Catherine M. Banks

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.

Investigating social dynamics and global connectivity: an agent-based modeling approach

Social scientists have been exploring the factors that contribute to the assimilation and standardization of population as they affect a populace over a period of time. Research in this field has made the case for employing agent-based models to investigate this social phenomena. This research builds on the basic tenants of these previous approaches as a way to investigate the nature of global connectivity as it affects the standardization of a strong central culture of a specific region. The simulation runs conducted in this study make clear that the more cultural features that “characterize” agents, the less likely it is for their cultures to assimilate. The simulations also indicate that even when a culture has a 50%–60% chance of being attracted to the features of an outside culture, almost total cultural migration is possible. The paper presents an agent-based modeling approach to analyze dynamic (fluid) cultural change vis-à-vis global connectivity.

A methodology to explore the impact of policy changes on insurgencies

This study proposes a methodology to model the flow of a population into and out of an insurgency as a way of assessing counter-insurgency strategy and government policy. The causes, means and capacity of insurgencies are fluid, but the influence of government policy can be constant. Therefore, exploring the impact of policy change on insurgencies is valid and useful. This research provides a historical analysis of insurgency to provide a set number of variables that affect population behaviour in what are traditional insurgency activities. It then progresses to a specific case study, Colombia, a state that is now waging its insurgency as part of the Global War on Terrorism. A system dynamics approach will capture the model and represent the complex interdependencies of the variables influencing the system. The result is a model that portrays the effects of policy change in the state of Colombia.

Agent implementation for modeling insider threat

Insider threat modeling focuses primarily on the individual and the prediction of an insider threat incident. The majority of these models are statistical that tend toward trend-projections using various regression models. The modeling presented in this paper engages an agent-based paradigm that is designed to explore how an agent interacts with other employees and the organization in an environment that grants the agent opportunity and access. This paper continues our research with a discussion of the implementation of the agents decision-making in the context of emotional, rational, and social factors affecting agent disposition. We proffer that once the agents disposition reaches or exceeds a personal threshold, the agent is disposed to become an active threat. Our refinement of the agent facilitates continued and more rounded discussion to our original research question: “How and when does a predisposed insider make the decision to become an active insider threat?”