Views on General Systems Theory

TRITA-MMK 2005:10 ISSN 1400-1179 ISRN/KTH/MMK/R-05/10-SEViews on General System Theory by Ola Larses and Jad El-khoury MMK Technical Report Mechatronics Lab, Department of Machine Design Royal Institute of Technology, KTH S-100 44 STOCKHOLM Stockholm 2005 Technical Report TRITA-MMK 2005:10 ISSN 1400-1179 ISRN/KTH/MMK/R-05/10-SE MMK 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 Contents 1 2 INTRODUCTION ....................................................................................................................................... 5 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 4 5 GST AND THE MOF................................................................................................................................ 14 REFERENCES .......................................................................................................................................... 16 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 3 Ola Larses and Jad El-khoury Views on General System Theory 4 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.2 Problem-solving application of systems thinking 3.1 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.2. shown in figure 1. two books from authors providing their views on GST (Weinberg 2001.1 Theoretical development of systems thinking (formulation of GST) 4. The general systems theory (GST) was established as a field of research in the 50’s. Checkland 1999) and one text book covering several theories on the subject (Skyttner 2001). The structure of the report consists of two major parts. Then. 1 The Systems Movement 2. 2. and what the core of the theory actually is. the basic concepts of the theory are given followed by a discussion of how the concepts relate.1). as well as a reflection on how GST relates to current meta-modelling efforts exemplified with the UML-MOF. The study was initially performed in the fall of 2004 and concluded in the spring 2005. However. 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).Ola Larses and Jad El-khoury Views on General System Theory 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).2 Application of systems thinking in other disciplines 2. The most commonly referred father 5 .3 Soft systems thinking 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 2.1 Hard systems thinking – Systems engineering 4. The first part provides a general overview of this broad field of science.2 Decision making systems 4. specifically the branch of theoretical development (box 3. the focus in this report is on the study of systems as such (box 2.1 Study of systems as such 3.1). It gives a short summary and overview of the General System Theory is. Checkland (1999) provides a map of seven enumerated subactivities in the systems movement. SST is related to social sciences. situation-driven and in interaction with the system. GST have strong bonds to Cybernetics.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. with the name later changing to the “International Society for General Systems Research”. (Buede 2000) Today. in the introductory retrospective section of his book he is content with separating hard and soft systems thinking. Information theory and Control theory. The first attempt to teach systems engineering as we know it today came in 1950 at MIT by Mr. 1954 in Berkeley. For these problems Checkland proposes a “soft” methodology. System theories are still a topic of high interest discussed in academia. The basic ideas of SST are summarized by Flood (2000). Bertalanffy founded the “Society for the Advancement of General Systems Theory” together with Kenneth Boulding in 1954. not sequential. systems engineering is promoted by the International Council on Systems Engineering (INCOSE) formed in 1990.Ola Larses and Jad El-khoury Views on General System Theory of GST is Ludwig von Bertalanffy. and the Systeemgroep Nederland. The soft systems thinking (SST. Some discussions still focus on the development of GST such as the journal of Systems Research and Behavioural Science. and still others develop systems engineering. In this problem oriented approach model building (capturing abstract activities and issues) is in focus. 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. Other communities have adopted the application of systems thinking. The hard systems thinking approach (box 4. they are goal-oriented. The term systems engineering dates back to Bell Telephone Laboratories in the early 1940s. 6 . However. box 4. The organization is today known as the “International Society for the Systems Sciences” (ISSS) and celebrated their fiftieth anniversary in 2004. The exact details of the model describing the system are in focus. The first significant systems engineering was performed for telephone systems to ensure that all the different parts of the phone system interoperated reliably. According to Checkland SST should be used iteratively. In 1956 the organization was renewed as the “Society for General Systems Research”. there are however a range of contemporary scientists who contributed in the field. and also for some human-made systems where there exist conflicting goals and purposes.3) is the approach developed by Checkland himself. This branch also has old roots. A mapping of the mentioned organizations and journals to the framework of Checkland is provided in figure 2. The actual details of the model are of less interest. The more applied problem oriented systems thinking (box 3. 