Views on General Systems Theory

MMK

TRITA-MMK 2005:10ISSN 1400-1179

ISRN/KTH/MMK/R-05/10-SE

Views on General System Theory

by

Ola Larses and Jad El-khoury

Stockholm

2005

Technical Report

Mechatronics Lab, Department of Machine Design Royal Institute of Technology, KTH

S-100 44 STOCKHOLM

MMK

Technical Report TRITA-MMK 2005:10

ISSN 1400-1179 ISRN/KTH/MMK/R-05/10-SE

Views on General System Theory

Machine Design KTH

Mechatronics Lab

April 2005

Ola Larses and Jad El-khoury

{olal;[email protected]}

Abstract

This report is the result of a literature study course work on the General System Theory (GST) performed by Jad Elkhoury and Ola Larses at the Mechatronics Division of the Department of Machine Design at the Royal Institute of Technology (KTH). The study was initially performed in the fall of 2004 and concluded in the spring 2005.

The structure of the report consists of two major parts. The first part provides a general overview of this broad field of science. It gives a short summary and overview of the General System Theory is, as well as a reflection on how GST relates to current meta-modelling efforts exemplified with the UML-MOF. The second major part of the report is a set of four book reviews covering very different books about the area: One original work of von Bertalanffy (1967), two books from authors providing their views on GST (Weinberg 2001, Checkland 1999) and one text book covering several theories on the subject (Skyttner 2001).

Keywords GST, Cybernetics, General System Theory, Skyttner, Bertalanffy, Checkland, Weinberg

Ola Larses and Jad El-khoury Views on General System Theory

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Contents

1 INTRODUCTION ....................................................................................................................................... 5 2 A SUMMARY OF THE FIELD OF GENERAL SYSTEM THEORY (GST)....................................... 5

2.1 HISTORY OF GENERAL SYSTEMS THEORY (GST) ................................................................................. 5 2.1.1 Current references, communities and courses ................................................................................ 7

2.2 CONCEPTS OF GST ............................................................................................................................... 9 2.3 A GENERAL SYSTEM MODEL - RELATING THE CONCEPTS OF GST ..................................................... 11

2.3.1 A Criticism of Bertalanffy ............................................................................................................. 12 2.3.2 The Analysis-Synthesis system method.......................................................................................... 12 2.3.3 The goal orientation of systems..................................................................................................... 13

3 GST AND THE MOF................................................................................................................................ 14 4 REFERENCES .......................................................................................................................................... 16 5 APPENDIX – BOOK SUMMARIES....................................................................................................... 17

5.1 BOOK REVIEWS................................................................................................................................... 17 5.1.1 General Systems Theory – Lars Skyttner (2001) ........................................................................... 18 5.1.2 General System Theory – Ludwig Von Bertalanffy (1968)............................................................ 21 5.1.3 Systems Thinking, Systems Practice – Peter Checkland (1981/1999)........................................... 24

5.2 EXTENDED BOOK REVIEW: AN INTRODUCTION TO GENERAL SYSTEMS THINKING – GERALD M. WEINBERG ........................................................................................................................................................ 28

5.2.1 The Problem.................................................................................................................................. 28 5.2.2 The Approach ................................................................................................................................ 30 5.2.3 System and Illusion........................................................................................................................ 33 5.2.4 Interpreting Observations ............................................................................................................. 37 5.2.5 Breaking down Observations ........................................................................................................ 41 5.2.6 Describing Behaviour ................................................................................................................... 45 5.2.7 Some Systems Questions................................................................................................................ 47 5.2.8 Further readings ........................................................................................................................... 48


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1 Introduction This report is the result of a literature study course work on the General System Theory (GST) performed by Jad Elkhoury and Ola Larses at the Mechatronics Division of the Department of Machine Design at the Royal Institute of Technology (KTH). The study was initially performed in the fall of 2004 and concluded in the spring 2005.

The structure of the report consists of two major parts. The first part provides a general overview of this broad field of science. It gives a short summary and overview of the General System Theory is, as well as a reflection on how GST relates to current meta-modelling efforts exemplified with the UML-MOF. The second major part of the report is a set of four book reviews covering very different books about the area: One original work of von Bertalanffy (1967), two books from authors providing their views on GST (Weinberg 2001, Checkland 1999) and one text book covering several theories on the subject (Skyttner 2001).

2 A summary of the field of general system theory (GST) This summary first gives a historical reference to the origins of GST and an overview of some GST related communities today. Then, the basic concepts of the theory are given followed by a discussion of how the concepts relate, and what the core of the theory actually is.

2.1 History of General Systems Theory (GST) To understand the history and aspects of system related activities a good start is to provide a classification framework. Checkland (1999) provides a map of seven enumerated sub-activities in the systems movement, shown in figure 1.

Figure 1 A map of the Systems Movement activities (Checkland 1999)

System ideas have influenced several disciplines such as biology and economics as indicated by box 2.2. However, the focus in this report is on the study of systems as such (box 2.1), specifically the branch of theoretical development (box 3.1). The general systems theory (GST) was established as a field of research in the 50’s. The most commonly referred father

1 The Systems Movement

2.2 Application of systems thinking in other disciplines

2.1 Study of systems as such

3.2 Problem-solving application of systems thinking 3.1 Theoretical development of systems thinking (formulation of GST)

4.1 Hard systems thinking – Systems engineering

4.2 Decision making systems

4.3 Soft systems thinking


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of GST is Ludwig von Bertalanffy, there are however a range of contemporary scientists who contributed in the field. GST have strong bonds to Cybernetics, Information theory and Control theory.

Bertalanffy founded the “Society for the Advancement of General Systems Theory” together with Kenneth Boulding in 1954. The founders emphasized their desire to promote the unity of science at the very first meeting which took place in December, 1954 in Berkeley, California. In 1956 the organization was renewed as the “Society for General Systems Research”, with the name later changing to the “International Society for General Systems Research”. The organization is today known as the “International Society for the Systems Sciences” (ISSS) and celebrated their fiftieth anniversary in 2004.

A related organization is the International Federation for Systems Research (IFSR) also established through the Society for General Systems Research in 1980 together with the Österreichische Studiengesellschaft für Kybernetik, and the Systeemgroep Nederland.

The more applied problem oriented systems thinking (box 3.2) is given three branches by Checkland (shown in figure 1). However, in the introductory retrospective section of his book he is content with separating hard and soft systems thinking. “Hard” methods like systems engineering assumes that the system have a clear purpose and is optimized towards this purpose, they are goal-oriented. This assumption holds for human-made systems in general but breaks down for human activity systems, and also for some human-made systems where there exist conflicting goals and purposes. For these problems Checkland proposes a “soft” methodology.

The soft systems thinking (SST, box 4.3) is the approach developed by Checkland himself. In this problem oriented approach model building (capturing abstract activities and issues) is in focus. The actual details of the model are of less interest. According to Checkland SST should be used iteratively, situation-driven and in interaction with the system, not sequential, methodology-driven and intervening with the system (as hard systems thinking). SST is related to social sciences. The target for Checkland is mainly human activity systems and social systems but the ideas seem relevant also for the engineering of complex technical systems with conflicting requirements. The basic ideas of SST are summarized by Flood (2000).

The hard systems thinking approach (box 4.1) is known as systems engineering and tries to arrange and describe the real world in a systemic manner in order to be able to perform proper engineering. The exact details of the model describing the system are in focus. This branch also has old roots. The first significant systems engineering was performed for telephone systems to ensure that all the different parts of the phone system interoperated reliably. The term systems engineering dates back to Bell Telephone Laboratories in the early 1940s, Bell labs performed major applications of systems engineering during World War II. The first attempt to teach systems engineering as we know it today came in 1950 at MIT by Mr. Gilman, Director of Systems Engineering at Bell. (Buede 2000) Today, systems engineering is promoted by the International Council on Systems Engineering (INCOSE) formed in 1990.

System theories are still a topic of high interest discussed in academia. Some discussions still focus on the development of GST such as the journal of Systems Research and Behavioural Science. Other communities have adopted the application of systems thinking, and still others develop systems engineering, such as the rapidly growing INCOSE. A mapping of the mentioned organizations and journals to the framework of Checkland is provided in figure 2.


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Figure 2 Some active players in the systems world

2.1.1 Current references, communities and courses Looking for the current activities in the systems movement a few communities with immediate references to GST can be found. Also, there are several web-based sources of information regarding the subject. Looking at the different communities and their varying angles on the subject shows the dispersed body of theory and the lack of consensus. A selection of web-sites related to GST is provided in table 1. The links were accessed April 2005.

Table 1 General Systems Resources

Site name Organization Link Brief descriptions from the sites

Principa Cybernetica Web

Principa Cybernetica Project

http://pespmc1.vub.ac.be

The project has started in 1989, and its first implementation as a website happened in 1993. Since then, the webpages discussing the different components of our philosophy have been regularly expanded and updated. Thus, our conceptual system gradually widens, deepens, and improves. Of course, the task is enormous, and will never be really finished. If you are interested in our Project, we invite you to join our efforts and become a contributor.

-homepage- International Society for Systems Sciences (ISSS)

http://www.isss.org

The ISSS is a broadly based professional society of scientists, philosophers, educators, futurists, humanists, business and policy practicioners, artists, writers, and many other professionals from diverse endeavors, who are drawn together by a common interest: understanding and interacting systemically with reality.

The Systems Movement

Hard Systems Thinking

Soft Systems Thinking

Problem solving approaches (application of system theories)

Development of system theories

Formulation of GST Cybernetics Information Theory Contemporary Organizations: International Society for the Systems Sciences (ISSS) International Federation for Systems Research (IFSR) Journals: Systems research and Behavioural Science (Wiley)

Soft Systems Modelling Social sciences Peter Checkland

Systems Engineering Contemporary Organizations: International Council on Systems Engineering (INCOSE) Journals: Systems Engineering


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Site name Organization Link Brief descriptions from the sites

-homepage- American Society for Cybernetics

http://www.asc-cybernetics.org/

The American Society for Cybernetics was founded in 1964 by a group of people in Washington, DC who were interested in the then new field of cybernetics. The founding members of the Society wanted to follow and to encourage the development of this interdisciplinary field. The Society now holds an annual conference, conducts seminars on the fundamentals of cybernetics, manage a listserve, and maintains contacts with cyberneticians in other countries.

-homepage- International Federation for Systems Research

http://www.ifsr.org/

The International Federation For Systems Research (IFSR), founded 1981, is a a non-profit, scientific and educational agency, constituted of member organizations from various countries. The overall purpose of the Federation is to advance cybernetic and systems research and systems applications and to serve the international systems community.

Project: A General Systems Theory

Harward Law http://h2o.law.harvard.edu/ViewProjectSyllabus.do?projectId=358

This course is a beta test for a subsequent course to be arranged for February 2006. Considerable arrangements need to be made first (with student input necessary). Pending that, the course text is von Bertalanffy, Ludwig (1969). General System Theory, New York: George Braziller. (Chapter 2 in General System Theory is the major read.) Editor comment: The page provides a good overview of several communities, journals and other GST resources.

Also, courses are held at different deparments across universities throughout the world. To provide a broad backround a selection of courses are referenced in table 2. The links have been accessed April 2005.

Table 2 General Systems Courses

Course name Cts Department University Brief description Generell Systemteori

5p 7,5p ECTS

Matematiska och systemtekniska institutionen

Växjö Universitet

Syftet med kursen är att de studerande ska: • kunna använda ett generellt systemteorietiskt synsätt vid analyser av verksamheter och design av informationssystem • känna till centrala begrepp inom systemteorin • känna till olika systemklassifikationer • kunna förstå och analysera konsekvenserna av att använda olika systemindelningar. http://w3.msi.vxu.se/~per/IVC742/IVC742.html

General System Theory

4p Business, Law & Information Sciences

University of Canberra

This subject explores a general framework for understanding diverse kinds of systems, including 'hard' systems such as those found in engineering applications, and 'soft' systems in which the structure of the system is less well defined and generally involving some form of human activity. The practical application of systems theory is in advanced problem-solving in systems. The main question which will be pursued during the semester is whether the techniques and ideas of so-called 'action research' - a way of finding out about the world first described by Lewin and refined and diversified by a number of authors and researchers including Checkland. http://www.canberra.edu.au/courses/index.cfm?action=detail&subjectid=004601&year=2004

System Theory - Department of Management

New Mexico State University

In this course, we will contrast three schools of systems theory: the naïve US school of systems theory, the General Systems Theory School, and the Language School of Systems Theory. http://cbae.nmsu.edu/~dboje/655/655_overview.htm

http://w3.msi.vxu.se/~per/IVC742/IVC742.html

http://www.canberra.edu.au/courses/index.cfm?action=detail&subjectid=004601&year=2004

http://www.canberra.edu.au/courses/index.cfm?action=detail&subjectid=004601&year=2004

http://cbae.nmsu.edu/~dboje/655/655_overview.htm


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Course name Cts Department University Brief description Systems Theory

1p Department of Forest Science

Oregon State University

The course is inspired by the increasing importance of interdisciplinary perspectives, especially as they relate to emerging issues such as ecosystem management and global environmental change. The goal is to explore some of the old and new ideas in this field and their possible application in basic and applied research. http://www.cof.orst.edu/cof/fs/gradprog/courses/turner/fs507-02.htm

Seminar in General Systems Theory and Strategic Modeling

- Information Systems and Decision Sciences Department

Ourso College Of Business Administration

This course is designed to expand each student's ability to analyze, understand and to conceptually model complex systems. The modern manager faces exceedingly complex systems that cannot be adequately analyzed using formal mathematical decision-making, intuiative or experiential techniques. In this course, the system dynamics approach to examining, conceptualizing and modeling macro systems is emphasized. The informational feedback characteristics of living systems are studied and methods to evaluate the productivity and effectiveness of industrial and governmental systems are addressed. Special attention is given to the dynamic interaction between system structure, policy, time delays and information structures in decisions and actions that determine system behavior and performance. Attention is given to the implications for information management that are derived from the general systems analysis process. http://emac3.ocs.lsu.edu/kathy/cct_courses/isds7920.html

General System Theory

5p - Czech Technical University in Prague

In this class students will learn the general, broadly founded methodology of Systems Theory to gain a sufficient insight in general systems principles and their theoretical limits. The course aims a wrapping up knowledge from other special classes and giving a common frame for many special engineering problems encountered in practice. It is dealt with such concepts like identification, decomposition and self-organization. http://web.cvut.cz/ctu/international/web/en/prospectus/normal/f300/subjXE33OTS.html

2.2 Concepts of GST Table 3 lists a collection of the key terms & concepts found in the General System Theory communities. This table is mainly borrowed from a review of Gillies (1982), with a few additional concepts. Since no consensus among the communities exists about GST, different communities focus on various concepts in this list.

