Figure 2. Systems Thinking versus Traditional Views The space element is often easier to grasp than the time element.
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But systems thinking requires that we ask: What circumstances and attitudes led to this point? What actions and behavior patterns led to this point? What are the likely attitudes, actions, and patterns going forward? What are the probable reactions of my: allies, enemies, competitors, neutral 3 rd parties, and the environment? Systems Thinking thus requires a vision of the future as well as an understanding of the past.
Systems thinking acknowledges that systems are dynamic, and has evolved from the field of General Systems Theory Bertalanffy. Systems are constantly subject to various forces and feedback mechanisms, some of which are stabilizing and some of which are reinforcing or de-stabilizing. This behavior is often counter-intuitive. System dynamics and system dynamics modeling are used to help understand the behavior of systems over time, to identify the driving variables so that system behavior may be positively impacted, and to help predict future states. It is important to note that systems thinking does not supplant either statistical or reductionist analytic thinking; it complements them, as shown in Figure 3: Figure 3.
All three approaches provide different but complementary perspectives on gaining more insight into and understanding of the behavior of a system. Systems Thinking requires that we recognize that in human-designed systems, repeated events or patterns derive from systemic structures which, in turn, derive from mental models. This is clearly depicted in the Iceberg Model Figure 4 , which is a core element of systems thinking: Figure 4. The Iceberg Model The Iceberg Model argues that events and patterns which we can observe are caused by systemic structures and mental models, which are often hidden.
Systemic structures are the organizational hierarchy; social hierarchy; interrelationships; rules and procedures; authorities and approval levels; process flows and routes; incentives, compensation, goals, and metrics; attitudes; reactions and the incentives and fears that cause them; corporate culture; feedback loops and delays in the system dynamics; and underlying forces that exist in an organization. Behaviors derive from these structures, which are in turn established due to mental models or paradigms.
In order to understand behaviors, we must first identify and then understand the systemic structures and underlying mental models that cause them. At this point, the Iceberg Model must be modified to distinguish natural systems from human-designed systems Figure 5 : Figure 5. The Iceberg Model Applied to Natural versus Human-Designed Systems In natural systems, the structures are always self-organized, while in human-designed systems the structures may be either self-organized or designed. But what is self-organization? In other words, the pattern is an emergent property of the system, rather than a property imposed on the system by an external influence.
This concept has significant implications for the origin of life and of the universe itself. Emergent properties are properties of the system as a whole rather than properties that can be derived from the properties of its components. Emergent properties are a consequence of the relationships among system components—they can therefore only be assessed and measured once the components have been integrated into a system. This means that one cannot address emergent properties using reductionist thinking.
Examples of emergence in human-designed systems include the meaning of words, traffic jam patterns, reliability, security, usability, countries, and the power of religion to influence behavior. The relationships among system components and the behaviors and patterns deriving from those relationships are additional key elements of systems thinking.
Literature Commonalities. With respect to perspective, then, most system thinking sources agree that systems thinking is the opposite of linear thinking; that it focuses on relationships versus components, and integration versus dissection; that it recognizes and addresses the dynamic nature of systems and that system feedback loops are essential to understanding system dynamic behavior; that systems exhibit self-organization and emergent properties; and that systems thinking has great power in analyzing, understanding, and influencing complex business, socio-economic, and natural problems and behaviors.
Literature Disparities. From the systems thinking literature, however, it also seems that there exist two general schools of thought or common themes regarding systems thinking: one school focuses on the Iceberg Model and on the patterns and events that are caused by systemic structures and mental models. This school sees system dynamics as a fundamental element of systems thinking, but does not equate it to systems thinking. The other school focuses on the inter-relationships among system components, the dynamic behaviors that arise therefrom, and system dynamics modeling, and tends to equate systems thinking with system dynamics, but does not embrace the Iceberg Model.
