our design. We further extend our investigation to the application sce- narios in which both players may hold unbalanced input datasets. We propose our second protocol ΠSPSU, by shuffling the sender’s dataset. This design can be viewed as a dual ver- sion of our first protocol, and it is suitable in the cases where the sender’s input size is much smaller than the receiver’s. Finally, we implement our protocols ΠRPSU and ΠSPSU in C++ on big datasets, and perform a comprehensive evaluation in terms of both scalability and parallelizability. The results demonstrate that our design can obtain a 4-5× improvement over the state-of-the-art by Kolesnikov et al. with a single thread in WAN/LAN settings.more » « less
-
Guruswami, Venkatesan (Ed.)We study a communication game between a sender and receiver. The sender chooses one of her signals about the state of the world (i.e., an anecdote) and communicates it to the receiver who takes an action affecting both players. The sender and receiver both care about the state of the world but are also influenced by personal preferences, so their ideal actions can differ. We characterize perfect Bayesian equilibria. The sender faces a temptation to persuade: she wants to select a biased anecdote to influence the receiver’s action. Anecdotes are still informative to the receiver (who will debias at equilibrium) but the attempt to persuade comes at the cost of precision. This gives rise to informational homophily where the receiver prefers to listen to like-minded senders because they provide higher-precision signals. Communication becomes polarized when the sender is an expert with access to many signals, with the sender choosing extreme outlier anecdotes at equilibrium (unless preferences are perfectly aligned). This polarization dissipates all the gains from communication with an increasingly well-informed sender when the anecdote distribution is heavy-tailed. Experts therefore face a curse of informedness: receivers will prefer to listen to less-informed senders who cannot pick biased signals as easily.more » « less
-
more » « less
Abstract We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-94676-0_10
