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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Veröffentlicht in:	Dynamic games and applications Jg. 13; H. 1; S. 187 - 207
Hauptverfasser:	Brown, Philip N., Seaton, Joshua H., Marden, Jason R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2023 Springer Nature B.V
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Author	Seaton, Joshua H. Marden, Jason R. Brown, Philip N.
Author_xml	– sequence: 1 givenname: Philip N. orcidid: 0000-0003-3953-0503 surname: Brown fullname: Brown, Philip N. email: pbrown2@uccs.edu organization: Department of Computer Science, University of Colorado Colorado Springs – sequence: 2 givenname: Joshua H. surname: Seaton fullname: Seaton, Joshua H. organization: Department of Computer Science, University of Colorado Colorado Springs – sequence: 3 givenname: Jason R. surname: Marden fullname: Marden, Jason R. organization: Department of Electrical and Computer Engineering, University of California Santa Barbara
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Cites_doi	10.1021/ma00200a025 10.1016/j.geb.2013.07.001 10.1016/j.geb.2012.02.017 10.1109/TAC.2021.3069809 10.1109/LCSYS.2020.3001240 10.1007/s00182-019-00693-1 10.1109/TCNS.2017.2740625 10.1002/ett.3128 10.1109/TAC.2019.2961995 10.1007/sll229-006-9034-z 10.1109/JSTSP.2013.2246511 10.1109/ACCESS.2020.2978853 10.1016/j.geb.2009.08.004 10.1006/game.1995.1046 10.1109/TPAMI.2012.79 10.23919/ECC.2019.8795902 10.1007/11847250_17 10.1109/CDC.2017.8263844 10.1006/game.1993.1023 10.1109/LCSYS.2022.3166748 10.1109/CDC42340.2020.9304054 10.1145/956755.956769 10.1109/CDC45484.2021.9683342 10.1145/380752.380883 10.1002/art.33407 10.1007/978-3-319-66700-3_17 10.1109/TAC.2021.3088412 10.1109/SFCS.2002.1181966
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Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
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SubjectTerms	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
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Title	Robust Networked Multiagent Optimization: Designing Agents to Repair Their Own Utility Functions
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