Chapter 2. Literature Review
“The system approach is the modus operandi of dealing with complex systems. It is holistic in scope, creative in manner, and rational in execution. Thus, it is based on looking at a total activity, project, design, or system, rather than considering the efficiency of the component tasks independently. It is innovative, in that rather than seeking modifications of older solutions to similar problems, new problem definitions are sought, new alternative solutions generated, and new measures of evaluation are employed if necessary” (Drew, 1995, pp. 4).
Origins and Fundamental Notions of System Dynamics
System Dynamics (SD) is a policy modeling methodology based on the foundations of decision making, (2) feedback mechanism analysis, and (3) simulation. Decision-making focuses on how actions are to be taken by decision-makers. Feedback deals with the way information generated provides insights into decision-making and effects decision-making in similar cases in the future. Simulation provides decision-makers with a tool to work in a virtual environment where they can view and analyze the effects of their decisions in the future, unlike in a real social system.
Forrester first used the concept of System Dynamics in an article entitled “Industrial Dynamics: A Major Breakthrough for Decision Makers” which appeared in Harvard Business Review in 1958. His initial work focused on analyzing and simulating micro-level industrial systems such as production, distribution, order handling, inventory control, and advertising. Forrester expanded his system dynamics techniques in Principles of Systems in 1968, where he detailed the basic concepts of system dynamics in a more technical form, outlining the mathematical theory of feedback system dynamics (Forrester, 1968).
A stark feature of modern times is continuous change. Changes in existing elements often encounter resistance from people themselves (for whose betterment the changes were sought in the first place). The problems we face today are often too complex and dynamic in nature, i.e., there are many factors and forces in play that we do not comprehend easily. Also, these factors and forces themselves are very dynamic in nature. Systems thinking is advocated by many “thinkers” who advocate holistic thinking and the conceptualization of “systems” wherein “everything is connected to everything else”. Systems Dynamics is an approach whose main purpose is to understand and model complex and dynamic systems. It employs concepts of nonlinear dynamics and feedback control, concepts that will be discussed in detail shortly.
Actions taken on an element in a system result in changes in the state of the element. These, in turn, bring about changes in other linked elements, and the effects may trail back to the “first” element. This is called feedback. Feedbacks are of two types: 1) Positive or self-reinforcing, which amplify the current change in the system; and 2) Negative or self-correcting, which seek balance and provide equilibrium by opposing the change taking place in the system. Complex systems are “complex” because of the multiple feedbacks/interactions among the various components of the system.
The feedback structure of a system generates its behavior. Most dynamics observed in systems fall under three fundamental modes of behavior: exponential growth, goal seeking, and oscillation (Sterman, 2000). These modes of behavior are shown in Figure 2.1. Exponential Growth arises from positive or self-reinforcing feedback. The greater a quantity is, the greater is its net change (increase/decrease), and this is the feedback to the process that further augments the net change. Thus this is a self-reinforcing feedback and there is an exponential growth/decline. Goal seeking behavior arises from negative or self-controlling feedback. Negative feedback loops tend to oppose any changes or deviations in the state of the system; they tend to restore equilibrium and hence are goal seeking. The rate of change diminishes as the goal is approached, such that there is a smooth attainment of the goal/equilibrium state of the system. Oscillation arises due to negative feedback with significant time delays. Corrective action to restore an equilibrium state or to achieve the goal of the system continues even after the equilibrium has been reached due to time delays in identifying the effects of the actions on the system. Thus the goal is overshot. Corrective action taken again (negative feedback loop) leads to undershooting and hence oscillation. The principle that behavior is a result of the structure of the system enables a discovery of the system structure (its feedback loops, non-linear interactions) by observing the behavior of the system. Therefore, when the pattern of behavior is observed, conclusions can be drawn about the dominant feedback mechanisms acting in the system.
Nonlinear interactions among the three major feedback structures give rise to other complex patterns of behavior of the systems (Sterman, 2000). S-shaped growth arises when there is a positive feedback initially, and later negative feedback dominates, leading to attainment of equilibrium by the system. S-shaped growth with overshoot occurs when, after an initial exponential growth phase, negative feedback with time delays kicks in. In this case, the system oscillates around the equilibrium state. Overshoot and collapse occurs as a result of the equilibrium state itself declining after the exponential growth phase has commenced, and negative feedback is triggered. Since the equilibrium declines, a second negative feedback gets activated, wherein the system approaches the new equilibrium state.
Causal Loop Diagrams
The feedback structure of complex systems is qualitatively mapped using causal diagrams. A Causal Loop Diagram (CLD) consists of variables connected by causal links, shown by arrows. Each link has a polarity. A positive (denoted by “+” on the arrow) link implies that if the cause increases (decreases), the effect increases (decreases) above (below) what it would otherwise have been. A negative (denoted by “-” on the arrow) link implies that if the cause increases (decreases), the effect decreases (increases) below (above) what it would otherwise have been (Sterman, 2000).
Causal loops are immensely helpful in eliciting and capturing the mental models of the decision-makers in a qualitative fashion. Interviews and conversations with people who are a part of the system are important sources of quantitative as well as qualitative data required in modeling. Views and information from people involved at different levels of the system are elicited, and from these, the modeler is able to form a causal structure of the system.
Stocks and Flows
Causal loops are used effectively at the start of a modeling project to capture mental models. However, one of the most important limitations of the causal diagrams is their inability to capture the stock and flow structure of systems. Stocks and flows, along with feedback, are the two central concepts of dynamic systems theory. Stocks are accumulations as a result of a difference in input and output flow rates to a process/component in a system. Stocks give the systems inertia and memory, based on which decisions and actions are taken. Stocks also create delays in a system and generate disequilibria (Sterman, 2000).
All stock and flow structures are composed of stocks (represented by rectangles), inflows (represented by arrows pointing into the stock), outflows (represented by arrows pointing out from the stock), valves, and sources and sinks for flows (represented by clouds).
1.1 Introduction to the Problem
1.2 Research Objectives
1.3 Motivation for Research
1.4 Methodology Overview
1.5 Overview of the Results
1.6 Organization of the Thesis
2. Literature Review
2.1 System Dynamics
2.2 Knowledge Elicitation and Group Modeling
2.3 Real World versus Virtual World
2.4 New Technology Implementation: Development and Integration
2.5 Performance Metrics
3. The Model
3.1 Problem Articulation .
3.2 Formulating Dynamic Hypotheses
3.3 Variable Definitions
3.4 The Causal Loop Diagram
3.5 The Quantitative Description: Formulating a Simulation Model
4. Results, Testing, Sensitivity Analysis, Validation and Verification
4.2 Hypotheses Testing: Cost Performance Drivers
4.3 Sensitivity Analysis .
4.4 Testing, Verification, and Validation
5.1 Overview of the Results .
5.2 Verification of the Dynamic Hypotheses
5.3 Policy Suggestions
5.4 Future Issues.
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A System Dynamics Model of the Development of New Technologies for Ship Systems