My Learning Process in Studying the Various Concepts - Essay Example

Course Review Name Institution Date Reflection Introduction My learning process has been tremendous and throughout the course, I have acquired valuable knowledge on various aspects. I have gained a clear understanding on a number of concepts. They include the following; General Systems Theory I was able to learn that the general systems theory (GST) focuses on the structural make up and composition of any system rather than putting emphasis on how the system functions. It describes a level of theoretical model building, between a highly generalized discipline and specific theories of specialized disciplines. At certain extremes, there are separate disciplines and sciences each with their corresponding bodies of theories. These disciplines are characterized by certain varying complexities. However, the general systems theory proposes that even though these systems are complex, it is possible that some of the basis for organization area similar. I came to the understanding that these systems, therefore, can be integrated and modeled using a mathematical perspective. Woolley and Shabman’s (2008) point out that the different phenomenon has a characteristic of being interdependent and interrelated. This occurrence could be physical, cultural, biological, or even social. As such, it can be deduced that the current world characterized by the existence of different organizations, it is possible to integrate the varying properties. For example, since our world consists of an interdependent global community, the criteria for setting ethics that govern human behavior should not only be concentrated on a minority group. There is therefore need for a general system the focuses on the future coming generation and sets up a basis for coexistence despite the differences. Chaordic systems I learnt that a Chaordic system is a framework that focuses on using chaos as an approach to bringing change. Chaordic system incorporates both chaos and order. According Anderson and Suess (2006: 51), a Chaordic system refers to a intricate and dynamical arrangement of connections between fundamentals which make up a unified a unified behavior that is both that cannot be controlled and patterned (orderly), simultaneously. According to The Chaordic Alliance the term “chaordic” means anything simultaneously orderly and chaotic; patterned in a way dominated neither by order nor chaos; and existing in the phase between order and chaos. According to Backström et al. (2002: 12), chaordic systems thinking is characterized by certain factors. Connectivity meaning that a Chaordic system is a Holon: Indeterminacy referring to an unknowable future: Dissipation meaning that learning is important for the purpose of a new experience: and emergence which relates Chaordic systems to being of higher level of coherence and complexity through interaction. For instance in any organization, a Chaordic system develops to maturity in an incremental manner. I learnt that as the growth approaches the highest limit, the system begins to divide into two branches. At this stage it becomes very susceptible to external variations. After hitting the limit growth it either dies or transforms to the next level (Backström et al 2013:29). Fractal and Scale Free Behavior An interest has been growing in the past decade in characterization and understanding of the properties of the complex arrangement. It has been discovered that scale-free action is among the most essential idea for a formidable understanding of the organization in actual systems in the world. I have deduced that this scale-free property has a deep outcome in most aspects of vibrant procedures that take place within the established networks. These include robustness, percolation and synchronization. For example, the available range of scale-free networks, there is no epidemic threshold. Infections that have low spreading rate are likely to prevail in the total population in the existing network. This indicates a radical change from the overall conclusions that are drawn from classical disease modeling. I acknowledge that fact that in irregular shapes of most natural objects including plants and galaxies inhibit the same characteristics and result from the same causal dynamics. Unlike humans, fractal systems are powered by recursive algorithm. This therefore means that they are both smart and dynamic in their different ways. In an organization embracing the use of fractal systems, information is readily available instantly. Fractal systems allow for an unlimited number of users to capture and input data remotely, thus allowing managers to make better decisions. Fractals will normally occur through conversation and language emphasis innovation and creativity, business success and the organization’s culture. Regular patterns of behavior and shapes can produce consistent enterprise such as the 6 Sigma and ISO 9000. When observing fractal, one gets information which remains an equally complex microcosm of the whole. For example, a boss and his or her employee with a consistent behavior create a self-reinforcing fractal while others are positively influenced by this fractal (Vaiman, 2012). Systemic