Table 3 General System Concepts

Term Definition Examples

Input The energy & raw material transformed by the system

Information, money, energy, time, individual effort, & raw material of some kind

Throughput

The processes used by the system to convert raw materials or energy from the environment into products that are usable by either the system itself or the environment.

Thinking, planning, decision-making, constructing, sorting, sharing information, meeting in groups, discussing, melting, shaping, hammering, etc.

Output

The product or service which results from the system's throughput or processing of technical, social, financial & human input.

Software programs, documents, decisions, laws, rules, money, assistance, cars, clothing, bills, etc.

http://www.cof.orst.edu/cof/fs/gradprog/courses/turner/fs507-02.htm

http://www.cof.orst.edu/cof/fs/gradprog/courses/turner/fs507-02.htm

http://emac3.ocs.lsu.edu/kathy/cct_courses/isds7920.html

http://web.cvut.cz/ctu/international/web/en/prospectus/normal/f300/subjXE33OTS.html

http://web.cvut.cz/ctu/international/web/en/prospectus/normal/f300/subjXE33OTS.html


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Feedback

Information about some aspect of data or energy processing that can be used to evaluate & monitor the system & to guide it to more effective performance.

How many cars were produced? How many had to be recalled to correct errors? How many mistakes were made? Why were mistakes made? HealthCareReportCard.com is an example of how hospitals are doing with certain diagnoses. Accreditation reports are an example as are patient satisfaction surveys, sales reports, and test results.

Subsystem

A system which is a part of a larger system. They can work parallel to each other or in a series with each other.

The finance department, the information system, the managerial system, the renal system, the political system, the workflow system (such as the conveyor belt), etc.

Static system

Neither system elements nor the system itself changes much over time in relation to the environment

A rock

Dynamic system

The system constantly changes the environment & is changed by the environment

A healthy young adult grows more independent, interdependent, & self-sufficient & self-directed in response to stimuli from peers, family, school, work, & recreational activities.

Closed systems

Fixed, automatic relationships among system components & no give or take with the environment

A rock is an example of the most closed system. We may encounter families that are isolated from the community & resistant to any outside influence.

Open systems

Interacts with the environment trading energy & raw materials for goods & services produced by the system. They are self-regulating, & capable of growth, development & adaptation.

Hospitals, families, people, body systems, banks, manufacturing plants, governmental bodies, associations, businesses, etc.

Boundary

The line or point where a system or subsystem can be differentiated from its environment or from other subsystems. Can be rigid or permeable or some point in between. Systems or subsystems will engage in boundary tending.

The nursing unit, the occupational therapy department, the elementary school, a person, an agency or business, a fence or wall, roles, ect

Goal

The overall purpose for existence or the desired outcomes. The reason for being. Currently, many organizations put their goals into a mission statement.

To educate students, to support people during illness & restore them to health, to make money, to create social order, etc.

Entropy The tendency for a system to develop order & energy over time.

Rules are made, policies & protocols are written, approved & communicated to staff; laws are enacted & violators are held accountable; a marathon runner in training gradually is able to run farther.

Negentropy The tendency of a system to lose energy & dissolve into chaos

The disorganization after a hurricane, a rigid, frightened family produces a child who is unable to think independently or leave home, a new business has no forms or protocols for handling consumer complaints.

Control or cybernation

The activities & processes used to evaluate input, throughput & output in order to make corrections

Pilots use instrument panels & devices to constantly evaluate & make course corrections; teachers grade papers & give students grades on exams; parents measure their children's height & weight & may adjust the child's diet; health care agencies use TQM or Quality Assurance programs; employee health nurses review records to see who needs immunization updates.


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Equifinality Objectives can be achieved with varying inputs & in different ways.

A nursing assistant assigned to empty catheter bags on a unit could begin in the middle of the hall, on the right side, on the left side, at the front or back of the hall & still end up with all the bags emptied. A traveller could take the interstate or back country roads & still arrive at their destination. The traveller could go by train, plane, bus or car & still arrive at desired location.

System

A set of objects together with relationships between the objects and between their attributes. The objects set (parts, elements, attribures, etc) are the undefined primitives of systems thinking.

See subsystem

Observer

An observer makes observations such as sensations on the sense organs, readings from instruments, etc. An observation is the act of choosing an element from a set of possible observations of that type for that observer. The objects defining a system come from the mind of an observer, and so a system is relative to the point of view of an observer.

The programmer, user, owner or maintainer of an information system.

Black box

A model of a system in which the system can only be known through observing its behaviour, without looking inside of it. The observer can however decide on the scope and range of observation, based on what is believed to be the important features of the system.

An astronomer studying the universe.

White box An approach to understanding the system in which the inside of the system is revealed.

Analyzing a electronic circuit by understanding the workings of its internal components such as resistors, transistors, etc.

State A particular situation of the system that the observer can recognize if it occurs again.

The on/off states of a lamp

Quality (property) A way of grouping states of a system The quality of mass defined by the states in which masses

are the same or different.

2.3 A General System Model - Relating the concepts of GST At the core of GST is the system definition, providing a meta-model of the world. With a proper meta-model a foundation for knowledge creation is provided. Unfortunately there is no common definition of systems and every other author (including ourselves) adds a new definition. However, in the literature of GST there are some core ideas that are repeated and seem to be established in the theory. The ontology of any system theory contains three principal constituents: unity, parts and relationships. These ideas can be used as a core of a system model describing a system, enabling analysis and synthesis of systems.

Further, it is commonly recognized that systems perform a transformation process and may have inputs and outputs. These concepts apply for both physical as well as for abstract systems. Also, each of the parts may be seen as a system of their own. The system concept can be applied recursively at any level of aggregation.


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2.3.1 A Criticism of Bertalanffy Beginning with the view of Bertalanffy, focus is placed on the properties of the whole. Bertalanffy claims that there is no such thing as emergent properties of systems. The whole is more than the sum of its parts, but equal to the sum of its parts and the relations between the parts. (Guberman 2002) “If, however, we know the total of parts contained in a system and the relations between them, the behavior of the system may be derived from the behavior of the parts.” (Bertalanffy 1969)

Figure 3 Ontological picture of system in GST (Dubrovsky 2004)

It is possible to arrange the concepts relating to unity, parts and relationships according to figure 3 in line with the ideas of von Bertalanffy. A well formulated and interesting criticism of GST is given by Dubrovsky (2004). Dubrovsky points out that the core of GST fails to formulate a single systems principle applicable to all systems, thus being general. The problems pointed out by Dubrovsky also explain the lack of a structured core in the books of GST (Skyttner 2001). He finds the origin of this problem both in the system concept of GST as well as in the related methodology applied.

The criticism of the concept of system, as defined by Bertalanffy, is contained in two paradoxes, the paradox of emergence and the paradox of system environment.

Unity is represented as an entity of its own. However, if unity is the sum of the parts and their relationships then unity ceases to exist if any of the parts are removed. Thus unity is dependent on the parts and becomes a redundant concept. For the concept of unity to be justified, it must have properties of its own.

Further, if unity is seen as a system boundary then what is outside that boundary is called the system environment. However, as the system is interacting with the environment, the system and the environment must be two separate entities and thus the system is outside the environment (The paradox of system environment). Skinner avoids the paradox of environment by claiming that an organism is not a system but rather a “locus of behaviour”. (Dubrovsky 2004).

2.3.2 The Analysis-Synthesis system method Kant provides an interpretation of unity that avoids the emergence paradox. Kant emphasizes the priority of unity over the relationships of parts. Unity is not emergent but exists prior to the relationships of parts. A system is not a matter of empirical observation, but rather a theoretical model or ‘schema’ determined by the combination of system principles and the subject matter. According to Shchedrovitsky (1966) (as referenced by Dubrovsky), in the Activity approach the relationships are created in a process of synthesis, and the parts are defined in a process of analysis. A metaphor is provided by Dubrovsky (2004):

System unity relationships

Part

Part

Part


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Suppose one drops a teacup (unity), so it breaks (‘analysis’) into pieces (parts). One then glues the pieces together (‘synthesis’) in such a way that one can drink tea from it again (restored Unity). In this metaphor, the glue symbolizes a new addition (Relationship) that was not present in the teacup before it was broken, but had to be added in order to restore the cup.

This system definition, with unity as a complementary representation to parts and relations, is illustrated in figure 4.

Figure 4 Logical relations among System constituent according to Kant (Dubrovsky 2004)

Further, three oppositions are defined as form-content, complex-simple and external-internal. Based on these oppositions three system procedures are found. The first procedure concern decomposition of an object into parts and is the opposite composing. The second is the measuring of aspects of parts and wholes that is the opposite of configuration. The third is the insertion of an element into the object’s structure, opposed by the extraction of an element out of the structure.

2.3.3 The goal orientation of systems Bertalanffy claims that systems are teleological, that they are goal-oriented and strive towards some end. The goal-orientation of systems has been criticized. Jordan, as referenced by Checkland (1999), distinguishes purposive and non-purposive systems. A mountain range is non-purposive while a road is defined as purposive. Checkland notices that the purpose is in the eye of the designer, builder and user of the road and not intrinsic in the road itself. He then proposes more useful distinctions that can be used to further clarify the goal orientation concept. He classifies five types of systems: Transcendental, Natural, Human activity, Designed physical and Designed abstract systems.

Part

Part

Part

Part

Part

Part

Parts and relationships

Analysis Synthesis

System unity


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Transcendental systems are beyond knowledge, unknown to man and can be ignored for our purposes. Natural systems can be analyzed, human activity systems can be analyzed and influenced, and designed systems can be analyzed and redesigned. A designed system has a function designed for a purpose. Checkland makes a clear distinction between activities (or systems) that simply serve a purpose, and activities (or systems) which are the result of a willed choice by human beings. Checkland chooses to label the first type serving a purpose as purposive, and the latter, when conscious human action is involved purposeful.

3 GST and the MOF In this section, we compare the ideas presented in Dubrovsky (2004) with the meta-meta-model of the UML language, the MOF. The models are represented as UML class diagrams (OMG 2002).

Bertalanffy’s GST model as interpreted by Dubrovsky are visualised in the class diagram shown in figure 5a. In this model, the composition of Parts into a Unity is represented using the composition relation “contains”. This simple model does not however model the recursive definition of each part, as a unit with its own decomposition into parts and relations. Figure 5b further develops the model, illustrating the recursive nature of the system definition.

This model is valid for both Batalanffy’s and Kant’s system view. The difference lies in how the Unity and Parts are formed. The decomposition and relations are, according to Dubrovsky, inherent in the system in the Bartalanffy version, and formed by the beholder in the Kantian version.

Figure 5 A UML class representation of the GST

Figure 6 shows the definition of the MOF (version 1.4). This model is obviously more elaborate than the GST model. It is possible to compare the two and find the GST concepts in the MOF. The ‘contains’ aggregation between Unity and Parts in GST maps to the ‘contains’ aggregation between Model Element and Namespace in MOF. The generalization between Model Element and Namespace and the ‘DependsOn’ association can be seen as instances of the ‘relation’ association in the GST model. The similar reasoning can be applied for the other abstractions in the MOF model.

Unity

Part

contains

relation

1

*

*

*

Unity/Part

relation

contains

*

1 *

*

(b) (a)


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Figure 6 Key abstractions of the MOF model (v1.4)

It is an interesting to note that the MOF model is defined using a class Diagram, implying that a class model is a meta-meta-meta-model (Level 5 model). The GST model is very close to that of a simple Class diagram, with the assumption that the generalisation and aggregation relationships are seen as special types of association relationships between classes. A unity/part maps to a class, while a relation maps to association. The problem generally encountered in class diagram is the flat structuring of the system, which is handled in the GST using the ‘contains’ relation. This ‘contains’ relation should however not be confused with the composition/aggregation association in class diagrams, since the former is used in GST to manage the complexity of the system model, while in the latter, aggregation/composition is used to describe a decomposition property inherent in the system itself. This is the exact criticism addressed to Bertalanffy by Dubrovsky.


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4 References Ahari P. (2003) A Living Systems Approach to Product Design and Development. Doctoral thesis,

ISRN/KTH/MMK/R-03/41-SE, TRITA-MMK 2003:41, ISSN 1440-1179, Department of Machine Design, KTH, dec 2003

Bertalanffy, L. von (1969) General System Theory. New York: Brazilier.

Buede, D. (2000) The Engineering Design of Systems: Models and Methods, John J. Wiley & Sons.

Checkland, P. (1999) Systems thinking, Systems practice: Includes a 30-Year Retrospective. John Wiley & Sons.

Checkland, P. (1981) Systems thinking, Systems practice. John Wiley & Sons.