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. The founders emphasized their desire to promote the unity of science at the very first meeting which took place in December. California. such as the rapidly growing INCOSE. Gilman. methodology-driven and intervening with the system (as hard systems thinking). Bell labs performed major applications of systems engineering during World War II. Director of Systems Engineering at Bell. This assumption holds for human-made systems in general but breaks down for human activity systems.2) is given three branches by Checkland (shown in figure 1). “Hard” methods like systems engineering assumes that the system have a clear purpose and is optimized towards this purpose. The links were accessed April 2005.1. and will never be really finished. artists. http://www. the task is enormous. Also. org philosophers. Of course. the webpages discussing the different components of our philosophy have been regularly expanded and updated. The ISSS is a broadly based professional society of scientists.Ola Larses and Jad El-khoury Views on General System Theory The Systems Movement 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) Problem solving approaches (application of system theories) Soft Systems Thinking Soft Systems Modelling Social sciences Peter Checkland Hard Systems Thinking Systems Engineering Contemporary Organizations: International Council on Systems Engineering (INCOSE) Journals: Systems Engineering Figure 2 Some active players in the systems world 2. and many other professionals from diverse endeavors. Looking at the different communities and their varying angles on the subject shows the dispersed body of theory and the lack of consensus. communities and courses Looking for the current activities in the systems movement a few communities with immediate references to GST can be found. educators. Since then. humanists. we invite you to join our efforts and become a contributor.v The project has started in 1989.be as a website happened in 1993. -homepage- International Society for Systems Sciences (ISSS) 7 . writers.ac. futurists.1 Current references. and its first implementation ub. If you are interested in our Project. who are drawn together by a common interest: understanding and interacting systemically with reality. there are several web-based sources of information regarding the subject. our conceptual system gradually widens. Table 1 General Systems Resources Site name Principa Cybernetica Web Organization Principa Cybernetica Project Link Brief descriptions from the sites http://pespmc1. business and policy practicioners. A selection of web-sites related to GST is provided in table 1. deepens. and improves.isss. Thus. org/ by a group of people in Washington. http://h2o. http://w3.edu/View ProjectSyllabus. the course text is von Bertalanffy. The main question which will be pursued during the semester is whether the techniques and ideas of so-called 'action research' . The links have been accessed April 2005. we will contrast three schools of systems Mexico theory: the naïve US school of systems theory.asc. including 'hard' systems such as those found in engineering applications.canberra. Pending that. and 'soft' systems in which the structure of the system is less well defined and generally involving some form of human activity. journals and other GST resources. http://www.) Editor comment: The page provides a good overview of several communities. do?projectId=35 8 This course is a beta test for a subsequent course to be arranged for February 2006. Law & Information Sciences System Theory - Department of Management 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. The practical application of systems theory is in advanced problem-solving in systems.se/~per/IVC742/IVC742.h arvard.html University This subject explores a general framework for understanding of Canberra diverse kinds of systems. scientific and educational agency.edu/~dboje/655/655_overview. (Chapter 2 in General System Theory is the major read.vxu. http://cbae.Ola Larses and Jad El-khoury Views on General System Theory Site name -homepageOrganization American Society for Cybernetics Link Brief descriptions from the sites http://www.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.ifsr. -homepage- International Federation for Systems Research Project: General Systems Theory A Harward Law Also. Considerable arrangements need to be made first (with student input necessary).nmsu. The International Federation For Systems Research (IFSR).au/courses/index. General System Theory.edu. The founding members of the Society wanted to follow and to encourage the development of this interdisciplinary field. is a a non-profit. Ludwig (1969). The overall purpose of the Federation is to advance cybernetic and systems research and systems applications and to serve the international systems community. The Society now holds an annual conference.cfm?action=detail &subjectid=004601&year=2004 New In this course.The American Society for Cybernetics was founded in 1964 cybernetics. courses are held at different deparments across universities throughout the world. constituted of member organizations from various countries. Table 2 General Systems Courses Course name Cts 5p Generell Systemteori Department University Matematiska Växjö och Universitet 7. the General State Systems Theory School. and the Language School of Systems University Theory. http://www.law.5p systemtekniska ECTS institutionen Brief description General System Theory 4p Business. To provide a broad backround a selection of courses are referenced in table 2. and maintains contacts with cyberneticians in other countries. manage a listserve.msi. org/ founded 1981. New York: George Braziller. DC who were interested in the then new field of cybernetics. conducts seminars on the fundamentals of cybernetics.htm 8 . decision-making. The goal is to explore some of the old and new ideas in this field and their possible application in basic and applied research. conceptualizing and modeling macro systems is emphasized. understand and to conceptually model complex Business systems. documents. 