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Chapter XIII Modeling with System Archetypes: a Case Study Chapter XIII Modeling with System Archetypes: A Case Study Mahendran Maliapen IGate Consulting, Canada ABSTRACT This chapter examines the application of system archetypes as a systems development methodology to create simulation models. Rapid organizational change and need to adapt to new business models limits the lifespan of both the databases and software applications. With the information representa- tion permitted by archetypes, diagnostic analysis and can help to evolve generic classes and models for representing the real world. Archetypes do not describe any one problem specifically. They describe families of problems generically. Their value comes from the insights they offer into the dynamic inter- action of complex systems. The case of a healthcare system is discussed here. As part of a suite of tools, they are extremely valuable in developing broad understandings about the hospital and its environment, and contribute more effectively to understanding problems. They are highly effective tools for gaining insight into patterns of strategic behavior, themselves reflective of the underlying structure of the system being studied. Diagnostically, archetypes help hospital managers recognize patterns of behavior that are already present in their organizations. They serve as the means for gaining insight into the underlying systems structures from which the archetypal behavior emerges. In the casemix model of the hospital, the investigation team discovered that some of the phenomena as described by these generic archetypes could be represented. The application of system archetypes to the strategic business analysis of the hos- pital case reveals that it is possible to identify loop holes in management’s strategic thinking processes and it is possible to defy these fallacies during policy implementation as illustrated by the results of the archetype simulation model. In this research study, hospital executives found that policy modification with slight variable changes helps to avoid such pitfalls in systems thinking and avoid potentially cost prohibitive learning had these policies been implemented in real life. Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Modeling with System Archetypes INTRODUCTION such characteristic behavior is required. With the publication of The Fifth Discipline (Senge, The system development methodologies applied to 1990), there was dearth of interest in using the information systems are based on the premise that science of archetypes; explicit system modeling the domain concepts with the use of information of complex issues can be achieved by examining engineering tools represent business processes the whole system. The goal is to understand how and data directly into software and database the feedback structure of a system contributes models (Jacobsen, 1992; Martin & Odell, 1992; to its dynamic behavior. The stocks and flows, Rumbaugh et al., 1991; Walden & Nerson, 1995). the polarities of feedback loops interconnecting The approach allows rapid prototyping and quick them, and shifts in the significance or dominance testing. of various loops in the structure help contribute However, rapid organizational change and to this understanding. need to adapt to new business models limits At a molecular level, there are two distinct the lifespan of both the databases and software types of causal loop structures, namely balanc- applications. The lifecycles of change to update ing and reinforcing loops. The behavior mode these systems with the organic growth of organi- of a simulation at any given time is determined zations is costly and often requires rearchitecting by the strongest feedback loop(s). The overall the very foundational concepts these software pattern of behavior over time can be related to systems are based on. In UML, the foundation changing relative strengths of feedback loops. model and classes are clear (Booch, 1994). The A system with one balancing and one reinforc- time lag between model concept, use cases, and ing loop produces S-shaped development if the skeleton software code by applying forward and reinforcing loop dominates in the first phase, reverse software engineering to incorporate base and the balancing loop dominates in the second changes forces continual rebuilding, testing, and phase. This form of information representation redeployment of systems. These methodologies is consistent and universal. do not support the representation of information System archetypes are patterns in corporate in such a way that would enable system diagnosis, structure or behavior that recur again and again. analysis, and simulation to assist and to gauge the They are that part of an organization, which rep- “appropriateness” of its real world representation. resents keys to “pattern recognition” activities, If these diagnostic changes are not made, the incorporated in the discipline of system thinking. systems suffer creeping obsolescence, and as a Nine archetypes are generally acknowledged as result, diminishing utility over time. forming the set of tools that reveal patterns of The term archetype is used to denote knowl- behavior in systems (Senge, 1990). edge level models which define valid information structures. A system dynamics archetype can be • Limits to growth (a.k.a. limits to success) defined as a molecular building block of stocks • Shifting the burden and flows for a model structure. An archetype is • Eroding goals therefore an abstraction of a feedback structure • Escalation that is known to generate a particular type of • Success to the successful behavior. It describes a representative unique • Tragedy of the commons behavior, which is the characteristic for that • Fixes that fail thumbnail part of the greater structure within • Growth and under investment the model. It can be considered a “plug and play” • Accidental adversaries module into the structure of any other model where 0 12 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi- global.com/chapter/modeling-system-archetypes/19362 Related Content Cognitive Processes of the Elderly Brain with MindGym Approach Jurij Tasic, Darja Rudan Tasic, Andrej Vovk, Dusan Šuput, Shushma Patel, Dilip Patel, Marjan Gusev and Sasko Ristov (2015). International Journal of Software Science and Computational Intelligence (pp. 18-34). www.irma-international.org/article/cognitive-processes-of-the-elderly-brain-with-mindgym-approach/157435 Cancer Classification From DNA Microarray Using Genetic Algorithms and Case-Based Reasoning Lilybert Machacha and Prabir Bhattacharya (2021). International Journal of Software Science and Computational Intelligence (pp. 17-37). www.irma-international.org/article/cancer-classification-from-dna-microarray-using-genetic-algorithms-and-case-based- reasoning/266226 An Artificial Intelligence-Based Vehicular System Simulator Marvin T. Chan, Jonathan T. Chan, Christine Chan and Craig Gelowitz (2017). International Journal of Software Science and Computational Intelligence (pp. 55-68). www.irma-international.org/article/an-artificial-intelligence-based-vehicular-system-simulator/175655 Hybrid Genetic Algorithm: An Optimization Tool Kedar Nath Das (2014). Global Trends in Intelligent Computing Research and Development (pp. 268-305). www.irma-international.org/chapter/hybrid-genetic-algorithm/97061 Case Technology for Psycho-Diagnostic System Generation Xenia Naidenova (2010). Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models (pp. 337-365). www.irma-international.org/chapter/case-technology-psycho-diagnostic-system/38489.
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