Legacy constructive military simulation systems such as the Corps Battle Simulation (CBS) and the Joint Theater Level Simulation (JTLS) have simple models of military commanders and their decision-making process. It would be useful to the military community to advance the robustness and the realism of these decision models. To improve human decisions in a simulation we need better models of human decision-making. The Recognition-Primed Decision (RPD) model was developed to explain the mental process that decision makers, especially experienced ones such as senior military commanders, go through to arrive at a decision. Several efforts are ongoing to try to produce a computational model of RPD. These efforts are centered on rule-based, neural network, and fuzzy logic approaches. This paper analyzes a different approach, using a composite agent, to implement the RPD model. A composite agent uses multi-agent system simulation technology to implement various cognitive processes of a single entity or agent. It is this author’s contention that a composite agent’s decision-making method closely matches that described by the RPD model. This close match is expected to produce a better implementation of the RPD model.
Contributors. Foreword. Preface. Part One Fundamentals of Medical and Health Sciences Modeling and Simulation. 1 Introduction to Modeling and Simulation in the Medical and Health Sciences (Catherine M. Banks). 2 The Practice of Modeling and Simulation: Tools of the Trade (John A. Sokolowski). Part Two. Modeling for the Medical and Health Sciences. 3 Mathematical Models of Tumor Growth and Wound Healing (John A. Adam). 4 Physical Modeling (Stacie I. Ringleb). Part Three. Modeling and Simulation Applications. 5 Humans as Models (C. Donald Combs). 6 Modeling the Human System (Mohammed Ferdjallah and Gyu Tae Kim). 7 Robotics (Richard Lee). 8 Training (Paul E. Phrampus). 9 Patient Care (Eugene Santos Jr, Joseph Rosen, Keum Joo Kim, Fei Yu, Dequing Li, Elizabeth Jacob, Lindsay Katona). 10 Future of Modeling and Simulation in the Medical and Health Sciences (Richard M. Satava). Appendix. Index.
Used to describe some interesting and usually unanticipated pattern or behavior, the term emergence is often associated with time-evolutionary systems comprised of relatively large numbers of interacting yet simple entities. A significant amount of previous research has recognized the emergence phenomena in many real-world applications such as collaborative robotics, supply chain analysis, social science, economics and ecology. As improvements in computational technologies combined with new modeling paradigms allow the simulation of ever more dynamic and complex systems, the generation of data from simulations of these systems can provide data to explore the phenomena of emergence.
To explore some of the modeling implications of systems where emergent phenomena tend to dominate, this research examines three simulations based on familiar natural systems where each is readily recognized as exhibiting emergent phenomena. To facilitate this exploration, a taxonomy of Emergent Behavior Systems (EBS) is developed and a modeling formalism consisting of an EBS lexicon and a formal specification for models of EBS is synthesized from the long history of theories and observations concerning emergence. This modeling formalism is applied to each of the systems and then each is simulated using an agent-based modeling framework.
To develop quantifiable measures, associations are asserted: 1) between agent-based models of EBS and graph-theoretical methods, 2) with respect to the formation of relationships between entities comprising a system and 3) concerning the change in uncertainty of organization as the system evolves.
These associations form the basis for three measurements related to the information flow, entity complexity, and spatial entropy of the simulated systems. These measurements are used to: 1) detect the existence of emergence and 2) differentiate amongst the three systems.
The results suggest that the taxonomy and formal specification developed provide a workable, simulation-centric definition of emergent behavior systems consistent with both historical concepts concerning the emergence phenomena and modern ideas in complexity science. Furthermore, the results support a structured approach to modeling these systems using agent-based methods and offers quantitative measures useful for characterizing the emergence phenomena in the simulations.
This chapter contains sections titled: Introduction Cadavers and Wax Models Standardized Patients Plastination Human Data Sets Conclusion Key Terms References
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.
Governmental and institutional service providers assigned to areas where involuntary or forced population movement occurs are required to provide goods and security as well as policies and strategies to distressed peoples. This type of population displacement took place on an epic scale in 2011. Modeling and simulation presents a constructive approach to critical analysis and projections needed for decision-making via the representation and characterization of distressed populations to envisage why, when, and where migration will occur. This paper presents a multi-disciplinary methodology to researching and modeling population displacement in a broad, yet inclusive sweep. An Environment Matrix and an Agent Matrix are presented that can be used as a template to develop an agent-based model to capture this phenomenon. These matrices facilitate an accurate representation of an environment and a thorough characterization of agents. The crisis in Syria serves as a use-case for matrix development. This type of agent-based modeling and analysis can proffer insight on how to prevent, hold constant, or moderate escalating effects of threats to populations in jeopardy as well as anticipate when forced migration might occur.
The visualization aspect of modeling and simulation (M&S) is rarely discussed. The primary concerns are typically model design, statistical probabilities, analysis, and verification and validation. However, visualization is an integral part of the M&S process and provides several opportunities to provide insightful information for developers and users alike. In recent years, discussions on data visualization have arisen including what makes an good visualization. In this paper, we provide discussion for what makes a visualization good and whether visualization evaluation techniques are applicable to M&S. We outline a basic taxonomy of M&S visualization and discuss the current state and issues faced by each type. The wider field of data visualization is also discussed to give context for M&S visualization. The paper concludes with a discussion on way forward for evaluating effective M&S visualization.
Although the story of the Irish struggle for independence and ethnic recognition goes back centuries, the 20th century proved most violent. This case study focuses on the Irish insurgency in the early decades, specifically the Easter Rising of 1916 and the Anglo-Irish War of 1919-1921. The insurgency will be characterised with an engineering modelling technique that captures relationships between and among inorganic and organic factors, i.e., events and human behaviour/response. System dynamics will be used to facilitate a holistic representation of these events and relationships from a macro to micro perspective. The purpose of the study is to better understand the Irish struggle for national and ethnic recognition through a system dynamics model that facilitates assessment of cause and effect factors, direct and indirect variables, and corresponding and correlative relationships of the insurgency as a complex system. The paper proffers a research and representation methodology that addresses the constraints placed on typical empirical research by coupling social science research methods with advanced system modelling applications.
