An Adversarial Risk Analysis Framework for Batch Acceptance Problems

Jorge González-Ortega (), Refik Soyer (), David Ríos Insua () and Fabrizio Ruggeri ()
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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

Decision Analysis, 2021, vol. 18, issue 1, 25-40

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.

Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:18:y:2021:i:1:p:25-40

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