Robust Networked Multiagent Optimization: Designing Agents to Repair Their Own Utility Functions

We study settings in which autonomous agents are designed to optimize a given system-level objective. In typical approaches to this problem, each agent is endowed with a decision-making rule that specifies the agent’s choice as a function of relevant information pertaining to the system’s state. The...

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Bibliographic Details
Published in:Dynamic games and applications Vol. 13; no. 1; pp. 187 - 207
Main Authors: Brown, Philip N., Seaton, Joshua H., Marden, Jason R.
Format: Journal Article
Language:English
Published: New York Springer US 01.03.2023
Springer Nature B.V
Subjects:
Communications Engineering
Computer Systems Organization and Communication Networks
Control algorithms
Decision making
Design
Distance learning
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Equilibrium
Game Theory
Management Science
Mathematics
Mathematics and Statistics
Multi-agent Dynamic Decision Making and Learning
Multiagent systems
Networks
Operations Research
Optimization
Redesign
Social and Behav. Sciences
Utility functions
Distributed optimization
Game theory
Utility design
Networked learning
ISSN:2153-0785, 2153-0793
Online Access:Get full text
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Summary:We study settings in which autonomous agents are designed to optimize a given system-level objective. In typical approaches to this problem, each agent is endowed with a decision-making rule that specifies the agent’s choice as a function of relevant information pertaining to the system’s state. The choices of other agents in the system comprise a key component of this information. This paper considers a scenario in which the designed decision-making rules are not implementable in the realized system due to discrepancies between the anticipated and realized information available to the agents. The focus of this paper is to develop methods by which the agents can preserve system-level performance guarantees in these unanticipated scenarios through local and independent redesigns of their own decision-making rules. First, we show a general impossibility result which states that in general settings, there are no local redesign methodologies that can offer any preservation of system-level performance guarantees, even when the affected agents satisfy an inconsequentiality criterion. However, we then show that when system-level objectives are submodular, local redesigns of utility functions do exist which allow nominal performance guarantees to degrade gracefully as information is denied to agents. That is, in these submodular settings, agents can adapt to informational inconsistencies independently without incurring much loss in terms of system-level performance.
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ISSN:2153-0785
2153-0793
DOI:10.1007/s13235-022-00469-5