Dubrovsky V. (2004) Toward System Principles: General System Theory and the Alternative Approach. Systems Research and Behavioural Science. Vol 21. 2004. pp 109-122.

Flood R.L. (2000) A Brief Review of Peter B. Checkland’s Contribution to Systemic Thinking. Systemic Practice and Action Research. Vol. 13, No. 6, 2000, p723-731.

Gillies, D. A. (1982). Nursing management a systems approach. Philadelphia: W. B. Saunders Company, 56-74.

Guberman S. (2002) Reflections on Ludwig von Bertalanfy’s “General System Theory: Foundations, Development, Applications” Proceedings of the 5th European Systems Science Congress, Crete, October 2002

IEEE. (1998) IEEE Standard for Application and Management of the Systems Engineering Process. IEEE-Std 1220-1998., IEEE, New York, 1998.

Loureiro, G. Leany, P.G. & Hodgson M. (2004) A Systems Engineering Framework for Integrated Automotive Development. Systems Engineering. Vol. 7, no. 2, p. 153-166.

OMG. 2002. OMG - Meta Object Facility, v1.4. April 2002.

Shchedrovitsky GP. (1966) Methodological problems of system research. General Systems 11.

Skyttner L. (2001) General Systems Theory. World Scientific Publishing, Singapore, ISBN 981-02-4175-5

Weinberg, G. (2001) An introduction to general systems thinking (silver anniversary ed.), Dorset House Publishing Co., Inc., New York, NY, 2001


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5 Appendix – Book summaries

5.1 Book reviews There are many books written about systems, system theories and system design. 32 000 titles were found at Amazon in 2003 (Ahari 2003). Some of them define systems in a narrow sense and in a domain specific context, while others deal with general system theory with a generic definition of system. Further, different literature targets either the description or the analysis or the synthesis of systems, or possibly a combination thereof. In this chapter a range of books about systems in the widest sense are reviewed.

The chosen books should give a good overview of the field. Ludwig von Bertalanffy is seen as one of the fathers of the theory, and the material in his book contains many of the original sources of GST. Skyttners book is a recent textbook covering several theories, which is rather rare in the field of general theories. Weinberg is an older introduction more focussed on content than on providing complementary theoretical views. Checkland targets systems that are hard to analyze due to complexity or with unclear purposes, providing a methodology labelled the soft system methodology (SSM).

Three of the book reviews (Bertalanffy, Skyttner and Checkland) share a common format. First they give a short evaluation of the contents, and then a summary of the book according to the structure of the contents is posted. At the end of each section some concepts and ideas of the book is summarized by keywords. The fourth review is a more comprehensive summary of the book by Weinberg, providing a deeper insight into the general system ideas.


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5.1.1 General Systems Theory – Lars Skyttner (2001) World Scientific Publishing, Singapore, ISBN 981-02-4175-5 (467 pages) The book of Lars Skyttner provides an overview of the main original ideas related to general systems theory as well as a summary of common ideas.

The first part of the book, titled “The theories and Why”, gives a good overview of related thinkers and the historical development of more abstract and general theories. What is lacking in this section is a proper conclusion, the contents are very descriptive and the author does very little to generalize the described theories which would be expected after such a thorough coverage of the topic. For my taste this would have been more valuable than a chapter with 14 different theories. It is however nice to read, especially for people collecting curiosities.

The second part of the book is astonishingly application specific with very few relevant parallels, generalizations and conclusions related to any of the general theories. In the end a summary of methodologies is given but the methodologies are too shallowly described in order to provide further insight into general systems theory.

The book may be a nice introduction for people looking for an overview of system theories and inspirational for further research; but it includes more detail in application specific topics than necessary and less insightful generalization and details of general system theories than expected. The concept of general systems theory remains unclear after reading the book. Skyttner states that it is impossible to be efficient with one theory and that several views are necessary, this may be true and also explains the general impression of the book as probing much into the details of the applications, and avoiding generalizations. This lack of generality seem a bit contradicting to the title and also the final chapter where a general systems theory is seen as the next paradigm of science. The confusion may be attributed to the deficiency of a clear conclusion in the book.

Summary of the contents The first chapter of the book provides a historical summary of the view on science since the 15th century. We are placed in the system age where problems (in theory) are addressed in a holistic and interdisciplinary way, instead of the prior reductionist method of breaking down problems into parts. The emergence of the general systems theory (GST) in the 50’s is mentioned, and Ludwig von Bertalanffy and Kenneth Boulding are specifically referenced in this context.

In the next chapter, Skyttner summarizes some basic ideas in GST. He introduces a range of definitions and concepts of systems that are used throughout the book. Some general laws, principles, theorems and hypotheses are also posed. Much of the material here is immediately derived from the work of von Bertalanffy.

The third chapter provides a summary of 14 existing system theories. Many of the theories aim for a classification system of systems. What is interesting in this collection is that many of the theories lack generality and instead apply specific layers of system types to describe the world. Many of the described system theories fit into one proposed distinct classification hierarchy defined by the respective authors, usually inspired/guided by the physical size of the systems. Some of the classifications seem very adapted to popular notions of science, and occasionally quite ad hoc, which make them feel less relevant and useful. However, some interesting general ideas can be extracted, for example from the subsystem definition of Millers Living Systems Theory, the typology of Checkland, the taxonomy of Jordan and the recursive application of the Viable System model of Beer.


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After the overview one chapter is spent on describing the communication and information theory. At first this dispositions feels strange, especially as the book lacks a good bridging section in the text. The chapter brutally begins with defining the difference of communication and information, but as the contents develop the chapter feels highly relevant and important for a wider understanding of systems. The idea that information only exist in the eye of the beholder is repeated and the nature of information as an abstract entity is discussed. The general points of information are then elaborated in the next chapter in a human application context discussing theories of brain and mind. This chapter concludes the first part of the book titled “The theories and Why”.

The second part called “The applications and How” begins with a chapter about Artificial Intelligence (AI) and Artificial Life that smoothly links to the last chapter of the first part about theories of brain and mind. The chapter includes nice trivia on the definition of life and futuristic visions including a recollection of current research topics in the field of AI.

The next, and seventh, chapter details the theory of organization and management. Here another nice historical recollection of a theoretical field is given but the attempt to summary of “a systems approach in ten points” is less clear. A management related topic is covered in the next chapter, “decision-making and decision aids”. The chapter provides theories on what decisions are, how they are conducted, and how computers can be used for supporting decisions. Some useful concepts and categories are introduced. The process of decision making is central both for management and engineering, however the role and process of decisions is usually not equally clear in engineering compared to management. The inclusion of the chapter seems highly relevant and provides some guidelines on how to cope with the increasing uncertainty of increasingly complex systems in a fast changing environment.

The ninth chapter on informatics is again very application-specific and full of technical details that add very little value for the informed reader. The only interesting content is a short summary of a suggested lifecycle model for evolutionary development of information and communication networks.

Chapter ten returns to the general theories and summarizes the application of a few system methodologies. The distinction between soft and hard methodologies based on the ideas of Checkland is established and the described methodologies are classified accordingly. Soft methodologies are best applied to ill-structured problems with unclear objectives and purposes. Hard methodologies are goal-oriented and solve well-defined structured problems. The overview and points made are relevant, the methodologies are however too briefly described to be well understood.

The final chapter is a political manifest that suggests systems thinking to be a new paradigm struggling to make a break in a world of critics. In the end the system theories will prevail as the only sustainable way to implement science.

Theoretical concepts and definitions Even though the book contains much application specific information, chapter 2 “Basic Ideas of General Systems Theory” provides a range of useful concepts and definitions.

The hallmarks of a general systems theory according to Skyttner, specifically referring to Bertalanffy and Litterer are summarized in a list of 10 points. Interrelationship and interdependence of objects and their attributes – Unrelated and independent elements never constitute a system. Holism – Some properties exist only at system level and cannot be detected by analysis of the components of the system.


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Goal seeking – Systems strive for a final state or equilibrium. Transformation process – Systems transform inputs into outputs. Inputs and outputs – Open or closed to the environment of the system. Entropy and negentropy – All systems tend toward disorder. A living system can for a finite time use energy to create order (negentropy). Regulation and feedback – All systems have regulatory mechanism, feedback is a requisite for effective control. Hierarchy – Systems exist of subsystems, that in turn are systems of their own. A hierarchy of systems exist. Differentiation – Specialized units in the system performs specialized functions. Equifinality and multifinality – systems have alternative ways to achieve the same goals (convergence), and can obtain different mutually exclusive goals from a similar initial state (divergence). Besides the summary of the general systems theory a set of useful classification frameworks and concepts are provided. For example, systems can be: Concrete (living, non-living), conceptual or abstract – Depending on the tangibility of the system. Open, closed or isolated – Depending on the relation to the environment. Decomposable, near-decomposable or non-decomposable – Based on the dependence of subsystems. Static or dynamic – Based on the activity of the system. Black, grey or white box – Depending on the knowledge of the internals of the system. Chapter 2 provides a filtered summary of the collected work in systems theory and provides a toolbox for system reasoning. Unfortunately the presentation of the material could have been more structured. The concepts are highlighted in the text, which is very good, but a good structuring or overview of the concepts are lacking.


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5.1.2 General System Theory – Ludwig Von Bertalanffy (1968) George Brazillier, New York, ISBN 0-8076-0453-4 (295 pages) Von Bertalanffy is considered one of the founders of the general system theory and the book, also titled “General System Theory” is a collection of papers and book excerpts by his hand. The core of the theory is given in chapter 3 based on a paper authored in 1945, first published in German with the translated title “An Outline of the General Systems Theory”.

The ideas of Bertalanffy are surprisingly focused on mathematics, but he also strongly acknowledges the use of soft, qualitative models which is seen as an intermediate step in the theory building process.

Bertalanffy brings out a range of concepts related to general systems, but the system definition and ontology with related general system principles is not that clear and crisp. The models and concepts are rather soft, which Bertalanffy acknowledges and motivates by claiming that this is the first steps towards a more rigorously defined theory.

The book has good value as a collection of papers from one of the named “fathers” of GST. It contains some of the original formulations of some basic system concepts. However, in order to be useful, the theories presented need more elaborate contents. A harsh interpretation of the work is that is only says that entities and relationships should be represented by mathematics, and only a few hints of what these mathematics should look like is given.

Summary of the contents The book, being a revised edition, is introduced by a preface where some additions are made on the background and at the time current flows of the theory. The first chapter, also an introduction, repeats some of the historical background and the motivation of the field. Mathematics is acknowledged as a general theory as it can be applied to a variety of problems. With some previous background on the subject the introduction becomes somewhat long and tedious. In addition, the references to trends and people in academia in the 50’s and 60’s also make the section hard to read.

The second chapter details the shortcomings of disciplinary science and introduces some basic concepts. It is noted that contemporary science only works with closed systems and theories must be developed also for open systems. The constant increase of entropy is contradicting the possibility to build organized complex systems; the idea of an open system consuming energy remedies this conceptual problem. Further, the distinction of information as a complement to energy is necessary for some theoretical constructs. Also, goal orientation and purpose are concepts that must be added, the contemporary science is referred to as mechanistic and only working with causality. A mathematically based GST is expected to be developed, however the use of soft and verbal models should not be underestimated, especially at higher levels of abstraction, by von Bertalanffy referred to as organization.

The third chapter expands the mathematical content of the theory. First an important distinction between summative and constitutive characteristics of elements is introduced. The summative characteristics concern properties independent of relations. For summative characteristics the system is no more than the sum of its parts, a simple example is the total weight of a mechanical system. Summative properties are independent of other entities. The constitutive characteristics concern properties that depend on specific relations to other entities. In the expression “the whole is more than the sum of its parts” the difference refers to the constitutive characteristics. The usefulness of differential equations for this purpose is developed and some basics of mathematics and control theory are given.


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Then a range of system concepts are established. A discussion on dependent and independent properties of entities is given, where the system properties are the dependent properties that make the whole more than the sum of its parts. This is followed by an interesting note that in biological, psychological and social systems interactions among elements decrease over time, from wholeness to independent, specialized causal chains. The machine-like behaviour of the sum of the independent chains is referred to as progressive mechanization. Another related interesting note is that systems often have a leading entity. This leading entity defines the individuality (from indivisible) of the system. The individuality and the leading role of the leading entity, increases as specialization of entities progress in the system. By this specialization the dependencies among the entities increase and over time a system becomes more bound together. The process is referred to as progressive centralization. Systems will be arranged in a hierarchical order of centralized systems.

Then the concept of finality and types of finality is introduced. The first type is static teleology or fitness, which means that a given system seems to be useful for a given purpose. Then there is dynamic teleology indicating a directiveness of processes. This is a direction of events toward a final state, possibly based upon the structure of the system. There is also true finality or purposiveness, for example in man made systems where it is fitness and structured working of machines due to a planning intelligence. It is also mentioned that the same final state can be reached from different initial conditions and through different paths, this is labelled equifinality.

The fourth chapter expands the advances in GST and exemplifies how several fields of theory introduce system concepts like organismic analogies, and interdependencies of entities rather than causality. The last section of the chapter lists a set of theories and relates them to the previously described concepts of general system theory. Bertalanffy recognizes two methods of general systems research: the empirico-intuitive followed by himself, and the deductive developed by Ashby. The emprico-intuitive approach looks for laws in each discipline and then compares the results to find laws that hold across systems in general. The deductive approach begins with a definition of system and from this definition general laws are deduced.

In the fifth chapter the mathematical parts are expanded with definitions of equifinality and concepts related to open systems. A few applications in biology and their maturity in developing mathematical models are referenced.

The next chapter, called “the model of open systems” discusses how most (contemporary) theories are based on closed systems without import and export of matter. It quotes the laws that entropy is always increasing; but recognizes that for an open system, entropy may be reduced by import of matter. Negative entropy (or negentropy) is identified as information. A decrease in entropy of a system means that more information is available in the system in the sense that a complete system description requires a more elaborate content.

