Systems Science: Theory, Analysis, Modeling, and Design

This book describes a comprehensive approach to applying systems science formally to the deep analysis of a wide variety of complex systems. Detailed 'how-to' examples of the three phases (analysis-modeling-design) of systems science are applied to systems of various types (machines, organic (e.g. ecosystem), and supra-organic (e.g. business organizations and government). The complexity of the global system has reached proportions that seriously challenge our abilities to understand the consequences of our use of technology, modification of natural ecosystems, or even how to govern ourselves. For this reason, complex mathematics is eschewed when simpler structures will suffice, allowing the widest possible audience to apply and benefit from the available tools and concepts of systems science in their own work. The book shows, in detail, how to functionally and structurally deconstruct complex systems using a fundamental language of systems. It shows how to capture the discovered details in a structured knowledge base from which abstract models can be derived for simulation. The knowledge base is also shown to be a basis for generating system design specifications for human-built artifacts, or policy recommendations/policy mechanisms for socio-economic-ecological systems management. The book builds on principles and methods found in the authors' textbook Principles of Systems Science (co-authored with Michael Kalton), but without prerequisites. It will appeal to a broad audience that deals with complex systems every day, from design engineers to economic and ecological systems managers and policymakers.

這本書描述了一種全面的方法，將系統科學正式應用於對各種複雜系統的深入分析。詳細的「如何」示例展示了系統科學的三個階段（分析-建模-設計）應用於不同類型的系統（機器、有機系統（例如生態系統）和超有機系統（例如商業組織和政府））。全球系統的複雜性已經達到了嚴重挑戰我們理解技術使用後果、自然生態系統修改甚至如何治理自己的能力。因此，當簡單結構足夠時，避免使用複雜數學，以便讓最廣泛的受眾能夠應用和受益於系統科學的可用工具和概念。本書詳細介紹了如何使用系統的基本語言功能和結構解構複雜系統。它展示了如何將發現的細節捕捉到結構化的知識庫中，並從中推導出抽象模型以進行模擬。該知識庫還被證明是生成人造物品的系統設計規範或社經生態系統管理的政策建議/政策機制的基礎。本書基於作者與Michael Kalton合著的教科書《系統科學原理》的原則和方法，但無需先修知識。它將吸引廣泛的讀者群體，從設計工程師到經濟和生態系統管理者和政策制定者，他們每天都在處理複雜系統。

George E. Mobus is Associate Professor Emeritus of Computer Science & Systems and Computer Engineering & Systems in the School of Engineering and Technology at the University of Washington Tacoma. He received his PhD in computer science from the University of North Texas in 1994. His dissertation, and subsequent research program at Western Washington University (under National Science Foundation Grant No. IIS-9907102), involved developing autonomous robot agents by emulating natural intelligence as opposed to using some form of artificial intelligence. Mobus was awarded US Patent: #5,504,839, "Processor and Processing Element for Use in a Neural Network". He also received an MBA from San Diego State University in 1983, doing a thesis on the modeling of decision support systems based on the hierarchical cybernetic principles presented in this volume and in numerous papers. His baccalaureate degree was earned at the University of Washington (Seattle) in 1973, in zoology. He studied the energetics of living systems and the interplay between information, evolution, and complexity.

Before completing his academic pursuits of a Ph.D., he had risen through the ranks of a small controls engineering company in Southern California, from software engineer to the top spot. The credit for success goes to the education he got in systems sciences during his MBA program and helping reshape many of the internals of the company that improved profitability and work conditions.

George E. Mobus是華盛頓大學塔科馬分校工程與技術學院的計算機科學與系統、計算機工程與系統的名譽副教授。他於1994年從北德克薩斯大學獲得計算機科學博士學位。他的博士論文以及在西華盛頓大學的後續研究計劃（在國家科學基金會資助下，編號IIS-9907102）涉及通過模擬自然智能而不是使用某種形式的人工智能來開發自主機器人代理。Mobus獲得了美國專利：＃5,504,839，名為“用於神經網絡的處理器和處理元素”。他還於1983年從聖地亞哥州立大學獲得工商管理碩士學位，並在此期間進行了一個關於基於本卷和眾多論文中介紹的分層控制原則的決策支持系統建模的論文。他於1973年在華盛頓大學（西雅圖）獲得動物學學士學位，研究生物系統的能量學以及信息、進化和複雜性之間的相互作用。

在完成博士學位之前，他曾在南加州的一家小型控制工程公司中從軟件工程師晉升到頂級職位。他成功的原因歸功於他在MBA課程中獲得的系統科學教育，並幫助改善了公司的內部結構，提高了盈利能力和工作條件。