Modeling Opponents in Adversarial Risk Analysis

Adversarial risk analysis has been introduced as a framework to deal with risks derived from intentional actions of adversaries. The analysis supports one of the decisionmakers, who must forecast the actions of the other agents. Typically, this forecast must take account of random consequences resul...

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Detalhes bibliográficos
Autores: Ríos Insua, David, Banks, David, Ríos, Jesús
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2016
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/196670
Acesso em linha:http://hdl.handle.net/10261/196670
Access Level:Acceso aberto
Palavra-chave:Decision analysis
Bayesian model averaging
Adversarial risk analysis
Opponent modeling
Simultaneous games
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spelling Modeling Opponents in Adversarial Risk AnalysisRíos Insua, DavidBanks, DavidRíos, JesúsDecision analysisBayesian model averagingAdversarial risk analysisOpponent modelingSimultaneous gamesAdversarial risk analysis has been introduced as a framework to deal with risks derived from intentional actions of adversaries. The analysis supports one of the decisionmakers, who must forecast the actions of the other agents. Typically, this forecast must take account of random consequences resulting from the set of selected actions. The solution requires one to model the behavior of the opponents, which entails strategic thinking. The supported agent may face different kinds of opponents, who may use different rationality paradigms, for example, the opponent may behave randomly, or seek a Nash equilibrium, or perform level-k thinking, or use mirroring, or employ prospect theory, among many other possibilities. We describe the appropriate analysis for these situations, and also show how to model the uncertainty about the rationality paradigm used by the opponent through a Bayesian model averaging approach, enabling a fully decision-theoretic solution. We also show how as we observe an opponent's decision behavior, this approach allows learning about the validity of each of the rationality models used to predict his decision by computing the models' (posterior) probabilities, which can be understood as a measure of their validity. We focus on simultaneous decision making by two agents.The work of DRI was supported by the AXA‐ICMAT Chair in Adversarial Risk Analysis, the MINECO projects MTM2011‐28983‐C3‐1 and MTM2014‐56949‐C3‐1‐R, the FP7 SECONOMICS grant 285223, and the ISCH COST Action IS1304 on Expert Judgement.Peer reviewedPeer ReviewedBlackwell PublishingMinisterio de Economía y Competitividad (España)European CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2019201920162019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/196670reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#MINECO/ICTI2013-2016/MTM2014‐56949‐C3‐1‐Rinfo:eu-repo/grantAgreement/EC/FP7/285223info:eu-repo/grantAgreement/MICINN//MTM2011‐28983‐C3‐1Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1966702026-05-22T06:33:51Z
dc.title.none.fl_str_mv Modeling Opponents in Adversarial Risk Analysis
title Modeling Opponents in Adversarial Risk Analysis
spellingShingle Modeling Opponents in Adversarial Risk Analysis
Ríos Insua, David
Decision analysis
Bayesian model averaging
Adversarial risk analysis
Opponent modeling
Simultaneous games
title_short Modeling Opponents in Adversarial Risk Analysis
title_full Modeling Opponents in Adversarial Risk Analysis
title_fullStr Modeling Opponents in Adversarial Risk Analysis
title_full_unstemmed Modeling Opponents in Adversarial Risk Analysis
title_sort Modeling Opponents in Adversarial Risk Analysis
dc.creator.none.fl_str_mv Ríos Insua, David
Banks, David
Ríos, Jesús
author Ríos Insua, David
author_facet Ríos Insua, David
Banks, David
Ríos, Jesús
author_role author
author2 Banks, David
Ríos, Jesús
author2_role author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Decision analysis
Bayesian model averaging
Adversarial risk analysis
Opponent modeling
Simultaneous games
topic Decision analysis
Bayesian model averaging
Adversarial risk analysis
Opponent modeling
Simultaneous games
description Adversarial risk analysis has been introduced as a framework to deal with risks derived from intentional actions of adversaries. The analysis supports one of the decisionmakers, who must forecast the actions of the other agents. Typically, this forecast must take account of random consequences resulting from the set of selected actions. The solution requires one to model the behavior of the opponents, which entails strategic thinking. The supported agent may face different kinds of opponents, who may use different rationality paradigms, for example, the opponent may behave randomly, or seek a Nash equilibrium, or perform level-k thinking, or use mirroring, or employ prospect theory, among many other possibilities. We describe the appropriate analysis for these situations, and also show how to model the uncertainty about the rationality paradigm used by the opponent through a Bayesian model averaging approach, enabling a fully decision-theoretic solution. We also show how as we observe an opponent's decision behavior, this approach allows learning about the validity of each of the rationality models used to predict his decision by computing the models' (posterior) probabilities, which can be understood as a measure of their validity. We focus on simultaneous decision making by two agents.
publishDate 2016
dc.date.none.fl_str_mv 2016
2019
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/196670
url http://hdl.handle.net/10261/196670
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
MINECO/ICTI2013-2016/MTM2014‐56949‐C3‐1‐R
info:eu-repo/grantAgreement/EC/FP7/285223
info:eu-repo/grantAgreement/MICINN//MTM2011‐28983‐C3‐1
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Blackwell Publishing
publisher.none.fl_str_mv Blackwell Publishing
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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