We believe that both the Iceberg Model and system dynamics are fundamental to systems thinking. Those structures are the causative factors behind patterns and events. Thus the Iceberg Model represents a broader context and demonstrates how the underlying structures impact our daily lives in observable ways. It goes beyond dynamics and considers the psychology behind structure. For example, a systems thinking analyst may attack a complex problem by first constructing a causal loop diagram, and then translating it into a stock-an-flow diagram, and eventually into a dynamic model using iThink or similar software.
The Integrated Model.
Complete systems thinking thus integrates concepts from the Iceberg Model and concepts from causal loop diagrams and dynamic modeling into an overarching framework. This integrated model is depicted in Figure 6. Figure 6. The Iceberg Model introduces some of the key language of systems thinking: events, patterns, systemic structures, and mental models. Other key words include self-organization, emergence, feedback, system dynamics, and unintended consequences. A concise summary of systems thinking terms is provided here some definitions are taken from Kim and are included here with the kind permission of Leverage Networks, Inc.
In modeling software, a stock is often used as a generic symbol for accumulators. An accumulator is also known as a Stock or Level. Balancing processes seek equilibrium: They try to bring things to a desired state and keep them there. They also limit and constrain change generated by reinforcing processes. A balancing loop in a causal loop diagram depicts a balancing process. Emergent properties are a consequence of the relationships among system components. Examples include the flocking behaviour or murmuration of birds, the schooling of fish, the shape of an apple, traffic jam patterns, the concept of countries, and the ability of religion to influence behaviour.
For example, annual performance reviews return information to an employee about the quality of his or her work. Examples are the amount of water that flows out of a bathtub each minute, or the amount of interest earned in a savings account each month, which are also called rates. In systems, hierarchies often evolve from the bottom to the top; stable levels of the hierarchy provide system stability and resilience. Hierarchies also facilitate the evolution of simple systems into complex systems. Patterns may be physical, behavioral, or mental.
Patterns are usually caused by underlying systemic structures and forces. Reinforcing processes compound change in one direction with even more change in that same direction. As such, they generate both growth and collapse. A reinforcing loop in a causal loop diagram depicts a reinforcing process, which is also known as a vicious cycle or a virtuous cycle. Good examples include the tendency of a free market system to organize into buyers, sellers, traders, and bankers, and the tendency of geese to organize into a V-formation.
The structure of an organization, for example, could include not only the organizational chart but also information flows, interpersonal interactions and relationships, rules and procedures, authorities and approval levels, process flows, routes, attitudes, reactions and the incentives and fears that cause them, corporate culture, and feedback loops. There are both human-designed systems which serve a specific purpose and natural systems such as the solar system which may not have a specific purpose or whose purpose is unknown to us.
Examples include the negative impact of DDT on the environment, the dramatic increase in organized crime as a result of prohibition, the over-use of antibiotics resulting in antibiotic-resistant bacteria, and the devastation caused by gypsy moths, which were original imported to the United States as a cheaper source of silk.
Systems Thinking Tools. There are many systems thinking tools, but not all of them are fundamental or integral to the practice of systems thinking. To identify those that are fundamental, we have established the following criteria: 1. The tool must be widely applicable to most systems, not to a narrow sub-category of systems 2. It must be described in the systems thinking literature 3. The tool must be easy to use and understand without extensive training 4. It must address at least one of the concepts described above under the definition of systems thinking 5. In systems thinking, archetypes are problem-causing structures that are repeated in many situations, environments, and organizations.
Being facile at identifying them is the first step in changing the destructive structure. These 10 archetypes are very common in business situations, and the literature presents many suggestions for dealing with them. The key is to first identify them. Behavior Over Time graphs plot the values of pertinent system variables over time. They are often useful first steps in developing an understanding of systemic behavior and of how variables inter-relate. Causal Loops with Feedback and D elays. System behavior is usually determined by the presence of reinforcing and balancing processes. These are sometimes obvious such as the reinforcing process of compound interest and sometimes not as in the stabilizing impact of terrorism on international collaboration.
In either case, drawing causal loop diagrams helps to see the interrelationships among all system components. These can become quite complicated as cause-and-effect relationships, many of which are hidden or at least hard to see , are identified. But one of the first steps in attempting to understand system behavior is the construction of a causal loop diagram.
