Applying game theory for improving security in the process industries

Why should game theory be introduced and used in the chemical security practitioners?Security risks are initiated by deliberate behaviours for certain goals. For instance, thievesintentionally intrude a plant for stealing valuable materials, or terrorists maliciously set a fire on achemical facility to cause societal fear. Initiators of security events (henceforth, attackers) wouldintelligently observe the defender’s defence plan and then schedule their attack accordingly.Powell (2007) illustrated how resources can be mis-allocated if intelligent interactions between thedefender and the attacker are not considered. Game theory was invented in the economic domainfor modelling both the cooperative and competitive behaviours in a multiple actors system. In thelast 100 years, game theory has been theoretically improved and practically applied to variousdomains, such as the evolutionary biology, the nuclear balance, computer science etc. Theseresearches have demonstrated the capability of game theory in modelling intelligent interactions.Industrial managers need quantitative recommendations to support their decision making.Conventional security risk assessment methodologies (e.g., the API SRA framework (API, 2013)),being good at studying security systematically, are not able to provide quantitative insights. Moreover, results of these conventional methodologies are not repeatable which means thatapplying the same methodology to the same plant, different analysts may come to differentconclusions. Some quantitative security risk assessment models, for instance, by employing aBayesian Network framework (e.g., Argenti et al. (2018); Landucci et al. (2017); Fakhravar et al.(2017)), can provide quantitative and repeatable results as well. Nevertheless, these models fail onmodelling the intelligent interactions between the defender and the attacker. Game theory,conversely, has a rigorous mathematical foundation and models the intelligent interactions. Agame theoretic model explicitly indicates 1) who is involved in the game; 2) what actions can eachparticipant take; 3) what results (numbers) will each participant obtain, for each participants’strategy combination; 4) how much information that each participant has about the game.Furthermore, outputs of a game theoretic model (i.e., equilibrium) clearly and quantitativelyindicates what should the participants do (i.e., the equilibrium strategy) and what will eachparticipant obtain (i.e., the equilibrium payoff).A critical issue is that industrial managers often prefer a qualitative approach and they havedifficulties on understanding (the physical meaning of) the quantitative outputs of a gametheoretical model. This issue can be addressed by requiring game developers to do a further stepwork by also translating/mapping their quantitative outputs to qualitative descriptions, and thelatter should be expressed in terminologies that industrial practitioners are familiar with. Figure 1illustrates the idea