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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, thieves intentionally intrude a plant for stealing valuable materials, or terrorists maliciously set a fire on a chemical facility to cause societal fear. Initiators of security events (henceforth, attackers) would intelligently 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 the defender and the attacker are not considered. Game theory was invented in the economic domain for modelling both the cooperative and competitive behaviours in a multiple actors system. In the last 100 years, game theory has been theoretically improved and practically applied to various domains, such as the evolutionary biology, the nuclear balance, computer science etc. These researches 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 that applying the same methodology to the same plant, different analysts may come to different conclusions. Some quantitative security risk assessment models, for instance, by employing a Bayesian 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 on modelling the intelligent interactions between the defender and the attacker. Game theory, conversely, has a rigorous mathematical foundation and models the intelligent interactions. A game theoretic model explicitly indicates 1) who is involved in the game; 2) what actions can each participant 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 quantitatively indicates what should the participants do (i.e., the equilibrium strategy) and what will each participant obtain (i.e., the equilibrium payoff). A critical issue is that industrial managers often prefer a qualitative approach and they have difficulties on understanding (the physical meaning of) the quantitative outputs of a game theoretical model. This issue can be addressed by requiring game developers to do a further step work by also translating/mapping their quantitative outputs to qualitative descriptions, and the latter should be expressed in terminologies that industrial practitioners are familiar with. Figure 1 illustrates the idea., Journal of Integrated Security Science, Vol 2 No 1 (2018)

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases.

Historical data analysis shows that escalation accidents, so-called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent-based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent-based modeling technique explains the domino effects from a bottom-up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher-level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large-scale complicated cases.

The New York City 9/11 terrorist attacks urged people from academia as well as from industry to pay more attention to operational security research. The required focus in this type of research is human intention. Unlike safety-related accidents, security-related accidents have a deliberate nature, and one has to face intelligent adversaries with characteristics that traditional probabilistic risk assessment techniques are not capable of dealing with. In recent years, the mathematical tool of game theory, being capable to handle intelligent players, has been used in a variety of ways in terrorism risk assessment. In this article, we analyze the general intrusion detection system in process plants, and propose a game-theoretical model for security management in such plants. Players in our model are assumed to be rational and they play the game with complete information. Both the pure strategy and the mixed strategy solutions are explored and explained. We illustrate our model by an illustrative case, and find that in our case, no pure strategy but, instead, a mixed strategy Nash equilibrium exists.

Emergency management is crucial to finding effective ways to minimize or even eliminate the damage of emergent events, but there still exists no quantified method to study the events by computation. Statistical algorithms, such as susceptible-infected-recovered (SIR) models on epidemic transmission, ignore many details, thus always influencing the spread of emergent events. In this paper, we first propose an agent-based modeling and experiment framework to model the real world with the emergent events. The model of the real world is called artificial society, which is composed of agent model, agent activity model, and environment model, and it employs finite state automata (FSA) as its modeling paradigm. An artificial campus, on which a series of experiments are done to analyze the key factors of the acute hemorrhagic conjunctivitis (AHC) transmission, is then constructed to illustrate how our method works on the emergency management. Intervention measures and optional configurations (such as the isolation period) of them for the emergency management are also given through the evaluations in these experiments.

The research goals of this paper are the mental state coordination and collective synchronization mechanism of multi-agent groups. In this paper, I create a virtual forum using partial of the existing data of Facebook. Based on the virtual forum, I put forward a non-smooth synchronization model on the basis of global coordination of the mental state of agent groups. I also perform simulation experiments through the virtual forum, and carry out in-depth analysis and researches of the prominent aggregation phenomenon. With the above activities, I obtain the conclusion that in the organization networks like Facebook, public opinions are easy to tend to convergence.

Aim at preventing, or controlling if prevention is not possible, the spread of disease. We model several types of commonly-used government interventions in order to quantify this research. Finally we computationally tested the models using an artificial campus. The results show: 1) Campus pandemics extinguish even without intervention 2) Small scale inoculation programs are ineffectual, but large scale inoculation programs will bring non-linear increases in benefits 3) Identifying and isolating the infectious and their `strong social group' quickly dramatically lowers spread 4)Isolation Plus Close Public-space Intervention will decrease the peak value and the last time. This study can support quantitative experimentation and prediction of infectious diseases within predefined areas, and assessment of intervention strategies.

An approach based on game theory is proposed to schedule security patrolling for a pipeline system. The method proposes numbers of patrolling paths according to the risk of security incidents on the pipeline system, in order to allow the patrolling team to covering high-risk segments more than low-risk segments. Patrolling of the pipeline system was modeled mathematically, based on time and distance discretization. The overall approach also examines the presence of security countermeasures on a pipeline system, and their effects on the patrolling schedule. The application of the method is explained by an illustrative case study.