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Kim and Meadows both present good examples and explanations. An example of a very simple temperature control causal loop diagram is shown in Figure 7: Figure 7. They may also be non-physical things such as emotions: love, greed, angst, or lust. In systems, these quantities of things are called stocks. Stocks may increase or decrease due to flows into or out of them. Stock and flow diagrams show the stocks, inflows, and outflows. They are often developed in conjunction with causal loop diagrams, and they are important precursors to system dynamics modeling.
Stock and flow diagrams, like causal loop diagrams, are invaluable in understanding system behavior, and Bellinger provides a method for translating causal loop diagrams into stock-and-flow diagrams. In addition, Goodman, Kemeny, and Roberts provide a detailed description of the language of loops and links. A simple stock-and-flow diagram depicting logging impact on a forest from Meadows is shown in Figure 8: Figure 8. Some stock-and-flow infrastructures are repeated frequently in business and scientific systems. Primarily used for system dynamics modeling, these main chains are described well in Richmond and provide a head-start for anyone attempting to model system dynamic behavior.
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An example of a manufacturing main chain infrastructure from Richmond is shown in Figure 9: Figure 9. They typically read from the upper left to the lower right, communicating thereby the chief message of the text. Per Boardman, the diagram is a network comprising nodes, links, flows, inputs, outputs, beginning, and end, and it must fit on a single page although that page may be quite large. Colors may be used to indicate similar or linked concepts or transformations, or to draw attention to key elements.
One can see that although systemigrams contain elements of causal loop diagrams, they are substantially more than that and their main thrust is not feedback loops, but rather telling a story. Although systemigrams are very useful in understanding existing systems, there have been recent attempts Cloutier et al. Details of systemigrams and more examples may be found in both Sauser, and Boardman and Sauser. Figure System Dynamics is the study and analysis of system behavior over time feedback loops, time delays, non-linear behavior.
System dynamics was originally developed in the late s by Jay W. It is difficult to understand a system without understanding its behavior over time, which is often non-intuitive. Modeling also helps identify control points and how one can influence the system. With so many I. As stated above I believe all these others are simply views or auxiliary views of the Zachman Framework.
These are not better or worse than each other, but specific perspectives needed to illuminate a specific item of interest, similar to such in the drafting domain. You must be logged in to post a comment. Some integration examples. Analytical models for performance, physical, and other quality characteristics, such as reliability, may be employed to determine the required values for specific component properties to satisfy the system requirements. An executable system model that represents the interaction of the system components may be used to validate that the component requirements can satisfy the system behavioral requirements.
The descriptive, analytical, and executable system model each represent different facets of the same system. The component designs must satisfy the component requirements that are specified by the system models. As a result, the component design and analysis models must have some level of integration to ensure that the design model is traceable to the requirements model. The different design disciplines for electrical, mechanical, and software each create their own models representing different facets of the same system.
It is evident that the different models must be sufficiently integrated to ensure a cohesive system solution.
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To support the integration, the models must establish semantic interoperability to ensure that a construct in one model has the same meaning as a corresponding construct in another model. This information must also be exchanged between modeling tools. One approach to semantic interoperability is to use model transformations between different models.
Transformations are defined which establish correspondence between the concepts in one model and the concepts in another.
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In addition to establishing correspondence, the tools must have a means to exchange the model data and share the transformation information. There are multiple means for exchanging data between tools, including file exchange, use of application program interfaces API , and a shared repository. The use of modeling standards for modeling languages, model transformations, and data exchange is an important enabler of integration across modeling domains. Barry, P. Koehler, and B.
Agent-Directed Simulation for Systems Engineering. January Wymore, A. Law, A. Simulation Modeling and Analysis , 4th ed. Model-Based Systems Engineering. Estefan, J. Hybertson, D. Systems Engineering Vision September Rouquette, N. Jump to: navigation , search. Types of Models. Navigation menu Personal tools Log in. Namespaces Page Discussion.