A decision support framework for security resource allocation under ambiguity. Issue 1 (14th October 2020)

• Record Type: Journal Article
• Authors: Ma, Wenjun; Liu, Weiru; McAreavey, Kevin; Luo, Xudong; Jiang, Yuncheng; Zhan, Jieyu; Chen, Zhenzhou
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 1(2021)
• Page Start: 5
• Page End: 52
• Publication Date: 2020-10-14
• Subjects: ambiguity -- decision making under uncertainty -- D‐S theory -- game theory -- public security -- security game; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22288
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: There has been increasing interest in using Stackelberg game (known as a security game) to allocate limited security resources against different attacker types with a specific probability distribution. However, real problems of this kind often face ambiguous information, such as imprecise, unreliable and absent payoffs, and ambiguous assignments of these payoffs. To this end, based on decision theory and the Dempster–Shafer theory of evidence, this paper proposes a novel framework that can handle these common types of ambiguity. More specifically, this paper deploys the underlying principles of existing rules from decision theory, as a way to characterise different attitudes to ambiguity, during the transformation of ambiguous payoffs into point‐valued payoffs. Hence, our framework holds some good properties: (i) it subsumes traditional security games without ambiguous payoffs, (ii) a uniform margin of error will not affect the results and (iii) the influence of complete ignorance can be minimised. Also, our framework is evaluated by using nine different transformation rules, under various conditions and constraints, against 73, 000 randomly generated games (a first comprehensive empirical evaluation to date). The evaluation reveals the benefits of each transformation rule and confirms that different rules can model individuals' different attitudes to ambiguity.