Systems Analysis and Modeling

A Macro-to-Micro Approach with Multidisciplinary Applications

Donald W. Boyd

• Publisher: Academic Press
• Year: 2000
• Print ISBN: 9780121218515
• eBook ISBN: 9780080518398
• Topic: Technology & Engineering
• Subtopic: Engineering General
• Index: Technology & Engineering

Systems Analysis and Modeling presents a fresh, new approach to systems analysis and modeling with a systems science flavor that stimulates systems thinking. After introducing systems modeling principles, the ensuing wide selection of examples aptly illustrate that anything which changes over time can be modeled as a system. Each example begins with a knowledge base that displays relevant information obtained from systems analysis. The diversity of examples clearly establishes a new protocol for synthesizing systems models. - Macro-to-micro, top-down approach - Multidisciplinary examples - Incorporation of human knowledge to synthesise a systems model - Clear and concise systems delimitation - Complex systems using simple mathematics - "Exact" reproduction of historical data plus model generated secondary data - Systems simulation via systems models

Part I

Introduction to Analysis and Modeling

CHAPTER 1

Systems Analysis and Model Synthesis

1.1 INTRODUCTION

Two or more components comprise a system if they interact within a common domain to achieve a common goal or function. In general, a system is not isolated but rather is linked to adjacent systems through its inputs and outputs. Consequently, systems are dynamic, adjusting internally in response to external disturbances. A systems model consists of two or more interacting components. An effective systems model is capable of linking with models of adjacent systems. To possess such specific properties, a model must be designed, following a procedure that in many ways resembles engineering design. Engineering design depends on a growing body of knowledge. Knowledge begins with engineering education and continues to grow through practical experience. Usually, engineering design involves the following elements:

• Analysis Design object is differentiated into subcomponents and their interrelationships. Specifications are formulated.

• Synthesis Alternative systems designs are considered. Subcomponents are integrated into a functional whole to produce tentative designs.

• Testing Design specifications are verified for the chosen alternative, performance is monitored for validity, and functionality is checked for credibility with the user.

• Implementation Design object is installed and becomes operational for the user.

Recycling to previous steps generates new alternatives and leads to improvements in the design.

Similarly, model design depends on a growing body of knowledge. Each system domain is characterized by knowledge items. Knowledge items are acquired through study (analysis) of the system. Relevant items are synthesized into a systems model. Even a cursory study reveals that systems are characterized by triads. For example, this paragraph just introduced a systems triad of knowledge acquisition, systems analysis, and model synthesis. Furthermore, knowledge acquisition is a process that involves three entities, a general information triad composed of source, medium, and message. Here are some examples of information triads:

• Textbook author, paper, “theme”

• Tracks animal, snow, “a bear passed this way yesterday”

• Fossil record prehistoric being, bone, “a tyrannosaur lived here 100 million years ago”

• Physical universe designer, design, “axioms of existence”

• Knowledge base Table 1.1 displays a triad of information triads.

Table 1.1

Information Triads of the Knowledge Base

Source	Medium	Message
scientist	scientific inquiry	theory
statistician	statistical observation	statistical data
domain expert	interrogation	heuristics

Knowledge items are expressed in the message column, and are classified as hard or soft. For hard knowledge to be relevant, the message must consist of true facts that are applicable. Whether from scientific inquiry or statistical observation, a fact must be treated as a binary variable, a hypothesis to be tested. Facts can be established as true or false because they have the property of being observable by objective application of the physical senses. On the other hand, soft knowledge is subjective, not open to observation, and consists of heuristics: experience and rules of thumb. Knowledge items may be so...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• Part I: Introduction to Analysis and Modeling
• Part II: Deterministic Models
• Part III: Stochastic Models
• Glossary
• References
• Index