IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v179y2018icp3-11.html
   My bibliography  Save this article

A graphical method for simplifying Bayesian games

Author

Listed:
  • Thwaites, Peter A.
  • Smith, Jim Q.

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.

Suggested Citation

  • Thwaites, Peter A. & Smith, Jim Q., 2018. "A graphical method for simplifying Bayesian games," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 3-11.
  • Handle: RePEc:eee:reensy:v:179:y:2018:i:c:p:3-11
    DOI: 10.1016/j.ress.2017.05.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832017305355
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2017.05.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Koller, Daphne & Milch, Brian, 2003. "Multi-agent influence diagrams for representing and solving games," Games and Economic Behavior, Elsevier, vol. 45(1), pages 181-221, October.
    2. James E. Smith & Samuel Holtzman & James E. Matheson, 1993. "Structuring Conditional Relationships in Influence Diagrams," Operations Research, INFORMS, vol. 41(2), pages 280-297, April.
    3. Concha Bielza & Prakash P. Shenoy, 1999. "A Comparison of Graphical Techniques for Asymmetric Decision Problems," Management Science, INFORMS, vol. 45(11), pages 1552-1569, November.
    4. Smith, J. Q., 1989. "Influence diagrams for Bayesian decision analysis," European Journal of Operational Research, Elsevier, vol. 40(3), pages 363-376, June.
    5. Joseph B. Kadane & Patrick D. Larkey, 1983. "The Confusion of Is and Ought in Game Theoretic Contexts," Management Science, INFORMS, vol. 29(12), pages 1365-1379, December.
    6. Debarun Bhattacharjya & Ross D. Shachter, 2012. "Formulating Asymmetric Decision Problems as Decision Circuits," Decision Analysis, INFORMS, vol. 9(2), pages 138-145, June.
    7. John C. Harsanyi, 1967. "Games with Incomplete Information Played by "Bayesian" Players, I-III Part I. The Basic Model," Management Science, INFORMS, vol. 14(3), pages 159-182, November.
    8. Ross D. Shachter, 1986. "Evaluating Influence Diagrams," Operations Research, INFORMS, vol. 34(6), pages 871-882, December.
    9. Zvi Covaliu & Robert M. Oliver, 1995. "Representation and Solution of Decision Problems Using Sequential Decision Diagrams," Management Science, INFORMS, vol. 41(12), pages 1860-1881, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bielza, Concha & Gómez, Manuel & Shenoy, Prakash P., 2011. "A review of representation issues and modeling challenges with influence diagrams," Omega, Elsevier, vol. 39(3), pages 227-241, June.
    2. Lopez-Diaz, Miguel & Rodriguez-Muniz, Luis J., 2007. "Influence diagrams with super value nodes involving imprecise information," European Journal of Operational Research, Elsevier, vol. 179(1), pages 203-219, May.
    3. González-Ortega, Jorge & Ríos Insua, David & Cano, Javier, 2019. "Adversarial risk analysis for bi-agent influence diagrams: An algorithmic approach," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1085-1096.
    4. Borgonovo, Emanuele & Tonoli, Fabio, 2014. "Decision-network polynomials and the sensitivity of decision-support models," European Journal of Operational Research, Elsevier, vol. 239(2), pages 490-503.
    5. Concha Bielza & Prakash P. Shenoy, 1999. "A Comparison of Graphical Techniques for Asymmetric Decision Problems," Management Science, INFORMS, vol. 45(11), pages 1552-1569, November.
    6. Debarun Bhattacharjya & Ross D. Shachter, 2012. "Formulating Asymmetric Decision Problems as Decision Circuits," Decision Analysis, INFORMS, vol. 9(2), pages 138-145, June.
    7. Prakash Shenoy, 1998. "Game Trees For Decision Analysis," Theory and Decision, Springer, vol. 44(2), pages 149-171, April.
    8. Demirer, Riza & Shenoy, Prakash P., 2006. "Sequential valuation networks for asymmetric decision problems," European Journal of Operational Research, Elsevier, vol. 169(1), pages 286-309, February.
    9. Ruth Y. Dicdican & Yacov Y. Haimes, 2005. "Relating multiobjective decision trees to the multiobjective risk impact analysis method," Systems Engineering, John Wiley & Sons, vol. 8(2), pages 95-108.
    10. Salo, Ahti & Andelmin, Juho & Oliveira, Fabricio, 2022. "Decision programming for mixed-integer multi-stage optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 299(2), pages 550-565.
    11. Shenoy, Prakash P., 2000. "Valuation network representation and solution of asymmetric decision problems," European Journal of Operational Research, Elsevier, vol. 121(3), pages 579-608, March.
    12. Rodriguez-Muniz, Luis J. & Lopez-Diaz, Miguel & Gil, Maria Angeles, 2005. "Solving influence diagrams with fuzzy chance and value nodes," European Journal of Operational Research, Elsevier, vol. 167(2), pages 444-460, December.
    13. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    14. Ríos Insua, David & Ruggeri, Fabrizio & Soyer, Refik & Rasines, Daniel G., 2018. "Adversarial issues in reliability," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1113-1119.
    15. C. L. Smith & E. Borgonovo, 2007. "Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 1027-1042, August.
    16. Guilhem Lecouteux, 2018. "Bayesian game theorists and non-Bayesian players," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 25(6), pages 1420-1454, November.
    17. Michael Macgregor Perry & Hadi El-Amine, 2021. "Computational Efficiency in Multivariate Adversarial Risk Analysis Models," Papers 2110.12572, arXiv.org.
    18. Cho, Sungbin, 2009. "A linear Bayesian stochastic approximation to update project duration estimates," European Journal of Operational Research, Elsevier, vol. 196(2), pages 585-593, July.
    19. John M. Charnes & Prakash P. Shenoy, 2004. "Multistage Monte Carlo Method for Solving Influence Diagrams Using Local Computation," Management Science, INFORMS, vol. 50(3), pages 405-418, March.
    20. David Rios Insua & David Banks & Jesus Rios, 2016. "Modeling Opponents in Adversarial Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 742-755, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:179:y:2018:i:c:p:3-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.