Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/196670
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Modeling Opponents in Adversarial Risk Analysis |
Autor: | Ríos Insua, David ; Banks, David; Ríos, Jesús | Palabras clave: | Decision analysis Bayesian model averaging Adversarial risk analysis Opponent modeling Simultaneous games |
Fecha de publicación: | 2016 | Editor: | Blackwell Publishing | Citación: | Risk Analysis 36: 742- 755 (2016) | Resumen: | 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. | URI: | http://hdl.handle.net/10261/196670 | DOI: | 10.1111/risa.12439 | Identificadores: | doi: 10.1111/risa.12439 issn: 1539-6924 |
Aparece en las colecciones: | (ICMAT) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
603712.pdf | 232,02 kB | Unknown | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
26
checked on 18-abr-2024
WEB OF SCIENCETM
Citations
21
checked on 25-feb-2024
Page view(s)
168
checked on 23-abr-2024
Download(s)
531
checked on 23-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.