Chapter seven is a case study of general systems theory in biology. Open systems, feedback and mathematical relations are suggested to be complementary and equally important theoretical contributions. Bertalanffy also make a warning for oversimplification in the models. Mathematical models are shown to be useful but not conclusive, all models are said to be approximations of what they describe. Equations are representing a theory if all parameters of the equation can be confirmed by independent experiment; and if predictions of yet unobserved facts can be derived from the theory. Then a case showing the development of a mathematical description for metabolism and growth exemplifies the reasoning.

Chapters eight and nine continues to exemplify applications of the general systems concepts through the sciences of man. Chapter eight details the benefits of using a system model in the social sciences. First it is established that the contemporary theories of man as a robot is


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incomplete, and must be replaced with a view of man as an active open system interacting at the social system level. This does not imply that mathematical laws are useless, for example, historical events can often be explained by applying a law of social behaviour; however, Bertalanffy again warns about oversimplification and interpreting models to strictly.

In chapter nine, system theory in psychology and psychiatry is discussed. The model of man as an active organism is further extended and exemplified. The ideas of progressive mechanization and the development of leading entities are repeated. It is also implied that the brain may perform such changes, adding higher layers of behaviour with each layer including a leading entity. This is in line with the levels of Maslow, and is exemplified by the evolution of creatures in nature.

The final chapter widens the scope by posting that most of the published theories are based on the mindset of the western world and our notions of organizational structures, time and space. Examples of cultural differences in the number of existing words for a given phenomenon, and even in how time is treated are given, specifically from the Eskimo Hopi culture and American Indian cultures. Any model only captures a few aspects of reality and the categories and models of our experience and thinking are determined by biological and cultural factors. Also, different backgrounds and domain knowledge also influences our view of a system. An example is given with a table, seen as a system of atoms by the physicist, a system of wood by the biologist and a unit of capital by the economist.

Theoretical concepts and definitions Being an overview of the early theories of GST several concepts and definitions are introduced. Below are a few of the core ideas: Isomorphism – Along the disciplines of science there exist common ground, possible to cover by a common theory. This property is referred to as isomorphism. Goal orientation (active systems) – Systems may actively strive for a goal or final state. They are not necessarily mere causal machines. Open system – Systems where material flows in and out are open systems. In an open system entropy may increase locally due to the inflow of material. Homeostasis (feedback) – Systems strive for a desirable steady-state through regulating itself based on feedback of information. Equifinality – systems have alternative ways to achieve the same goals (convergence), and can obtain different mutually exclusive goals from a similar initial state (divergence). Progressive mechanization – Systems strive towards specialized entities. Progressive centralization – In the process of mechanization each system is organized around a leading entity. Hierarchical Layers – Each leading entity creates a system layer, higher layers can be introduced with new leading entities and subsystems also have leading entities.


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5.1.3 Systems Thinking, Systems Practice – Peter Checkland (1981/1999) John Wiley & Sons Ltd, Chichester U.K., ISBN 0-471-986 062 (66+330 pages) The book was first published in 1981 and in the 1999 edition it includes “a 30-year retrospective” a paper also published elsewhere. This section is one of the most interesting parts of the book, giving a brief outline of the soft systems methodology (SSM) comparing it to hard systems engineering.

SSM advocated by Checkland is seen as complementary to systems engineering to deal with complex situations, exploring the underlying process. The target for Checkland is mainly human activity systems and social systems but the ideas seem relevant also for the engineering of complex technical systems with conflicting requirements.

Studying the ideas reveals a loosely defined methodology that occasionally seem arbitrary and non-systematic. However, this is maintained as one of the strengths of the methodology as it enables a discovery of the actual underlying processes. The general idea is to draw simple, graphical models to visualize parts of the system and create a common ground for discussions. The actual notation in the models is very simple but elaboration of modelling is suggested if it improves understanding of the system, the selection of extended language is left as an open issue.

The book gives a nice perspective on the benefits and limitations of system ideas in practical application for undefined problems. For technical systems with clear requirements a hard systems engineering approach is suggested, but the book places nice bounds on the domain of systems engineering.

Summary of the contents The 30-year retrospective, which is the first part of the volume, begins by discussing the notion of hard and soft systems. Checkland points out that hard systems thinking have been successful in engineering technical systems with clear purposes and objectives. However, in fuzzy situations with unclear entities and where the objectives are uncertain and contradicting soft systems thinking can be useful to clarify the picture. (This can be applied to social systems but also architecture design and similar situations.)

After an historical overview of proposed methodologies from the author, the current modelling methodology is outlined. Checkland proposes that soft problems should be explored by modelling the transformation in focus, guidelines for performing this modelling is given. The purpose of the model is either to implement change or to understand a complex process.

Checkland underlines the methodology aspect of soft system methodology (SSM). A methodology is a collection of methods from which you choose the appropriate ones for a given situation. Ultimately SSM should be used iteratively, situation-driven and in interaction with the system, not sequential, methodology-driven and intervening with the system. It is concluded that SSM does not replace but rather extends the traditional and existing system engineering approach. Then the first chapter in the main book begins with an introduction that places systems theory side-by-side to science. Systems theory is not a science, it is a meta-theory that can be applied in the same way as science.

The main text is divided in two parts similar to the title, a systems thinking part and a systems practice part. Chapter two is the first of the systems thinking part and gives a thorough and well told walkthrough of the development of science, from the Greeks (as always) to Einstein. A scientific approach breaks down a problem, formulates it mathematically, and makes


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repeatable experiments to verify the validity of predictions. The value and usefulness of this approach are appreciated as monumental. This background serves as a foundation for the discussions of the systems approach covered in the next chapter.

The third chapter introduces some problems for science. The scientific approach has problems to cope with complex, large, multi-variable problems where it is impossible to isolate a small set of variables for analysis. Further, for higher level systems like social systems it is difficult, not to say impossible to properly measure and repeat experiments. Monitoring a system changes a system which means that results depend on the method of measuring (compare the uncertainty of Heisenberg). A third problem is the issue of management where decision-making is an instant interaction and the situation is rarely repeated. The one shot nature of monitored events inhibits a scientific approach. These problems call for a different approach to cope with some human activities.

First Checkland introduces the concepts of Emergence and Hierarchy. Systems are hierarchical, layered arrangements where some properties emerge at a given layer of abstraction and each layer contains laws that need to be studied separately. Each layer contains systems or ‘holons’ that are organized and linked. Second, Communication and Control are introduced as an important systems concept pair. In a system not only energy but also information is flowing. The information is a view of the system that allows entities within it to react. Some entities exert control on others with respect to given control variables, defined by the information content. In technical systems this control is designed by the control engineer, for biological systems this control exists and can be modelled by a systems approach.

Next, Checkland produces and overview of the systems movement where he recognizes three problem solving applications related to the theoretical development of systems thinking. Applied systems thinking is performed in (1) ‘hard’ systems, (2) decision-making problems and (3) ‘soft’ systems.

The fourth chapter “Some Systems Thinking” begins with describing some basic ideas that are recurring in different theories. A systems description is always related to an observer. The description itself contains entities, relationships/coherence, a boundary, a control mechanism (defining the entity’s identity) and is part of a hierarchy.

The theory of Boulding’s hierarchy and Jordans’ taxonomy are referenced and the importance of the observer in a system description, lacking in Jordans model, is underlined. Then the typology of Checkland himself is described. Systems belong to one of five types: natural systems, human-made physical systems, human-made abstract systems, human activity systems and unknown transcendental systems. Natural systems are evolutionary made, while the human-made systems are designed for a purpose. Human activity systems are, according to Checkland, distinguished by the free will which makes humans unobservable and unpredictable. Human activity systems are made up of purposeful activities, occasionally using purposive designed systems as tools. These definitions together with a short conclusion on basic systems thinking summarize the first part.

The second part “Systems Practice” begins with an overview of “Hard” systems thinking, and its’ limitations. “Hard” methods like systems engineering assumes that the system have a clear purpose and is optimized towards this purpose, they are goal-oriented. This assumption holds for human-made systems in general but breaks down for human activity systems, and also for some human-made systems where there exist conflicting goals and purposes. For these problems Checkland proposes a “soft” methodology which is elaborated in the next chapter.


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The soft systems methodology has seven stages. Stages 1 and 2 concerns expression of the problem; as much information as possible is collected from a variety of sources to make the richest possible picture available. In stage 3 a root definition of relevant system(s) are formulated. Based on the root definition a conceptual model is developed. The root definition is an account of what the system is; the conceptual model is an account of the activities the system must do in order to be the system of the root definition. In phase 5 the derived conceptual model is compared to reality. Do the activities of the conceptual model fit the existing system or what are the differences? Based on the results, stage 6 contemplates feasible and desirable changes and stage 7 implements changes through action. The methodology is illustrated by an example, and further examples are given in chapter seven.

After the examples, chapter seven introduces some conclusions from the research. First the importance of understanding the Weltanschauung (viewpoint) of the root definition is discussed. Depending on the viewpoint of the person formulating the root definition the contents of it will be different; this is one of the purposes of the root definition, to capture diverging views of the system. Further, the root definition can be focused on a given primary task or a more general issue of a system. The mnemonic CATWOE elaborated below is given to support the construction of root definitions. The importance of elaborating the initial study is underlined, and understanding of the power and politics derived from roles, norms and values are rated as highly important for the success of a study and the development of a useful conceptual model.

The importance of understanding the difference between what a system does and how the system does it is also important. The conceptual model derived from the root definition is an abstract formulation of what the system does, the comparison with reality reveals how the desired function is performed. In the stage of mapping between the two the link must be understood. Related to the logical hierarchy is the law of conceptualization that states that a system which serves another cannot be defined or modelled until a definition and model of the system served are available. Models, according to Checkland, consist of verbs specifying activities which actors carry out.

The eight and final chapter is presented as a third part of the book containing conclusions. In this chapter Checkland places his theory and methodology in the context of social science and related work of other authors. At the end of the book are two appendices with some hands-on advice on the topic, collected under the headings “building conceptual models” and “a workbook for starting system studies”.

Theoretical concepts and definitions In the typology of Checkland there exist five types of systems: Natural systems – Found in nature and developed by evolution. Human-made physical systems – Tools and machinery existing in the real world. Human-made abstract systems – Conceptual systems like mathematics. Human activity systems – A purposive system which expresses some purposeful human activity. Trancendental systems – Systems yet unknown to man. In the soft systems methodology a root definition must be formulated, the root definition is a formulation of the function of a given system (a ‘what’). Root definition – A position in a means end hierarchy of Why(R), What(P), How(Q). It is always possible to go up or down in this hierarchy. A root definition should meet the requirements of including the six elements of CATWOE: Customers – The beneficiaries or victims of the system.


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Actors – Agents who carry out the activities of the system, especially its main transformation. Transformation – The core process of the root definition. Weltanschauung – The viewpoint of the person formulating the definition. Ownership – The guarantee of the existence of the system. Environmental constraints – Impositions that the system takes for given.


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5.2 Extended book review: An Introduction to General Systems Thinking – Gerald M. Weinberg

Dorset House Publishing Co., Inc., New York, NY, 2001 ISBN:0-932633-49-8(279 pages)

5.2.1 The Problem

The Complexity of the World Science and engineering have brought about an unprecedented speed of change, without being able to control its effects. General Systems Theory is brought about because science has been a success. Science and technology have revealed a complexity that it could not deal with. Page 3: The GS movement has taken up the task of helping scientists to unravel complexity, technologists to master it and others to learn to live with it.

Mechanism and Mechanics Page 3: Physics does not endeavour to explain nature … it endeavours to explain the regularities in the behaviour of objects … called the laws of nature. What is science? To answer this, we examine physics/mechanics. As expressed by Karl Deutsch, the mechanical model of the world implies that “the whole is completely equal to the sum of its parts … parts were never modified by each other … “. This works for 2 or 40 parts such as bridge, but is a problem for too many parts. So, why use this model for science? Because it allows us to reduce our complex systems to simpler ones. To be able to formally solve large systems, we informally reduce them to simpler ones first by ignoring insignificant parts, assumptions, etc. In other words, you start qualitatively to get your model, and then apply science/quantitative work.

Square Law of Computation Without simplification, the amount of computation increases at least as fast as the square of the number of equations. In practice, there is a limit to how much computations we can do in money and time. Hence, we need simplifications and assumptions.

The Simplification of Science and the Science of Simplification When getting your model – building your assumptions – how do you know what to ignore? Why ignore force of personality when calculating forces between bodies? It is because when we try the assumptions, we get satisfactory results that match observed data.

The general system thinker’s task is to understand the simplifying assumptions of a science. He goes through the process by which a scientist forms his model and uses this to suggest useful models for other sciences.1

The law of computation is not only about the limits of computing devices. The brain is also a computing device and we need to handle the amount of information given. Need to be able to simplify, idealise and streamline the world so it becomes tractable to the brain. This is the reason a GS thinker is interested in simplification – the science of simplification. By studying

1 GS thinker produces a set of tools of simplifications that are common enough to be useful for all scientists. That is, they build a common model that can be applicable by all sciences. Isn’t this what we are trying to do in AIDA2? Build a common model that all specialists understand and can base their specific models on? So, why not use GST?


