Bayesian Persuasion with Externalities: Exploiting Agent Types
- URL: http://arxiv.org/abs/2412.12859v1
- Date: Tue, 17 Dec 2024 12:41:17 GMT
- Title: Bayesian Persuasion with Externalities: Exploiting Agent Types
- Authors: Jonathan Shaki, Jiarui Gan, Sarit Kraus,
- Abstract summary: We study a Bayesian persuasion problem with externalities.
In this model, a principal sends signals to inform multiple agents about the state of the world.
We study the problem of computing optimal signaling strategies for the principal.
- Score: 21.508431216175143
- License:
- Abstract: We study a Bayesian persuasion problem with externalities. In this model, a principal sends signals to inform multiple agents about the state of the world. Simultaneously, due to the existence of externalities in the agents' utilities, the principal also acts as a correlation device to correlate the agents' actions. We consider the setting where the agents are categorized into a small number of types. Agents of the same type share identical utility functions and are treated equitably in the utility functions of both other agents and the principal. We study the problem of computing optimal signaling strategies for the principal, under three different types of signaling channels: public, private, and semi-private. Our results include revelation-principle-style characterizations of optimal signaling strategies, linear programming formulations, and analysis of in/tractability of the optimization problems. It is demonstrated that when the maximum number of deviating agents is bounded by a constant, our LP-based formulations compute optimal signaling strategies in polynomial time. Otherwise, the problems are NP-hard.
Related papers
- Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD [7.434126318858966]
Distributed learning is essential to train machine learning algorithms across heterogeneous agents.
We conduct an analysis of Unified Distributed SGD (UD-SGD)
We assess how different sampling strategies, such as i.i.d. sampling, shuffling, and Markovian sampling, affect the convergence speed of UD-SGD.
arXiv Detail & Related papers (2024-09-26T03:12:20Z) - Principal-Agent Reward Shaping in MDPs [50.914110302917756]
Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest.
We study a two-player Stack game where the principal and the agent have different reward functions, and the agent chooses an MDP policy for both players.
Our results establish trees and deterministic decision processes with a finite horizon.
arXiv Detail & Related papers (2023-12-30T18:30:44Z) - On the Complexity of Multi-Agent Decision Making: From Learning in Games
to Partial Monitoring [105.13668993076801]
A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees.
We study this question in a general framework for interactive decision making with multiple agents.
We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
arXiv Detail & Related papers (2023-05-01T06:46:22Z) - Compressed Regression over Adaptive Networks [58.79251288443156]
We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem.
We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
arXiv Detail & Related papers (2023-04-07T13:41:08Z) - Distributed Adaptive Learning Under Communication Constraints [54.22472738551687]
This work examines adaptive distributed learning strategies designed to operate under communication constraints.
We consider a network of agents that must solve an online optimization problem from continual observation of streaming data.
arXiv Detail & Related papers (2021-12-03T19:23:48Z) - Multi-agent Policy Optimization with Approximatively Synchronous
Advantage Estimation [55.96893934962757]
In multi-agent system, polices of different agents need to be evaluated jointly.
In current methods, value functions or advantage functions use counter-factual joint actions which are evaluated asynchronously.
In this work, we propose the approximatively synchronous advantage estimation.
arXiv Detail & Related papers (2020-12-07T07:29:19Z) - Heterogeneous Explore-Exploit Strategies on Multi-Star Networks [0.0]
We study a class of distributed bandit problems in which agents communicate over a multi-star network.
We propose new heterogeneous explore-exploit strategies using the multi-star as the model irregular network graph.
arXiv Detail & Related papers (2020-09-02T20:56:49Z) - Multiagent Value Iteration Algorithms in Dynamic Programming and
Reinforcement Learning [0.0]
We consider infinite horizon dynamic programming problems, where the control at each stage consists of several distinct decisions.
In an earlier work we introduced a policy iteration algorithm, where the policy improvement is done one-agent-at-a-time in a given order.
arXiv Detail & Related papers (2020-05-04T16:34:24Z) - Public Bayesian Persuasion: Being Almost Optimal and Almost Persuasive [57.47546090379434]
We study the public persuasion problem in the general setting with: (i) arbitrary state spaces; (ii) arbitrary action spaces; (iii) arbitrary sender's utility functions.
We provide a quasi-polynomial time bi-criteria approximation algorithm for arbitrary public persuasion problems that, in specific settings, yields a QPTAS.
arXiv Detail & Related papers (2020-02-12T18:59:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.