Thwaites, PA orcid.org/0000-0001-9700-2245 and Smith, JQ (2018) A graphical method for simplifying Bayesian games. Reliability Engineering and System Safety, 179. pp. 3-11. ISSN 0951-8320
Abstract
If the influence diagram (ID) depicting a Bayesian game is common knowledge to its players then additional assumptions may allow the players to make use of its embodied irrelevance statements. They can then use these to discover a simpler game which still embodies both their optimal decision policies. However the impact of this result has been rather limited because many common Bayesian games do not exhibit sufficient symmetry to be fully and efficiently represented by an ID. The tree-based chain event graph (CEG) has been developed specifically for such asymmetric problems. By using these graphs rational players can make analogous deductions, assuming the topology of the CEG as common knowledge. In this paper we describe these powerful new techniques and illustrate them through an example modelling a game played between a government department and the provider of a website designed to radicalise vulnerable people.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Elsevier Ltd. This is an author produced version of a paper published in Reliability Engineering and System Safety. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Adversarial risk; Bayesian game theory; Chain event graph; Decision tree; Influence diagram; Parsimony |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/M018687/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 May 2017 11:18 |
Last Modified: | 13 Sep 2018 03:33 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ress.2017.05.012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116015 |