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the methods of simplification that have succeeded and failed in the past, we can handle complexity.2

Statistical Mechanics and Law of Large Numbers Scientists may sometimes be interested in average properties rather than exact proprieties of a single item. Consider the properties of a gas in a bottle. We need not look at the specific molecules, but can study average properties such as volume, pressure and temperature. The law of large numbers states that “the larger the population, the more likely we are to observe values that are close to the predicted average values.” A more useful rule of thumb is the Square Root of N Law, which states that “the inaccuracy in an average statement is in the order of the square root of N, where N is the number of the population on which the study is performed.”. Statistical mechanics deals with “unorganised complexity” – complex systems, that are sufficiently random in their behaviour so that they are sufficiently regular to be studied statistically. The concept of “randomness” is most important for systems thinking. “Simplicity” is as slippery a concept as randomness. To a first approximation, the number of objects is a measure of complexity – the complement of simplicity. Randomness is the property that makes statistical calculations come out right.

I

III

II

Complexity

Randomness

I – organised simplicity – machines

II – unorganised complexity – populations III – organised complexity – systems

III is too complex for analysis and too organised for statistics, hence we resort to systems theory. System theory came about as knowledge moved from the mechanical view (I) to the organised complexity world (III)3

Law of Medium Numbers

The philosophy of technology is usually drawn from scientific philosophy of its time. The technology of machines has drawn its inspiration from mechanics, dealing with complexity by reducing the number of parts. The technology of government has drawn upon statistical mechanics, creating simplicity by dealing with people in the structureless mass, taking averages. There is a lack of means to deal with systems between the two extremes – systems of medium numbers. The Law of Medium Numbers states that “for medium-number systems, we can expect that large fluctuations, irregularities and discrepancies with any theory will occur more or less regularly.” (Anything that can happen, will happen.) The importance of this law lies in its scope of application since we are surrounded by such systems. (Computers, humans, forests, etc.) Science is a very useful tool, but its fruits are simple fruits, fruits of

2 This is also what we are interested in! To produce some simplification techniques that allow the designers to handle the complexity of the systems to be built 3 Modern machinery is moving from I to III as computer technology is introduced. Maybe that is why we can use GST in our engineering work?


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simplifications. Many ills of society came from a too good an application of these fruits. Page 20: Science is unable to cope with MNS, though its success with systems of its own choosing mislead many into thinking of science as a way of dealing with ALL systems. One method of simplification applied in technology, is to focus on the parts of the system, while paying less attention to the connections between these parts and to the rest of the system. The problem is pushed from the parts to the connections. This may be carried to great extremes. From time to time, it is recognised that a system is not merely a collection of parts, but a collection together with the relationships between them. Then, a new level of technology is reached. This new technology becomes in its turn a ‘component’ in the new way of thinking and the connections to it become the weakest part of the system. This separation of function is useful, but it should not be carried to extreme. Revolutionary movements recognise the importance of the connections and synthesise them into a new field of knowledge (new part) such as electromagnetism, physical chemistry, etc. GST is not going to yield the kind of control expected over MNS, its contribution is to be in limiting the excesses of other approaches to complexity. Page 22: Perhaps we are reaching the useful limits of science and technology whose philosophical underpinnings are techniques restricted to systems of small and large numbers.4

5.2.2 The Approach

Organism, Analogy and Vitalism GST aids thinking about Medium number systems (or organised complexity) by finding general laws. These laws are stated informally to aid understanding, but they must be supportable by vigorous operations on vigorously defined models. This is to avoid previous mistakes by other approaches. Compare to the organismic approach that turned to living systems for analogy to handle complexity. Organismic thinking explains things through analogy. They may reduce everything to a single primitive, the vital essence. The organismic thinking – the use of analogy – is not to be discarded. Even scientists still use it to simplify thinking. What is important is not to stop at the analogy and to render it into a precise, predictive model. Similarly, GST is scientific in its thinking.

Science is the study of things that can be reduced to the study of other more primitive things. Animistic religions explain the behaviour of everything by referring to its unique spirit. Mechanists explain everything in the primitives of physics. For it to be a science, the set of primitives cannot be too small or too large. (Explaining everything through god is not scientific.) These primitive things are not questioned, and it is expected that a scientist have faith in them. Page 28: Every model is ultimately the expression of one thing we think we hope to understand in terms of another that we think we do understand.

The Scientist and his Categories Page 32: One manifestation of ethnocentrism is the belief that one’s own culture is ‘superior’ to that one does not understand. Thinking is done in completely personal, idiosyncratic terms, so much so that how it is done is incommunicable. However, many overt categories of thought exist. By possessing a common set of standard categories of thought – symbolised by special words or phrases – groups can simplify the process of internal communication. To be part of the group, one must master the internal category of thought. A physicist generally possesses the thoughts of celestial mechanics as well as that of auto mechanics and have no problem switching between them. Only when difficulties arise, that he notices the different category

4 Isn’t that what we are also experiencing when designing a truck? The parts (disciplines) are very clean but the connections are getting weak. Need to synthesise, leading to multidisciplinary engineering (Mechatronics).


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schemes. When he does, he will identify the ‘foreign’ language of auto mechanics as the source of difficulty (ethnocentrism).5

Scientific disciplines, like social groups, have category schemes to facilitate internal communication. These categories may change while ‘normal scientists’ work within a given scheme or paradigm. ‘Revolutionists’ create new ones and destroy old ones. This same revolution may be performed by ‘interdisciplinarians’ on many different disciplines by carrying the change intact from one to the other. ‘Interdisciplinarians’ differ from ‘generalists’ in that the former knows one thing that they apply over and over again, while the latter knows many things. He adapts to the other paradigms instead of applying his paradigm on the new discipline. Like the anthropologist that adopts to live with many different cultures, as opposed to the colonist who imposes his paradigm on the cultures he needs to live with. How is that done? They too have a single paradigm, but it is taken from a much higher vantage point, from which all the disciplines are seen to be alike, but obscured by different languages. For this to work, a belief in the unity of these disciplines is needed.6 Page 35: To be a good generalist, one should not have faith in anything. Every article of faith is a restriction on the free movement of the generalist among the disciplines. One should be careful in assuming that one paradigm is more ‘real’ that another. It may be essential for a scientist to have faith in the truth of his discipline, but this only diminishes his chances of making a revolution or moving to another discipline.

The Main Article of General System Faith Nobody can live without faith. GS approach simply replaces one set with another, in the hope of being more useful. On what basis does it promise to be useful? The answer lies in the main article of GS faith: “The order of the empirical world itself has an order which might be called order of the second degree.” A generalist finds laws about laws. The primary way of discovering GS laws is by induction. The generalist starts with the laws of different disciplines, search for similarities and then announces the new law of law. The induced laws can then be used to draw conclusions about cases not yet observed. Each time this succeeds, the general law is strengthened. But this does not always work since induction does not always work. But we are willing to take the risk of error since there is an explosive growth of knowledge. The generalist jumps to conclusions based on insufficient evidence, constantly making a fool of himself. 7

A generalist approaches a system with a certain naïve simplicity. We form a general impression of the whole before going into the details, but by foregoing detailed analysis, we are exposed to certain errors. No approach, be it analytical or synthetic, can guarantee flawless search. Each approach has its errors. By taking the grand leap based on the faith in the order of the second degree, we may often be wrong, but at least we shall find out soon. The slow-but-sure method of analysis may only guarantee that we cannot possibly arrive on schedule. (When lost in slightly familiar territory, we use general impressions as guides to 5 In Aida2, we want to make the engineers aware of these categories, so that they can identify the source of mis-communication quickly, and even try not to blame the foreigner a better engineering world if we are all equal, and all people also understand that no one is superior. 6 In aida2, engineers are also expected to have this believe and try to have this high vantage point. 7 The order of the engineering thinking should be lifted to a second degree order. The advantage of being a mechatronics is that we already know a bit about many disciplines so we have better chances of finding similarities. But we still need to move from interdisciplanarians to generalists. And, as long as we do not get ashamed and are willing to back away from conclusions when proven wrong, it should be ok to jump to conclusions. In aida2, will also use this attitude when generalising engineering. We guess and hope to be right.


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more familiar territories. If we are mistaken, we can readily correct. If we insist on reading every house number, we may miss dinner.)

The Nature of General System Laws Analogy, category schemes, generalisation are tools of GS thought. Before explaining the use of laws in GS thinking, look at scientific laws. In scientific laws, the paradigm of a scientific assertion is of the form “if so … then so”. We often forget the condition nature because assertions are stated in short hand format, since otherwise the statement becomes too long. Laws play the roles of guides to measurement, define terms, remind us to look for things we have not noticed and predict behaviour. The fewer the if-clauses, the more general and useable the law is.

When measurements are found incompatible with a well-established law, the last thing to be changed is the law itself. (Contrary to the believe that one negative case invalidates a scientific law) We formulate a GS law, the Law of Conservation of Laws: “When the facts contradict the law, reject the facts or change the definitions, but never throw away the law”

GS laws are not designed to yield answers, and hence can afford to be wrong. They will never be used for precise analysis, instead they yield insight. So, the laws will be stated in the more memorable definition rather than the accurate version. In order to avoid hollow generalisations, each law should be followed by at least two ‘happy particularities’ in order to demonstrate it. This leads to the law of Happy Particularities: “Any GS law must have at least two specific applications”. It is also necessary to avoid under-generalisations and excess caution and hence the law of Unhappy Particularities: “Any law is bound to have at least two exceptions.”8

While these laws apply to any generalising behaviour, there are laws applying to the typical ‘systems’ part of GS thinking. For example, the Composition law: “The whole is more than the sum of its parts.” and the Decomposition law: “The part is more than a fraction of the whole.” (These laws seem contradicting, which makes them hard to forget!)

Of what use are GS laws? Since they are very general and since systems are complex, they will not be helpful at making exact predictions. But because they are general and because systems are complex, these laws can help us avoid the grand fallacy on the way to an exact prediction “It isn’t what we don’t know that gives us trouble, it’s what we know that ain’t so.”

Varieties of System Thinking Page 43: The main role of models is not so much to explain … as to polarise thinking and to pose sharp questions… fun to invent and play with … This quote was originally applied for mathematical models by Kac, but can also be applied to the models of GS. He implies that there are 3 sorts of activities involving models: 1. Improving the thought process; 2. studying special systems; 3. creating new laws and refining old ones. In this framework, the GS approach’s largest contribution is to improve thought processes. (Few people might be engaged in creating new GS laws (3)). This is demonstrated by seeing how a generalist approaches a new subject. He uses the general paradigms for thought and communication. The content that maybe understood from the new discipline might be small, but it is an advantage since he is not afraid of the unfamiliar. A second type of GS activity is the application of it in different fields or special systems such as biology, engineering, etc. A third activity is the creation of new laws and refining old ones, called GS research, as opposed to GS thinking and GS application. The GS movement did not start as a discipline but is

8 We will use these laws in that spirit also. Don’t have to follow laws strictly.


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becoming one. While it was originally intended to overcome overspecialisation, it is becoming a specialisation itself.

5.2.3 System and Illusion

A System is a Way of Looking at the World Page 52: as any fool knows, a system is a way of looking at the world. It is a point of view – natural for a poet, terrifying for a scientist. Knowledge is ‘truth’, knowledge is ‘reality’ and to speak of systems in this way is not to acquire knowledge. If 2 scientists viewing the same scene have different ‘systems’ then science will be no better than poetry. When 2 different people look at the same thing and realise they see different things they want to establish which of the 2 views the real one is and which is fooled. They believe in the concept of observer independent truth. This concept is egocentric. Egocentrism is a form of animism which is a form of vitalism. Scientists have worked hard to get rid of animism/vitalism and thoughts such as ‘If I were a planet, sailing through space, how would I be attracted to the great mass of the sun?” or ‘If I were nature, would I tell lies” or “If I were nature, would I throw dice?”. How would we know how nature (reality) feels? Such thoughts have barred the way to scientific progress, yet they are not totally without use. We can get insight into the ideas of force and motion from our internal response to situations, and we can make progress in science by believing in the reality of the external world. Hence the realist will quote Einstein: ‘The belief in an external world independent of the percipient subject is the foundation of all science” (objective observations) but note that Einstein did not say ‘An external …’. He did not say that an external world is essential, but that the belief in it is essential. Yet, he put the relativity theory which rocked the scientific world because it was based on the premise that we could only know the external world through our perceptions. Belief in an external world is a heuristic device, a mental tool to aid in discovery. But, like all heuristic devices, it cannot tell us when and where it can be successfully applied. Mechanics alone cannot tell which systems will yield to mechanical analysis. This is generalised to the ‘banana principle’: “Heuristic devices don’ tell you when to stop.”

There is a scale of ascending values of heuristic devices, depending on how far you can go before you must stop: idea, concept, rule, principle, law, reality, truth. The further along the scale we go, the less we notice that it is a device. We forget the Banana Principle and think we can use it forever. The more success we have, the more sure we become and the more sure we are, the more likely to suffer an illusion - The conviction that there is only one way of interpreting the visual pattern in front of us. (Isn’t that what most people believe about science? That is provides the truth, the ultimate heuristic device, since it has been so successful.)

The belief in an external world is one of the most powerful thinking tools we have and we don’t intend to discard it. There exists a complementary tool: “relational thinking”. There may be ‘real objects’ out there in the world, but if there are, it is not because we perceive them as real. Perception responds to both illusion and reality. Similarly, there maybe ‘real laws of nature’, but if there are, our strong belief in their existence may be preventing their discovery. So, let us see what we can learn if we occasionally suspend the belief in independent reality. This too is a heuristic device.

Absolute and Relative Thinking Statements in a language only have a meaning in relation to certain accepted meanings of the words in them. ‘Accepted meanings’ implies that somebody is doing the accepting – the


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observer. The appearance of absolute meaning in certain statements comes because there is an almost universal agreement on the meaning.

A system has ‘no purpose’, for purpose is a relation that depends on the observer. General Motors to a user exists to put out cars, yet to the junk dealer it is there to put out scrap metal. A system does not have a reason to exist, but more or less an official public reason, just like the public agreement on the meaning of a word. We just do not need to highlight this all the time.

