Markov Persuasion Processes: Learning to Persuade from Scratch

Khái niệm cốt lõi

The authors introduce Markov persuasion processes (MPPs) to address sequential scenarios where a sender interacts with myopic receivers in an unknown environment. They propose an algorithm that optimizes regret and violation trade-offs in learning without knowledge of receiver rewards.

Tóm tắt

The content discusses Bayesian persuasion, the introduction of MPPs, challenges faced in real-world applications, and the development of the OPPS algorithm for learning under partial feedback. The algorithm aims to balance regret and violation while optimizing information disclosure policies.

Key points include:

• Introduction to Bayesian persuasion and MPPs.
• Challenges in real-world applications due to assumptions about receiver rewards.
• Development of the OPPS algorithm for learning under partial feedback.
• Trade-off between regret and violation in learning algorithms.

The OPPS algorithm is designed to optimize regret and violation trade-offs in learning without knowledge of receiver rewards, addressing challenges faced in real-world applications.

Thống kê

Regret grows sublinearly with episodes.
Violation matches lower bound guarantees.
Exploration phase crucial for building approximation of persuasiveness constraints.

Trích dẫn

"In Bayesian persuasion, an informed sender strategically discloses information to influence the behavior of an interested receiver."
"Markov persuasion processes model scenarios where a sender sequentially faces a stream of myopic receivers in an unknown environment."
"The OPPS algorithm balances exploration and exploitation phases to achieve optimal trade-offs between regret and violation."

Thông tin chi tiết chính được chắt lọc từ

Markov Persuasion Processes

by Francesco Ba... lúc arxiv.org 03-07-2024

https://arxiv.org/pdf/2402.03077.pdf

Yêu cầu sâu hơn

How can the OPPS algorithm be adapted for different types of environments

The OPPS algorithm can be adapted for different types of environments by adjusting the exploration-exploitation trade-off based on the specific characteristics of the environment. In cases where there is limited feedback available, such as in partial feedback scenarios, the algorithm can prioritize exploration in the initial phases to gather information about persuasiveness constraints. This allows for a more informed decision-making process during exploitation phases, leading to better performance in terms of regret and violation. Additionally, the algorithm's parameters, such as α which controls the length of exploration versus exploitation phases, can be fine-tuned based on the nature of the environment.

What are the implications of assuming perfect knowledge of receiver rewards in real-world applications

Assuming perfect knowledge of receiver rewards in real-world applications has significant implications. Firstly, it may not align with reality as receivers' preferences and behaviors are often complex and dynamic, making them difficult to predict accurately. This assumption could lead to suboptimal outcomes as it oversimplifies human decision-making processes. Furthermore, perfect knowledge may raise ethical concerns regarding privacy and data protection if sensitive information about individuals is used without their consent or awareness. In practice, it is more realistic and ethical to work with incomplete or uncertain information when designing persuasion strategies.

How does the concept of Bayesian persuasion extend beyond traditional communication strategies

The concept of Bayesian persuasion extends beyond traditional communication strategies by incorporating principles from game theory and decision theory into strategic interactions between an informed sender and a receiver. Unlike one-way communication approaches that focus solely on conveying messages or influencing behavior through advertising or marketing tactics, Bayesian persuasion involves a strategic disclosure of information tailored to influence decisions effectively while considering uncertainties about outcomes.
By leveraging probabilistic models like those found in Bayesian inference frameworks, senders can optimize their signaling schemes based on prior beliefs and observed outcomes to maximize desired actions from receivers efficiently. This approach goes beyond simple persuasive techniques by integrating sophisticated mathematical tools that account for uncertainty and strategic reasoning in decision-making processes across various domains like economics (e.g., auctions), online advertising platforms (e.g., recommendation systems), voting mechanisms (e.g., political campaigns), among others.