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An Adversarial Risk Analysis Framework for Batch Acceptance Problems

Author

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  • Jorge González-Ortega

    (Departamento de Estadística e Investigación Operativa, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, Madrid 28040, Spain)

  • Refik Soyer

    (Department of Decision Sciences, George Washington University, Washington, District of Columbia 20052)

  • David Ríos Insua

    (School of Management, University of Shanghai for Science and Technology, Shanghai 200092, China; Instituto de Ciencias Matemáticas, Consejo Superior de Investigaciones Científicas, Universidad Autónoma de Madrid, Universidad Carlos III de Madrid, Universidad Complutense de Madrid, Madrid 28049, Spain)

  • Fabrizio Ruggeri

    (Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, I-20133 Milano, Italy)

Abstract

We provide an adversarial risk analysis framework for batch acceptance problems in which a decision maker relies exclusively on the size of the batch to accept or reject its admission to a system, albeit being aware of the presence of an opponent. The adversary acts as a data-fiddler attacker perturbing the observations perceived by the decision maker through injecting faulty items and/or modifying the existing items to faulty ones. We develop optimal policies against this combined attack strategy and illustrate the methodology with a review spam example.

Suggested Citation

  • Jorge González-Ortega & Refik Soyer & David Ríos Insua & Fabrizio Ruggeri, 2021. "An Adversarial Risk Analysis Framework for Batch Acceptance Problems," Decision Analysis, INFORMS, vol. 18(1), pages 25-40, March.
    • Handle: RePEc:inm:ordeca:v:18:y:2021:i:1:p:25-40
    DOI: 10.1287/deca.2020.0420
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    References listed on IDEAS

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    Cited by:

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    2. William N. Caballero & Ethan Gharst & David Banks & Jeffery D. Weir, 2023. "Multipolar Security Cooperation Planning: A Multiobjective, Adversarial-Risk-Analysis Approach," Decision Analysis, INFORMS, vol. 20(1), pages 16-39, March.
    3. Muhammad Ejaz & Stephen Joe & Chaitanya Joshi, 2021. "Adversarial Risk Analysis for Auctions Using Mirror Equilibrium and Bayes Nash Equilibrium," Decision Analysis, INFORMS, vol. 18(3), pages 185-202, September.
    4. Farkas, Sébastien & Lopez, Olivier & Thomas, Maud, 2021. "Cyber claim analysis using Generalized Pareto regression trees with applications to insurance," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 92-105.
    5. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).
    6. Ekin, Tahir & Naveiro, Roi & Ríos Insua, David & Torres-Barrán, Alberto, 2023. "Augmented probability simulation methods for sequential games," European Journal of Operational Research, Elsevier, vol. 306(1), pages 418-430.

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