It is more forceful to speak in absolute terms. Most of the time, absolute speech will not get us in trouble, though we may learn something if we examine the relative nature of some seemingly absolute statements. A simple example of absolute thinking is seen in answers to the question: ‘What happens to the reading on a thermometer if we suddenly plunge it into hot water?’ A simple answer is: ‘the reading must rise because the reading measures the expansion of mercury and the mercury expands when heated.’ There are 2 concealed absolutisms in this statement. One has to do with the time scale of the observation, since it seems to imply instantaneous expansion. The second lies in the ‘expansion of mercury’ statement. The reading actually measures the difference in expansion between mercury and glass (relative expansion, not absolute.) and because the glass, being on the outside, expands first, the thermometer actually drops first before it starts rising. A thermometer, like a language, is an instrument for understanding the world. When we use it for simple things, we can use simple language to describe what it does. But, for more advanced applications, we may need to refine the view of the thermometer.

System writers speak of ‘emergent’ properties of a system, properties that did not exist in the parts, but are found in the whole. Others attack this idea, saying that these properties are but another name for vital essence. They support their arguments with examples of emergent properties that turned out to be perfectly predictable. Both arguments are right, but they are in trouble because they speak in absolute terms, as if the ‘emergence’ were stuff in the system, rather than relationships between a system and an observer. Properties emerge for a particular observer when he did not or could not predict their appearance. We can find cases where a property is emergent to one observer and predictable to another. By recognising emergence as a relationship between the observer and whatever he observes, we understand that properties will emerge when we put together complex systems. The property of ‘emergence’ no longer emerges for us, though it surprises those who take the absolute view.

How can we avoid fallacies of absolute thought? Always remember the human origin of our models, words, instruments and techniques. Absolute thought is a simplification that serves well at certain times, on a certain scale of observation and for certain purposes. It works as long as we work following conventional patterns, in conventional situations. Page 62: … any system is the point of view of one or several observers. Whether our view – or their view – is good or bad can only be judged according to the purposes which the system is designed to satisfy.9

A System is a Set Even though any arbitrary way of looking a the world can be a system, we could not say anything about truly arbitrary sytems. So, we narrow our attention to non-arbitrary systems, forcing attention to the reaons for the non-arbitrariness. These reasons are the source of order that makes systems thinking possible. Non-arbitrariness has two sources. It could be ‘out there’ in the real physical world, or in the observer. We focus on the observer for the moment. We note that any way of looking at the system do not form an arbitrary system, for the way 9 In AIDA2, there are several types of observers designing a system. A system will consist of several points of view. The good/bad is based on the purpose of each of the types of observers.


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belongs to the mind of the observer. Arbitray systems are hard to find since as soon as we think of one, it becomes non-arbitrary.

The role of the observer is ignored in systems writing by, for example, moving into a mathematical representation of the system – without saying how that representation was chosen. For example, Hall and Fagen give the following definition: ‘A system is a set of objects together with relationships between the objects and between their attributes.’ No clue is given to where did the objects come from. We know that they come from the mind of the observer. While they emphasise the relationships as essential parts of a system concept, they fail to note that the system itself is relative to the view point of an observer.10

If systems are sets of things, set theory and its notations would be of great convenience. The mathematics of sets (set theory) tells much about the properties of sets, but nothing about how to choose them.

Observers and Observations We have so far been vague about what the set underlying a system is a set of. Hall and Fagen, engineers, made no bones about saying it was a set of objects. Others speak of parts, elements, attributes, components or variables. This implies that nobody knows. This diversity of names suggests that the members of a system set are one of the undefined primitives of system thinking. System thinkers talk about these members, but never say what they are. In fact, once we say what they are, we are no longer talking about systems in general, but about a particular system.11 As long as the members are not set, our theorising is strictly contentless – mathematical. A mathematical argument cannot be ‘true’ or ‘false’ but ‘valid’ or ‘invalid’. Valid means it is internally consistent. Once we set up a correspondence between the mathematical argument and something ‘real’, we can speak of the argument as being true for that correspondence. The mathematical view cannot distinguish between ‘sterile’ and ‘productive’ arguments. The hyper-mathematisation, or the generation of great mathematical general theories is a problem since the theories so general they cannot be applied to anything (sterile) and, on a mathematical level, they cannot be distinguished from productive theories. Page 69: … Let them make the effort to express these ideas in appropriate words without the aid of symbols, and if they succeed, … they will find themselves very much enlightened during the process…12. We avoid using mathematics unless we intend to use it more than once, justifying the effort of explanation of the idea. We introduce set theory to give ourselves a convenient way to talk about a delimited range of possibilities. We will use sets in the elaboration of out concept of observer. An observer makes observations, such as sensations on the sense organs, readings from instruments, etc. An observation is the act of choosing an element from a set, the set of all possible observations of that type for that observer. In other words, an observer may be characterised by the observations he can make. The set notations lets us recognise that there are two aspects of an observer – the kind of observations he can make (Scope) and the range of choices he can make with each kind (Range). So an observer can be characterised as a set of sets {Scope, Range}. The characterisation of an observer may be at once too narrow or too broad. Too narrow if we exclude some of his scope or fail to make the grain sufficiently fine.

10 Each observer type would choose its own set of object types and hence we need different points of view for different observers. The choice is based on the observers’ own expertise. 11 In Aida2, language designers do not talk about what the elements are, we just talk in general. 12 In aida2, we will use math once we have the intuitive feeling of things and the maths will be used to simplify. It has to make sense first.


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We may be either not aware or interested in certain possible observations or the resolution levels.13

A complete observation by an observer consists of one selection from each set in his scope. How many possible observations can be made? The set of all possible observations is the product set (Cartesian product) of the observer’s range sets. The product set may be too broad a model for the observer, where even though he can make each of the component discriminations, he may not be able to make all combinations. Certain elements in the product set may need to be excluded for a more precise description of the observer. By including such a broad characterisation of the observer, we are committing an error of assuming that the observer can observe things he may not be able to do. On the other hand, if we properly characterise his scope and the grain of each component, then the cross product model would at least not exclude any observations he might make. In this model of the observer, we shall remind ourselves how much computational capacity our model requires (Square law of computation).

We have no requirement that the observer be able to make individual observations ‘correctly’, because the observations are our primitive, undefined elements and the word ‘correct’ applied to them is meaningless. All he must do is to recognise 2 sensations as being ‘the same’ and he is the final arbiter.

The Principle of Indifference We cannot speak of an observation as correct or incorrect, but without a notion like that of ‘correctness’, it is difficult to say much about observers and their observations. Hence, we introduce the concept of ‘consistency’ which is the compatability of one set of observations with another. The notion does not depend on how the observer names the observations. Hence, the Principle of Indifference: ‘Laws should not depend on a particular choice of notation.’

But we are often fooled by the names of things. During and after a revolution, things are often renamed just to change thinking patterns. To put the principle into operation, we rely on mathematical symbols, which take the sting out of words. The first step in testing the consistency of 2 observers is to neutralise the form of their observations, giving each observation an arbitrary name. A is said to be consistent with B if A never gives 2 different symbols for one of B’s symbols, even if A does not, or cannot, make as fine grained observations as B (That is, B might specify a more accurate observation than A, and hence their observations are not the same, yet A’s observation is still consistent with B’s.). If A is consistent with B, we can always tell what A’s observation is once we know B’s. But, if A is consistent with B, it is not necessarily the case that B is consistent with A, since if B makes more accurate observations that A, knowing what A observers does not lead us to what B observes. Mathematically, A is consistent with B if there is a one-to-many mapping from A to B. A many-to-one mapping implies inconsistency. If A is consistent with B, A’s observations add nothing to those of B. An observer that makes more discriminations in a situation than another is said to dominate the other in that situation. Generally, neither observers dominate the other in all situations. We sometimes learn things from A that we could not learn from B, and vice versa. There is a many-to-many mapping between A and B’s observations. For example, consider A and B looking at a table, one from either adjacent side, at eye level to both A and B. If we toss a penny on table, they can both say if it is to their left or right, but

13 In aida2, an observer is replaced by a designer. An observation is equivalent to a particular design decision he can perform. This decision comes from the set of all possible decisions for that designer. Each decision type is concerned with a particular type of decisions.


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not how close it is to them and each can tell if the penny is on or off the table. A and B can agree if the penny is on/off the table. If the penny is on the table, we cannot predict what A says from that of B, and vice versa. If we use their observations properly, each will make a contribution to our understanding of where the penny lies.14

We have been assuming a special position for ourselves, our point of view. It is easy to slip into imagining that we can get ‘above the table’ when talking about other people’s viewpoints, but we really have no reason to believe that we have such super powers of observations. For simple cases, we can talk about different points of view if we are willing to introduce an explicit fiction – the superobserver. The superobserver needs to have enough viewing capacity which covers the abilities of the other observers (but not more). He needs to be able to dominate all other involved observers. A superobserver’s view must dominate the view of every other observer present. This dominance can be assured if the superobserver’s set of observation states is the Cartesian product of all of the others (It is all possible combinations of observations.). But, in many cases, only a subset of these combinations is needed. The superobserver powers, though finite, grow much faster that the other observers. Combinatorial growth is a critical flaw in any discussion of multiple points of view, for though we can imagine that a superobserver might exist, there is little chance of having one in complex situations. We must particularly refrain from imagining that WE are the superobserver, capable of seeing what ordinary mortals cannot.15

5.2.4 Interpreting Observations

States Imagine that you walk into a strange room with a big black box, and that you are a super-super-observer. That is, you can dominate ‘any’ other observer. (The concept of super-super-observer is like the concept of ‘reality’, in that it contains all possible observations.)

The things you observe on the box are a red light (R), a green light (G) and a whistle (W), which is the scope of your observation S = {R, G, W}. The lights can be either on or off, (notation 1 or 2), which we call the ‘states’ of the light. The range of the light observations is hence, R = (1, 2) and G = (1, 2). The whistle can have one of six tones, W = (1, 2, 3, 4, 5, 6). The cartesian product of your ranges produces all possible states of the box, such as a = (1, 1, 1), b = (1, 1, 2), …, j = (1, 2, 4), etc, resulting in 2x2x6=24 possible states.

You note the sequence of observations of the box which happens to be … a n i k a n i k a n i k … Luckily, there is a great deal of regularity, or constraints, in the sequence, otherwise there will be a lot more writing to do. How much more writing? One cannot ask ‘how many possible sequences are there?’ since a sequence can be indefinitely long, and there will be an infinite number of possible sequences. But, one can ask ‘how fast does the number grow as the length of the sequence grows?’ If there are 2 observations in a sequence, the sequence is a pair of choices for the set of 24 states, and all possible pairs is this the product set which has

14 In Aida2, we generalise the concept to involve types of observers. Each type of observer/designer/discipline makes his set of observations and there may be overlap in common observations/properties. Common observations need to be consistent. However, each observer type dominates the other in one way but is dominated in other ways. If we use these observations properly, we can learn more about the system. Combining these observations, we can reach further understanding of the system (emergent properties) that neither observer could have contributed to. This is the role of x-disciplinary analysis. 15 This is a good argument against having a single super-model that has all properties as opposed to having multiple-models for each observer/discipline. In aida2, we refrain from thinking or having a super discipline. Every thing is always distributed. An analysis view might combine parts from different views but is not seen as a superobserver of these views, since it only takes a small set of properties from each view.


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24x24 members. Therefore, there are 256 possible sequences of length 2, or (24 power n) sequences of length n. A superobserver would need a super memory if he’s to remember everything he sees, or else be lucky and see highly constrained sequences. Being constraint, one can use compact means of recording the observations as a mapping from one observation to the one that follows it. If the mapping is not one-to-many, there are no ambiguities, and the behaviour of the box is predictable after just one observation.

So far, you have been a passive observer. Although you are omniscient (all seeing), you are not omnipotent (all powerful). As a superobserver, you have no power at all (impotent). You have been playing a game called ‘black box’, where the observer is impotent to manipulate the box by looking ‘inside’. The black box models an observer who cannot or will not influence the system to be investigated, such as an astronomer studying the universe. Human beings often interact with the systems they observe. We may believe the world to be independent of the percipient observer, but we definetly feel it depends on the participant observer.

Let us suspend the black-box rules, and endow you with limited powers of interaction. You touch a spring and a little door opens. Inside the door, a sign says ‘KICK ME’. So, you give the box a tap. Instantly, the pattern of light and sound changes and we see … g m d f g m d f … You then give the box a bolder kick and get … b j r c q h p l o e b j r … Further kicking fails to produce any other behaviours than the three already produced.

The box is further examined independently by two other observers. A friend, upon observing the box, concludes that it only changes between two sequences of states, and that it is not determinate in its behaviour. A stranger, the inventor of the ‘music box’, agrees that there are three sequences, but that the last sequence is 5 states long.

To settle the disagreements, all observers walk into the room together. There, the inventor explains that the music box plays three national anthems, and to get the music box to change tunes, all you have to do is to ‘kick it’, which actually means ’yell at it’. When he demonstrates this, everyone declares in unison: ‘See, it works just the way I said’.

The Eye-brain Law After experiencing being a superobserver, you should never be surprised to see something that others do not see. By applying the ‘principle of indifference’ to ‘you’ and ‘others’ in the previous sentence, we get another insight that is a little harder to accept.

Why where there disagreements between you and the other observers? The inventor explains that the box plays six different notes, which you know, but is a revelation to the friend, since he is slightly deaf and can only hear three notes. When you ask about the lights, the inventor replies, ‘what lights?’ When you show him the lights, he says that they are nothing to do with the box. As long as one of them is on, everything is OK.

