Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

How to Discourage Adversaries From Affecting Decision Outcomes of a Repeated Patent Application Decision--Making Process

Version 1 : Received: 25 June 2021 / Approved: 28 June 2021 / Online: 28 June 2021 (15:22:26 CEST)

How to cite: van der Sluis, W. How to Discourage Adversaries From Affecting Decision Outcomes of a Repeated Patent Application Decision--Making Process. Preprints 2021, 2021060676. https://doi.org/10.20944/preprints202106.0676.v1 van der Sluis, W. How to Discourage Adversaries From Affecting Decision Outcomes of a Repeated Patent Application Decision--Making Process. Preprints 2021, 2021060676. https://doi.org/10.20944/preprints202106.0676.v1

Abstract

Outcomes of repeated decision--making processes may be affected by adversarial actors, without being noticed. Adversaries may try to gain knowledge about a particular decision--making process, identify its decision--makers, and guess which underlying decision support model is used. Then they can simulate the process, and craft different scenarios to affect its decision outcomes. Therefore, designers of decision support systems need to incorporate this in the decision modeling phase. The purpose of this study is to demonstrate this for the repeated decision--making in a patent application process. In this process, two sequential decision outcomes can be affected by adversarial actors: a company's decision to which type of patent office to send a patent request to, and the decision of a specialized patent officer to grant an application, or not. It is motivated that the company's decision--maker is \emph{bounded} rational. A theory for information--theoretic bounded rational decision--making under uncertainty proposed by Ortega et al.\ is adopted to model this type of decision--maker. A framework is provided to simulate a number of scenarios that adversaries may deploy to affect decision outcomes of a repeated patent application decision--making process. The framework is also utilized for statistically testing the presence of the scenarios, and to demonstrate how to discourage adversaries from deploying them.

Keywords

Adversarial risk analysis and decision analysis; information--theoretic bounded rational decision--making; simulation

Subject

Computer Science and Mathematics, Algebra and Number Theory

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