## Facets of Systems ScienceThis book has a rather strange history. It began in spring 1989, thirteen years after our Systems Science Department at SUNY-Binghamton was established, when I was asked by a group of students in our doctoral program to have a meeting with them. The spokesman of the group, Cliff Joslyn, opened our meeting by stating its purpose. I can closely paraphrase what he said: "We called this meeting to discuss with you, as Chairman of the Department, a fundamental problem with our systems science curriculum. In general, we consider it a good curriculum: we learn a lot of concepts, principles, and methodological tools, mathematical, computational, heu ristic, which are fundamental to understanding and dealing with systems. And, yet, we learn virtually nothing about systems science itself. What is systems science? What are its historical roots? What are its aims? Where does it stand and where is it likely to go? These are pressing questions to us. After all, aren't we supposed to carry the systems science flag after we graduate from this program? We feel that a broad introductory course to systems science is urgently needed in the curriculum. Do you agree with this assessment?" The answer was obvious and, yet, not easy to give: "I agree, of course, but I do not see how the situation could be alleviated in the foreseeable future. |

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So-so, and lacking clarity of content. Also, Kluwer apparently has no formally trained editors on staff, as they miss so many common grammatical errors in all of the publisher's books that it is fast becoming annoying.

### Contents

What Is Systems Science? | 3 |

More about Systems | 9 |

22 More about Relations | 13 |

23 Constructivism versus Realism | 19 |

24 Classification of Systems | 24 |

Exercises | 28 |

Systems Movement | 31 |

32 Systems Thinking | 37 |

Systems Knowledge | 123 |

72 Systems Science Laboratory | 124 |

73 Laws of Systems Science | 125 |

74 Metamethodological Inquiries | 128 |

Complexity | 135 |

82 Complexity and Information | 137 |

83 Bremermanns Computational Limit | 144 |

84 Computational Complexity | 149 |

33 Other Relevant Developments | 47 |

34 TwoDimensional Science | 52 |

Exercises | 54 |

Conceptual Frameworks | 55 |

42 Deductive Approaches | 56 |

43 Inductive Approaches | 61 |

44 Epistemological Categories of Systems | 63 |

45 Epistemological Hierarchy of Systems | 86 |

Exercises | 87 |

Systems Methodology | 89 |

52 General Systems Problem Solver | 93 |

53 Systems Modeling | 95 |

54 Classification of Systems Models | 98 |

55 Systems Modeling in a Broader Sense | 101 |

56 Methodological Role of the Computer | 105 |

Exercises | 106 |

Systems Metamethodology | 109 |

62 Characteristics of Methods | 110 |

63 Methodological Paradigms | 112 |

64 Examples of Methodological Paradigms | 113 |

Exercises | 121 |

Exercises | 157 |

Simplification Strategies | 159 |

A General Formulation | 161 |

93 Special Simplification Strategies | 162 |

Exercises | 168 |

GoalOriented Systems | 171 |

102 Paradigms of GoalOriented Systems | 175 |

103 Adaptive Systems | 177 |

104 Special Types of Goal Orientation | 183 |

Exercises | 190 |

Systems Science in Retrospect and Prospect | 191 |

112 Status and Impact of Systems Science | 197 |

113 The Future of Systems Science | 213 |

Mathematical Terminology and Notation | 219 |

References | 223 |

Classical Systems Literature | 235 |

Introduction and Comments | 237 |

Detailed Contents | 239 |

731 | |

735 | |

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### Common terms and phrases

abstract analysis applicable Ashby autopoiesis autopoietic basic behavior binary relations biological called Cartesian product cell characterized complex systems components concept consider constraints construct cybernetics defined denote described descriptive complexity determine developed differential equations discrete function discrete models discrete-time dynamic systems economic elements entropy environment epistemological equivalence relation evolution example existence fact formal framework fuzzy fuzzy logic given GSTs hierarchy holism human hypothesis important information theory input interaction involved Klir knowledge logic machine mathematical measure mechanics methodology methods natural objects observed organization output overall system paradigm particular phenomena physical possible principle properties relation relevant represent result role scale scientific scientists self-organizing sense Shannon entropy simple simulation social solution specific structure systems subset subsystems symbolic system theory systemhood systems problems Systems Research systems science systems thinking theoretical types values variables York