Now the mystery is cleared. Since the inventor ignores the lights, he sees only six states – the six notes. To him, the purpose of the box is known, which means that he does not have to discriminate as many state as the superobserver does. 16We could map your superview onto his (Each of your states maps to one of his states), but not visa versa (Each of his states maps to more than one of your states). The structure of the two views is also different. For example, the third sequence of ten states maps to a sequence of 5 states in the inventor’s view, traversed twice. Another difference is that the superobserver’s view is ‘state determined’ whereas his is not since each state is not always followed by the same state. The inventor, for example, may 16 AIDA2: Note the relation between the purpose and ignoring certain states in the previous two sentences. Based on the purpose the designer has, different aspects of the system are of interest, and other aspects are ignored.


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see an A or a D state after state A. But, if the inventor can remember the previous two states, he can predict the next state. The substitution of mental capacity for observing power is an illustration of a general law about observers, called the Eye-Brain Law: ‘To a certain extent, mental power can compensate for observational weakness.’ Through symmetry, the Brain-Eye Law is: ‘To a certain extent, observational power can compensate for mental weakness.’

As examples of the Eye-Brain Law, the experienced doctor needs fewer laboratory tests than the intern to make the same diagnosis, but the intern can substitute a good laboratory for the years of experience lacking. This Law will not work if there are no constraints at all on the observations. Memory is of no use unless the future is like the past.

Although the superobserver sees the fine details, he may miss the big picture. See-it-all does not mean know-it-all, for knowing means knowing how to ignore certain details.

A state is a situation that the observer can recognise if it occurs again. Discriminating too many states has been defined as undergeneralisation. Scientists are envisioned making the most precise measurements as a basis of theories, but in practice, they are lucky that the measurements are not overly precise. Newton based his law of universal gravitation on certain observations, which if they were made more precise (as precise as can be made today), his work would have been much more difficult. The balance between ‘eye power’ and ‘brain power’ cannot be pushed too far in either direction. The problem of science is to find the appropriate compromise.

The Generalised Thermodynamics Law Page 99: Galileo distinguishes between primary qualities of matter and secondary qualities – the former inherent in matter itself, the latter the product of the interaction of the body possessing certain primary qualities with the sense organs of a human or animal observer. As systems get more complex, the divergence of views between different observers increases. However, there may be situations in which the system exhibits behaviour to which all observers agree, despite their different observational powers, since observations do not entirely depend on our observation characteristics. This is put into the Generalised Thermodynamics Law: ‘More probable states are more likely to be observed than less probable states, unless specific constraints exist to keep them from occurring.’, which can be reframed as: ‘The things we see more frequently are more frequent: 1 because there is some physical reason to favour certain states (first law) or 2 because there is some mental reason. (second law).’

Given raw, detailed observations of the world, no two situations would be exactly alike, and hence no state will ever occur again unless we lump many states into one. Thus, in order to learn anything, we must forgo some potential discrimination of state, some possibility to learn everything. This is the Lump Law: ‘If we want to learn anything, we mustn’t try to learn everything.’ If psychologists saw every white rat as different, there would be no psychology. Science deals with repetitive events and each science has to have ways of lumping the states of the systems it observes, in order to generate repetition.

Functional Notation and Reductionist Thought The blackbox model of observation gives a passive view of the investigation process. The observer is not allowed to change the box, but he can decide on its scope and range of observation, based on what he believes are the important features of the system. Mathematically, this can be represented in ‘functional notation’ such as z = f(a, b, x), stating that z depends on, and only on a, b and z as far as we know or care at present. This notation is important in systems thinking since it allows us to present ‘partial knowledge’ about a system we do not know how to describe its behaviour exactly. Newton might have simply said F =


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f(M, m, r) before he could give the exact form of the gravitational forces between two masses. Functional notation can be mixed with explicit formulas to show ‘intermediate stages of knowledge’, for example F = g(m, M)/r2. Similarly, incomplete knowledge can be denoted as F = h(m, M, …)/r2.

Given a function T = f(W, I, t, D), we might desire to further refine the model by expressing D as a function of other quantities, even if we do not know what these quantities are. We can use functional notation to show our intentions to do so, by writing D = g(…).The form T = f(W, I, t, D) implies that the quantities in parenthesis are independent, in the sense that for the given discussion, we are not interested in the functional dependencies of these quantities, even thought they can themselves be dependant on other quantities. On the other hand, functional composition such as T = f(W, I, t, g(…)) shows the deeper levels of dependencies in a compact form.

The notation of decomposition of functions is appealing, since science explains by reducing one phenomenon to the terms of other phenomena. There are two main fallacies that can be committed during this reduction. A scientist may commit a Fallacy of Incompletness by omitting some quantity from one of the functional relationships, such as stating z = f(x, y) when it really is z = f(x, y, …). Secondly, even if the view is complete, the reduction must eventually stop either because of the limited capacity of the observer, or because the situation will not admit further reduction. Such a limit leads to a situation called ‘complementarity’ of observation.

Incompleteness and Overcompleteness What does it mean for T = f(a) to be incomplete? This functional relationship between T and a stands for an infinite set of equations of a, and speaking of the relationship as ‘wrong’ means that the specific equation of T is not in this set. This can occur in one of two ways: either T does not depend on a – overcompleteness – or T depends on something in addition to a – incompleteness. This can only be concluded from the observation of the behaviour of T and a.

If T sometimes changes while a remains constant, then we have to conclude that either T depends on something other than a or that T or a are measured incorrectly. We hence either expand our relationship to T = f(a, …) or refine our observations. On the other side, overcompleteness occurs if we observe that T does not depends on a at all, where T remains constant irrespective of the value of a.

Consider the more complicated system where T = f(a, b, c), and where T remains constant for a finite set of varying values of a, b and c. Is T not varying because T is not dependant on one or more of the quantities, or because the effects of the quantities cancel each other out? Given the finite set of observations, we cannot discriminate between the two cases. The box is black and we cannot see inside to say which is the ‘true’ structure.

Consider the earlier observations of the black box at the start of the chapter. The observations of the superobserver can be describe as St+1 = f(St) where St is the state observed at time t. His observations are ‘state-determined’ since the state at one instance is completely determined by the state at the previous instance. The inventor’s observations are presented as Vt+1 = h(Vt, …) because his views are not state determined. To make his view state determined, he could expand his impression of what a state is to include the lights, or he would need to observe two successive states, giving Vt+1 = h(Vt, Vt-1). Being superobservers, we can talk about this expansion of observation or memory power, but they do not have the information needed to make the choice. The black box, through its behaviour, tells us that our view is incomplete, in the sense that it is not state determined. It cannot however tell us how to complete this view, and we are left with the arbitrary choice between different views that fit the observations.


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Two models that fit all observed data are said to be ‘isomorphic’. (Mathematically, the two models would have to fit all ‘possible’ data, but we use the term in a more limited sense.) Black box observation, once it no longer yields new observations cannot resolve this isomorphism unless the box is opened. And opening the box means decomposing/reducing one step further (Until one cannot reduce no further and the problem of complementarity between the given isomorphs arises).

At any level of observation, the choice of isomorph is strictly up to us depending on our memory capabilities, previous knowledge, etc. This arbitrariness ensures that different observers will have a multitude of ways in which to interpret their observations, not just because of the different choice of isomorph, but because of what is to be observed as of primary importance.

The Generalised Law of Complementarity The second reason for the failure of reduction is that of the problem of complementarity.

Consider the experimental setup in which we would like to know the speed and position of a car from the single observation of a photograph. Since the car is moving, its velocity is to be deduced by measuring the blur of the photograph. At the same time, getting an exact value of its position requires that the shutter speed of the camera is decreased in order to reduce the blur in the photograph. Notice the complementary nature of this method. Getting an accurate velocity measure will reduce the accuracy of the position and vice versa, and hence whatever shutter speed we choose will involve some compromise. A different observer, who sets his shutter speed differently, will see a different – or complementary – picture. One way to escape this complementarity is by using more refined measurements such as a less grainy film, that is, by further reduction. By this refinement will eventually reach an end, and we must content ourselves with complementary views. The views of our two observers of the earlier blackbox, while viewing the same situations, are complementary. Neither view can be reduced to that of the other, nor were the views entirely independent since certain things can be derived from each about the other. This is the idea of complementarity: two mutually irreducible points of view that are not entirely independent.

Note that complementarity between two views does not only occur when the reduction of the two views cannot be further performed. If, for whatever reason, observers do not make infinitely refined observations, then between any two points of view, there will generally be complementarity. This gives the General Law of Complementarity: ‘Any two points of view are complementary’.

It is not always necessary to remove complementarity between two views. An economist and a sociologist looking at the same system, although there will be some correspondence between their views, do not care if their views are reconcilable since they are aware that they are looking at different things. Even two economists, although it might be possible, might not bother to reconcile their different observations by reducing their observations. Page 121: Reduction is but one approach to understanding, one among many. … Reductionism is an article of scientific faith, for nobody has ever observed the final reduction of any set of observations. … Reduction sometimes works, but we must admit that other methods sometimes work too. Because we are scientists, we believe that our methods will work more often, but there is no hard scientific evidence for that – only faith.

5.2.5 Breaking down Observations We will here discuss how the limited mental power of the observer influences the observations made. Consider again that you are a superobserver of the earlier blackbox, and you again see the states a through x. Given that you have limitless mental power, you observe


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that the system has a 20 state cycle. However, having been drugged by the inventor, you are only capable to remember the last 10 state transitions of the system, and hence are no longer able to see the cycle. In order to deal with this problem, you decide to narrow your view and only observer the two lights, since that reduces the number of states to 4 and hence increases your chances to remember the complete cycle. You now succeed in seeing the state determined, but smaller system. You try also to only focus on the tones and succeed in observing the complete cycle of 6 states. You have actually invented a new way of looking at the world, by decomposing the system in two independent parts, each of which is state determined, at the expense of having to deal with 2 smaller systems instead of one. But given that you have limited mental powers, the decomposition into independent parts enabled you to predict the system behaviour better. Page 134: This is the method of science, which would be unnecessary has it not been for our limited brains. Can a system’s behaviour always be decomposed into independent parts? This depends on the set of qualities being observed. A more appropriate set of qualities of the same system, might allow a better decomposition, and hence a more simplified view, of a system. A physicist recognises entropy and density; the chemist, valence and PH; or the economist profit and marginal utility. Depending on the choice of properties, your understanding and grasp of the system will hence vary.

The Metaphors of Science Trying to cope with complex phenomenon, we try to: 1. get a ‘complete’ view, broad enough to encompass all phenomena of interest; 2. get a ‘minimal’ view, by lumping states that are unnecessarily discriminated so we do not overtax our observational powers; 3. get an ‘independent’ view, decomposing the system into independent qualities, so to reduce the mental effort required. While these goals are often met, the resulting view may not be ‘natural’ or ‘satisfactory’, since it may not conform to the psychological categories we have either inherited or learn from the past. Our limited mental powers do not allow us to carry a different view for every moment of our lives, and we hence need to fit this view into earlier experiences. We are like a handyman that carries a single toolbox to perform electrical, carpentry, or whatever work. From time to time, a tool may be replaced by another, if it is found to be more generally useful in the future. This decision is based on the assumption that future work will be like those received in the past. By studying the past, the handyman may be able to develop a more useful box of tools. This assumption is based on an article of faith, the Axiom of Experience: ‘The future will be like the past, because, in the past, the future was like the past.’ This can be rephrased as a definition of the word ‘like’: ‘Two things are alike if one in the present can be substituted for one in the past’.

Consider poetry, whose essence is the metaphor, talking of one thing in terms of the other. (Burns: My live is like a red, red rose) This works since we know, or feel we know, some properties of one thing that we can transfer over to the other. We may not know how Burns feels about love, but we do know how it feels in the presence of a red rose. In this comparison, Burns depends on the universal experience of roses and colour perceptions. Page 142: One of the problems of specialisations of the sciences is that scientists in different fields have few common experiences to serve as the basis of communication. 17

Science and poetry are much alike. Scientifically, a metaphor is like a function. (loved one = f(rose, …)). Like a poet, a scientists starts with a complete view, then refines and simplifies it, reducing the original function to a function of other things. The ultimate reductions are finally

17 In Aida2, we will use the tools described later in this chapter, as common experiences for communication between engineers.


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assumed known and left undefined. These ultimate reductions must be rooted in observation of the world. Page 143: by examining the metaphors of science, we can learn about the limitations of the brains that do science.

Boundaries and Things One of the most deeply buried metaphors of science is the concept of a ‘thing’ or ‘part’ that can be cleanly separated from other things or parts. This metaphor is so deep that we seldom know that we are using it. The anthropologist speaks of the ‘social organisation’ of a tribe as if it were a box of matches he could carry around in his pocket. These things are the possessors of ‘properties’ or ‘qualities’ that they carry around with them, and can be isolated from other properties by isolating the thing from other things.

Our use of the ‘thing’ metaphor is closely allied to our experience of physical space, and particular to our experience of ‘boundaries’. On the surface of the earth, we can draw a line around something and easily discriminate ‘inside’ from ‘outside’. By analogy, we apply this concept to all our systems, using the term ‘system’ to mean ‘inside’ and ‘environment’ to mean ‘outside’. By the Principle of Indifference, we can call either one the ‘system’, for one man’s system may be another man’s environment.

Not all systems exist in the physical world. Even so, we already encounter difficulties of reasoning when we are dealing with systems with physical boundaries. We choose easily recognisable physical features such as a sharp change of colour, difference in texture, where solid meets a liquid, etc. We commonly consider the hair to be part of our body, because it is attached to it. For the physiologist, however, hair is better thought of as outside of the body, since it has been secreted from the body and does not take part in the body’s physiological processes. It can hence be treated like other excrements such as perspiration and urine. Our choice of boundaries makes a difference in the effectiveness of our thought. Problems arise since our choice of boundaries is generally influenced by previous experiences, which have been excellent guides most of the times, but lures us when the boundaries are not well-defined.