risk and Cascading risk According to ISO 31000, risk is the probability that an uncertainty will occur. Risk has been quantified in relation to the extent of a negative impact that is likely to happen and bring about a loss. However, certain risks would be a source of positive gains for example when an occurrence creates opportunities for stakeholders. Systemic risk refers to the uncertainty that a whole system may collapse rather than some parts of the system breaking down. That is, systemic risks result from connections between risks (‘networked risks’). In the banking sector, systemic risk possesses uncertainty brought about by the inter-connections within the financial systems and may lead to a severe economic tarnish. Huang, et al (2010: 19) viewed systemic risk as the premium required to insure against systemic financial distress. Banks assets are associated with common risk factors, but bank interactions in a default event are otherwise not modeled whereas Hautsch, et al (2012: 36) defined systemic risk beta as the marginal effect of a banks VaR on the VaR of the entire financial system. In essence, these approaches touch upon the source of systemic risk as being systematic risk but without providing explicit channels. In other words, systemic risk measure is a reflection of correlations which could be due to systematic risk and/or interbank linkages. The existing systems are likely to cascade within themselves. Global governance breakdowns, unwarranted trade, acts of terrorism among others are some of the problems that are facing the world. (Nikolaus, 2012). Due to the interdependence amongst most organizations, most systems have become vulnerable. The current challenges include the effects of global warming, outbreaks of killer diseases, food shortages as well as financial crashes across world nations. Risks that our world is already encountering include fiscal and economical crisis, global migration, and a volatile mix of mismatched wellbeing and varied cultures, which are always accompanied with social unrests, international and civil wars, and global terrorism amongst others (Kerson, 2010). Dynamic Systems After the course, I have realized that a dynamic system refers to a recent hypothetical move that is aimed at studying the development. In its current set-up, the theory comes from advances in comprehending intricate and nonlinear systems in both physics and mathematics. Nevertheless, it also sticks to an affluent tradition of systems’ opinion in Biology and psychology. Dynamic system basically means a classification or arrangement of elements that are likely to change over time. It can also refer to a set of mathematical questions which illustrate time-based systems with specific properties. System dynamics is an approach to assessing long term implication of a proposed course of action. According to research, systems dynamics has further proven to be a reliable method that can be implemented in identifying both current and future effects of the current decision. Michael Jackson describes system dynamics as ‘lying in the inter-relationships between the positive and negative feedback loops within which import in the system elements are bound (Nikolaus, 2012). It is essential to note that some minor changes in dynamic systems may lead to small variations in the manner in which interaction occurs. Other changes rather than behaving deterministically could be subjected to random inter actions. Nevertheless, when the interactions between given components become strong, there is a likelihood of a serious change of the functionality of other existing component. (Helbing 2010: 16). Understanding system dynamics is therefore essentials as it enables project managers and team supervisors to be in a position to control the system behavior to be desirable. Conclusion The various concepts that I studied during the course have broadened my understanding and appreciations of facts. I can now articulate issues reasonably regarding the concepts. I anticipate contributing towards sharing the knowledge that I have acquired with others for the benefit of the society. List of References Anderson, M. & Suess O. 2006, “The allure of catastrophe bonds.” International Herald Tribune. July 13, 2006. Backström, T, Eijnatten, F.M. van, & Kira, M., 2002, A complexity perspective on sustainable work systems Routledge Publishers, London. Helbing, D. 2010, Quantitative Sociodynamics: Stochastic Methods and Models of Social Interaction Processes. New York: Library of Congress. Kerson H., 2010, Quantum Field Theory: From Operators to Path Integrals, Wiley VCH, New York Nikolaus H. 2012, Econometrics of Financial High-Frequency Data, Routledge, London. Vaiman M., Bell K., Chen Y., Chowdhury B., Dobson I., Hines P., Papic M., Miller S., & Zhang P., 2012, “Risk assessment of cascading outages: Methodologies and challenges,” Power Systems, IEEE Transactions on, vol. 27, no. 2, pp. 631–641, Woolley, D. & Shabman, L., 2008, Decision-Making Chronology for the Lake Pontchartrain & Vicinity Hurricane Protection Project. Final Report for the Headquarters, U.S. Army Corps of Engineers; Submitted to the Institute for Water Resources of the U.S. Army Corps of Engineers, March 2008. Read More