John A. Sokolowski

Probabilistic Risk Analysis and Terrorism Risk

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.

A Conceptual Modeling Method for Critical Infrastructure Modeling

Conceptual Modeling often is perceived as a way of introducing the process of modeling a system, by concentrating on a reduced appreciation of that system, with a necessary reduction in the number of affecting variables and relations making up the model. As an alternative to this approach, rather than reduce the number of variables and relations, it has been found to be useful to include as many as possible variables and relations (resulting from a functional decomposition of a class of like systems) in a Potential Model (which represents the potential of all specific instances of the class of like systems), and then to move towards a Specific Model by applying contextual and situational values to the appropriate variables and relations that apply to the actual case. In so doing, the necessary reduction in variables and relations in moving from Potential Model to Specific Model allows the conceptual model to lead to further steps in the modeling process, ensuring that all relevance to the class of system is retained. This technique has been applied to a critical infrastructure modeling project, with both the method and results being presented here.

Enhanced decision modeling using multiagent system simulation

Modeling and simulation is being used more and more to capture our physical world in a computational form. However, simulating the mental processes that go on within the mind is a complex task that has met with far less success. To improve on this situation, the author developed a computational model of the decision process used by experienced decision makers. This model has, as its basis, the cognitive decision theory entitled naturalistic decision making and its specific implementation, called recognition primed decision (RPD) making. Specifically, this research developed a model that mimicked the decision process of a senior military commander at the operational level of warfare. The model, called RPDAgent, was validated against decisions made by actual military officers. RPDAgent produced decisions that were equivalent to its human counterparts—they were not optimum decisions, but they reflected the variability inherent in those made by humans in an operational military environment.

A multi-paradigm modeling framework for modeling and simulating problem situations

This paper proposes a multi-paradigm modeling framework (MPMF) for modeling and simulating problem situations (problems whose specification is not agreed upon). The MPMF allows for a different set of questions to be addressed from a problem situation than is possible through the use of a single modeling paradigm. The framework identifies different levels of granularity (macro, meso, and micro) from what is known and assumed about the problem situation. These levels of granularity are independently mapped to different modeling paradigms. These modeling paradigms are then combined to provide a comprehensive model and corresponding simulation of the problem situation. Finally, the MPMF is implemented to model and simulate the problem situation of representing the spread of obesity.

Modeling population displacement in the syrian city of aleppo

The persistent crisis in Syria has affected millions of its citizens by forcing their displacement from native or accustomed residences. Modeling the Syrian conflict provides a computational means to better understand why, when, and where these citizens flee. Thus, an agent-based model drawn on real-world data to represent Syrian cities (the environment) and the demographic constitution of those cities (the agents) has been developed and is explained in this discussion. The outputs of the model accurately reflect population displacement as it occurred in 2013. Importantly, the purpose of this agent-based modeling and the output analysis is to develop a means to anticipate, measure, and assess future displacement in Syria as well as to model other threatened populations in crises where displacement might occur. This paper presents the methodology to crafting the environment and agents to represent the Syrian city of Aleppo and the displacement of its citizens.

Modeling the Niger Delta Insurgency

Abstract Nigerias oil guarantees its role in the global economy; however, the states overwhelming weaknesses perpetuate a high level of volatility vis-à-vis the potential for an immediate disruption of the national, regional, and global environment. This study presents a current qualitative analysis of Nigeria to produce a model that characterizes current conditions in the state and the state response to the nefarious acts in the delta region. This model will serve as a baseline for a significant iteration of the model that reflects the delta region counter-insurgency at various levels of strength: a what-if scenario of the region with an increase of state security over southern separatists, insurgents seeking to gain control of the oil assets. The conjectured model serves to inform/educate decision and policy makers in developing proactive, effective strategies to counter existent and potential threats in the Niger Delta.

The Digital Patient: Advancing Healthcare, Research, and Education

The healthcare industry’s emphasis is shifting from merely reacting to disease to preventing disease and promoting wellness. Addressing one of the more hopeful Big Data undertakings, The Digital Patient: Advancing Healthcare, Research, and Education presents a timely resource on the construction and deployment of the Digital Patient and its effects on healthcare, research, and education. The Digital Patient will not be constructed based solely on new information from all the “omics” fields; it also includes systems analysis, Big Data, and the various efforts to model the human physiome and represent it virtually. The Digital Patient will be realized through the purposeful collaboration of patients as well as scientific, clinical, and policy researchers.

Applying reinforcement learning to an insurgency Agent-based Simulation:

A requirement of an Agent-based Simulation (ABS) is that the agents must be able to adapt to their environment. Many ABSs achieve this adaption through simple threshold equations due to the complexity of incorporating more sophisticated approaches. Threshold equations are when an agent behavior changes because a numeric property of the agent goes above or below a certain threshold value. Threshold equations do not guarantee that the agents will learn what is best for them. Reinforcement learning is an artificial intelligence approach that has been extensively applied to multi-agent systems but there is very little in the literature on its application to ABS. Reinforcement learning has previously been applied to discrete-event simulations with promising results; thus, reinforcement learning is a good candidate for use within an Agent-based Modeling and Simulation (ABMS) environment. This paper uses an established insurgency case study to show some of the consequences of applying reinforcement learning to ABMS, for example, determining whether any actual learning has occurred. The case study was developed using the Repast Simphony software package.

Handbook of Real-World Applications in Modeling and Simulation: Sokolowski/Modeling and Simulation Handbook

Handbook of Real-World Applications in Modeling and Simulation provides a thorough explanation of modeling and simulation in the most useful, current, and predominant applied areas of transportation, homeland security, medicine, operational research, military science, and business modeling. Offering a cutting-edge and accessible presentation, this book discusses how and why the presented domains have become leading applications of modeling and simulation techniques.

Using an Agent-based Model to Explore Troop Surge Strategy

In October of 2001, the United States invaded Afghanistan and replaced the Taliban government. Since its overthrow, the Taliban has pieced together and waged an insurgency to retake Afghanistan, and that insurgency has gained momentum and grown in strength while the United States/North Atlantic Treaty Organization (NATO) effort shrank in size to about 55,000 troops in 2007. A wide range of factors contributed to the insurgency, ranging from socio-cultural to economic to political. This research applied an in-depth study of Afghanistan to an agent-based model to determine if a military troop surge emphasizing a focused security effort could be successful in battling the growing insurgency within Afghanistan. An agent-based model was created and validated against the strategy and situation on the ground in Afghanistan that existed in 2007. Three experiments were conducted representing surges of 50%, 200%, and 400%. The results indicated that a surge of 200% or greater of the existing size force would be necessary to reduce the size of the insurgency, but that a surge of only 50% (50,000 more troops) would not bring about any significant changes as compared to the existing strategy. These model results provide insight into the potential success of various sized troop surges in Afghanistan that implement a focused security effort.