A system boundary may not be infinitely thin, and hence can partake of both system and environment. Rather than separating, such a boundary ‘connects’. System thinkers use the term ‘interface’ to describe that type of the world that looks both inside and outside at the same time. ‘Interface’ is a more useful word than ‘boundary’, for it reminds us to pay attention to the connection, and not just the separation, between system and environment.

Conventionally, a part is represented on paper as a closed region surrounded by a boundary, while a connection is represented as a line or arrow. Graphs with bounded boxes are useful in systems thinking. But, not all systems can be separated from their environment in a sharp, clean way. While the graph implicitly says that the system has sharp, sharp boundary, we are really saying the same kind of thing as ‘My love is like a red, red rose’. As scientists, if we are to make more specific conclusions about a system, we would need to progress to a more precise description of the separation. Moreover, our brains are limited to about 15 boxes at a glance, and beyond that we would need further support, which will come from several directions.

Qualities and the Principle of Invariance We cannot explain what we mean by a certain ‘quality’, except by pointing to the states which have different values of this quality. We call such a definition by pointing ‘ostensive definition’. We might explain one set of qualities in terms of another, but we should remember that the primordial set is obtained by ostensive definition.


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Qualities have a mental function for observers with limited memory. We may think that certain qualities are more ‘natural’ than others, but this actually means that we are more accustomed to observing in those terms, since it has been found more useful to observe in those terms. As we work in less familiar situations, our learned capacities become less effective. A quality is a way of grouping states of a system. For example, the quality of mass is defined by the states in which masses are the same or different. The ‘sameness’ and ‘difference’ operations allow us to identify and explain the quality in question. If we want to ‘measure’ the mass quality, we have to introduce another operation besides ‘sameness’ and ‘difference’ for states, such as ‘greater than’.

Physical scientists differentiate between ‘extensive’ and ‘intensive’ qualities depending on what happens to the quality when the system is divided into parts. An intensive quality is one which maintains the same quantity after the system is divided such as density, while an extensive quality depends on maintaining the full extent of the system, such as mass. These concepts are defined ‘relative to some act of breaking’ of the system. For example, the density quality of a chocolate block is extensive when related to the act of cutting the block in half. However, if the block is divided into its chocolate and peanut parts, then the densities would be different. The definitions of intensive and extensive qualities can be turned around to give a definition of ‘breaking into parts’: ‘If the intensive properties remain the same, then you have probably broken the system.’.

More generally, if the chocolate block is divided into the qualities of flavour and consistency, then neither part have any density at all. ‘Breaking into parts’ can be generalised into ‘transformations’, and we can hence derive the more general Invariance Principle: ‘With respect to any given property, there are those transformations that preserve it and those that do not preserve it’, or restated in terms of transformations: ‘With respect to a given transformation, there are those properties that are preserved by it and those that are not’.

A quality may be characterised by the transformations that preserve it, or a transformation may be characterised by the properties it preserves. In general, we cannot say precisely what we mean by a certain quality because there are an infinite number of possible transformations that can be performed. The Principle of Invariance can be restated: ‘We understand change only by observing what remains invariant, and permanence only by what is transformed.’

Partitions

As an example of the division of a system into parts, consider the act of dividing the system’s behaviour into qualities. A partition is defined by a set of ordered pairs of the parts (states), in which the relation between each pair describes the relation ‘has the same value of the quality’. Hence, the set consists of the Cartesian product of the set of states that has the given value of that quality. In order to define a ‘sharp’ partition, there exists three mathematical conditions that must be satisfied.

Clearly, a quality will not satisfy our idea of a quality if we cannot consistently identify it with a particular state. If a state x does not have the same value of the quality as the last time that state was observed – the pair (x, x) is not in the set – then the whole idea of quality breaks down. This leads to the mathematical condition for describing a partition, and thus describing a quality: ‘For every state x, the pair (x, x) must be in the relation. This is the ‘reflexive’ condition. When the partition describes a quality, it means that the quality cannot shift back and forth with time while the state remains the same. If we start with the idea of qualities, we identify states by the shifting of quality values. The reflexivity condition prevents us from the erroneous absolute thinking, of taking a relative quality as an absolute one. For example, if we attempt to divide a village into groups of ‘cousins’, the attempt is faulty because this property


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is a relation between two parts, and not an absolute property. This error is exposed when we notice that one is not a cousin of oneself.

The second property a relation must have to fit our intuitive notion of a quality is ‘symmetry’. If state x has the same quality value as state y, the it should also be the case that state y has the same quality value as state x. Consider trying to partition a village into groups of ‘friends’, and assuming that reflexivity is satisfy by assuming that one is one’s own friend. In certain understanding of the ‘friend’ quality, even if A considers B as a friend, it is not necessarily the case that B considers A as a friend.

The third condition is that of ‘transitivity’. Considering a symmetric definition of the ‘friend’ property. Even if A is a friend of B, and B is a friend of C, then for transitivity to hold, then A must be a friend of C, which is not necessarily the case. Transitivity may not hold with qualities involving graininess, that is qualities in which the sensing device has a minimum resolution level under which it cannot detect differences between two values. For example, if A is slightly more blue than B, and B is slightly more blue that C, such that a human cannot detect any of these differences, while the difference between A and C is noticeable, then an observer may classify A as the same colour as B as well as B as the same colour as C, but A as not the same colour as C. Resolution levels are part of any measuring process. With graininess transitivity may not hold, and hence there is no complete partition, no clear division of system into subsystems, no clear separation of system and environment.

5.2.6 Describing Behaviour

Simulation – The White Box In order to understand a system, a black box approach can be taken in which the system could only be known through observing its behaviour. The white box, or simulation, is another approach in which the inside of the system is perfectly revealed, and another system can be constructed to reveal its behaviour. But, as we shall see, because of our limitations, no box is ever entirely revealed to us.

If we can build a system that appears to behave in the same way as a system we claim to understand, our claim will gain some strength. But, we can never be sure that the simulating system captures all the properties of the studied system. System can be simulated by building scale physical models, such as in ship building and planes, or using ‘analog computing’ in which electrical circuits model the system under study. A digital computer, with its advantage of being programmable, is a more accessible simulation tool.

For a simulation to demonstrate understanding, we do not simply build a model that mimics or copies the system, but assemble a model from smaller number of parts from which the behaviour is generated.

Just building a white box of a system does not guarantee that we understand all of the system’s properties, but once the property has ‘emerged’, the white box makes it easy to uncover the source. But, without observing the behaviour – black box view – we may not have seen the property at all.

State Spaces

When dealing with systems with a large number of states, new tools, other than drawing the different states and the movements between them, are needed to represent them. If the system can be composed of two qualities, we can draw the states using Cartesian coordinates, where there is a place for every state, and every state has its place. Note that the assignment of state of a two-variable system to points in a physical plane is arbitrary. Certain arrangements may


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appear to yield a continuous line of behaviour, but we should remember that this appearance is a consequence of our assignment of numbers to quality values. Of course, we may want to find such an assignment so as to reduce our effort at describing the behaviour. Generally, a system with n qualities can also be mapped onto an n-dimensional space, called a ‘state space’. Operations such as ‘projection’, ‘sectioning’, ‘dimensional reduction’ can be performed on such a space producing a ‘section’, ‘project’ or ‘subspace’ respectively. Such operations may be useful in order to reduce the system complexity or the mental power needed, at the expense of loosing certain information.

The opposite of projection is that of ‘expansion’, in which a new dimension is added. This may be necessary when studying a system, after discovering that a certain variable is missing. Old work needs not be thrown away, since it becomes a projection of the new set of dimensions. The need for a new dimension may be discovered when realising that the line of behaviour of the system seems to cross itself, which indicates that something is wrong with our point of view if the system is to be state determined. A crossing represents two different paths emanating from the same point. Note however that in a projection, a crossing poses no problem. For behaviour represented in a state space, we have the Diachronic Principle: ‘If a line of behaviour crosses itself, then either the system is not state determined, or we are viewing a projection – an incomplete view.’.

Projections and other transformations help us overcome the limitation of our brains to handle many dimensions. Another way of handling the surplus of dimensions is by introducing the dimension of time. Time has the property of always moving in one direction, and hence ensures that no cycles or crossings occur. The addition of time also allows us to project each other dimension onto a two dimensional graph as a function of time. Page 196: If you cannot think of three ways of abusing a tool, you do not understand how to use it.

Behaviour in Open Systems Scientists prefer to study a state-determined system because its behaviour is simple, and this determinism is created by trying to fully enclosing the system. Indeterminism, or ‘randomness’, may be seen in the system either if the qualities observed are not complete, or if the enclosure is leaking and hence the system is open. An observer has no way of determining the cause of this randomness. A state determined system is represented by St+1 = F(St), while a ‘random’ system is represented by St+1 = F(St, …), where the ‘something else’ is unknown to the observer. We might as well say St+1 = F(St, Rt) giving the name R for that randomness. This is exactly the same form we would give an open System St+1 = F(St, It), where I is the ‘input’.

From this point of view, any closed system is state determined. Every finite state determined system has cycling behaviour since eventually a state, Sx, has to be reached again and the cycle starts again. When we see cycling behaviour, we suspect that the system is uninfluenced by external factors, or influenced by cyclic external factors, and that the external factors are too small to influence the system. The closed system fiction is a useful heuristic device. If we see non-cyclic behaviour, we look for an input. If we insist that the system is closed, we will assume that there exists ‘randomness’ in the system, saving the effort of looking for inputs or trying to make the system description more complete.

If we partition a state determined system into a ‘system’ and an ‘environment’, in general, the ‘system’ part will no longer be state determined, since depending on the inputs from the environment the behaviour of the system part varies. An open system has normally not a single line of behaviour, but a set of behaviours selected by the input. We speak about the set of behaviours of an open system as ‘Behaviour’ – capital B.


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Given that the behaviour of an open system is influenced by inputs from the environment, as well from its own behaviour, an observer cannot tell the reason for certain behaviour, unless he has white box knowledge of the system. This leads to the principle of Indeterminability: ‘We cannot with certainty attribute observed constraint either to system or environment’

We prefer to think and create our systems to be as closed as possible. Openness complicated prediction and observation, yet at the same time, it lets us gain predictability by allowing us to act on the system.

5.2.7 Some Systems Questions Page 227: R. W. Gerard: … But the real shift here is from a focus on organisation to a focus on action, from being to behaving, from form to function, from pattern to process, from the timeless to the temporal. Being is the cross section of an entity in time, and those aspects of the organisation which appear relatively unchanged in a series of such instants constitute the essential structure of the entity or organism. Invariance in time helps to identify the significant units of a mature system. Conversely, along a longitudinal section in time appear the transient and reversible changes, often repetitive, that constitute ‘behaving’ or functioning, and the enduring and irreversible changes, often progressive, that constitute ‘becoming’ or developing. And with this shift in time there occurs a shit in entity of concern – from an object, a pattern of matter in space, to a behaviour, a pattern of events in time.

We have discussed the ways we picture ‘being’: the notion of set, diagrams of structure, properties, boundaries and the white box; and behaving: state spaces, chronological graphs, input, randomness and the black box. We have also studied the relationship between being and behaving – how particular behaviour leads to the inference of particular structure through the extraction of ‘properties’, and how particular structure leads to the production of particular behaviour through the execution of ‘programs’. We have investigated the role of the observer in these things with the conclusion that we, as observers, are entangled with what we observe, entangled in ways that leave ultimately indeterminable which is being and which is believing.

We believe in the Law of Effect: ‘Small changes in structure (white box) usually lead to small changes in behaviour (black box)’. We believe so because we live in a world surrounded by systems whose structure is controlled to a much larger extent by the manner in which they might fail and by the precautionary measures which have been taken against their failure. Because of our belief in the Law of Effect, we tend to partition systems into a fixed and a variable part, in which the fixed part – or structure – is the ‘source’ of its behaviour. We partition the system into two sets of variables, P and V. In V we see the ‘behaviour’, or ‘function’, which is the variable functioning of the permanent ‘structure’ P. Although we may identify the system by its functioning, this is only a convenience, for the ‘real’ identity lies in the ‘structure’. Complementing this ‘structural’ view, is the ‘behavioural’ view, which says that the only way we know ‘structure’ in the first place is by observing behaviour. From this view, the Law of Effect can be restated as: ‘Small changes in behaviour will usually be found to result from small changes in structure’. These are two complementary ways of looking at the world.18

We are perfectly entitled to identify systems in any way we choose. The recipe for effective thinking is to use those ways of identifying systems that focus on what interests us, and to discard those ways that do not. There exists a mental cost of having a viewpoint too far out of touch with the ‘realities’ – either of the world out there or of the observer’s own mind. This gives us the Used Car Law: ‘A way of looking at the world that is not putting excessive stress

18 In aida2, there should be really no precedence or favouritism between the structural and behavioural models of our systems.


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on an observer need not be changed’ or ‘A way of looking at the world may be changed to reduce the stress on an observer.’19

5.2.8 Further readings 1. Specific examples of the application of GS are found in Ludwig von Bertalanffy and

Anatol Rapoport Ed., General Systems Yearbook. Vols 1-19. Ann Arbor: Society for General Systems Research, 1956-1974.. Look for one about engineering.

2. Look at www.fes.uwaterloo.ca/u/mbldemps/systems/systemdef.htm for system definitions

19 In Aida2, we adopt the principle that we want to reduce the stress on the observer, by choosing view points that best fit their view of the world.

http://www.fes.uwaterloo.ca/u/mbldemps/systems/systemdef.htm

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Views on General Systems Theory