9 . planning. The modern manager faces exceedingly complex Administrat systems that cannot be adequately analyzed using formal ion mathematical decision-making.edu/kathy/cct_courses/isds7920.lsu. money. different communities focus on various concepts in this list. broadly founded Technical methodology of Systems Theory to gain a sufficient insight in University general systems principles and their theoretical limits. energy. are usable by either the system itself shaping.2 Concepts of GST Table 3 lists a collection of the key terms & concepts found in the General System Theory communities. time delays and information structures in decisions and actions that determine system behavior and performance. intuiative or experiential techniques. or the environment. Output The product or service which results from the system's throughput or Software programs. clothing. decisions. individual effort. etc. constructing. Attention is given to the implications for information management that are derived from the general systems analysis process.cvut. financial & human input. with a few additional concepts. It is dealt with such concepts like identification. policy. social. Since no consensus among the communities exists about GST. the system dynamics approach to examining.cz/ctu/international/web/en/prospectus/normal /f300/subjXE33OTS. bills. & raw material of some kind The processes used by the system to convert raw materials or energy from Thinking.edu/cof/fs/gradprog/courses/turner/fs50702. The informational feedback characteristics of living systems are studied and methods to evaluate the productivity and effectiveness of industrial and governmental systems are addressed. http://emac3. hammering. cars. Special attention is given to the dynamic interaction between system structure. decomposition and selforganization. The in Prague course aims a wrapping up knowledge from other special classes and giving a common frame for many special engineering problems encountered in practice. rules. especially as they relate to emerging issues such as ecosystem management and global environmental change. processing of technical. sorting. This table is mainly borrowed from a review of Gillies (1982). In this course. laws. etc.orst. time.htm Ourso This course is designed to expand each student's ability to College Of analyze. assistance. melting. Throughput the environment into products that sharing information.Ola Larses and Jad El-khoury Views on General System Theory Course name Cts 1p Systems Theory Department Department of Forest Science University Oregon State University Brief description Seminar in General Systems Theory and Strategic Modeling - Information Systems and Decision Sciences Department General System Theory 5p - The course is inspired by the increasing importance of interdisciplinary perspectives.cof.ocs. http://web. Table 3 General System Concepts Term Input Definition The energy & raw material transformed by the system Examples Information.html Czech In this class students will learn the general. money.html 2. meeting in groups. discussing. http://www. take with the environment Interacts with the environment trading energy & raw materials for goods & services produced by the system. the political system. able to run farther. The finance department. teachers grade papers & give students grades on exams. Currently. a fence permeable or some point in between. development & adaptation. businesses. families. throughput & output cybernation in order to make corrections 10 . employee health nurses review records to see who needs immunization updates. approved The tendency for a system to develop & communicated to staff. automatic relationships among A rock is an example of the most closed system. to create social put their goals into a mission order. to support people during illness & being. Accreditation reports are an example as are patient satisfaction surveys. The overall purpose for existence or the desired outcomes. We may system components & no give or encounter families that are isolated from the community & resistant to any outside influence. etc. & recreational activities. Hospitals. banks. and test results. ect Systems or subsystems will engage in boundary tending. Goal Entropy The tendency of a system to lose Negentropy energy & dissolve into chaos The activities & processes used to Control or evaluate input. They can work parallel to each other or in a series with each other. The disorganization after a hurricane. the renal system. the workflow system (such as the conveyor belt). roles. body systems. A system which is a part of a larger system. Rules are made. The reason for To educate students. a marathon runner in training gradually is order & energy over time.com is an example of how hospitals are doing with certain diagnoses. laws are enacted & violators are held accountable. many organizations restore them to health. etc. interdependent. & capable of growth. work. the information system. family. school.Ola Larses and Jad El-khoury Views on General System Theory Term Definition 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. governmental bodies. to make money. a person. or wall. frightened family produces a child who is unable to think independently or leave home. associations. the subsystems. Feedback Subsystem Static system Dynamic system Closed systems Neither system elements nor the system itself changes much over time A rock in relation to the environment The system constantly changes the environment & is changed by the environment A healthy young adult grows more independent. a rigid. policies & protocols are written. & self-sufficient & self-directed in response to stimuli from peers. an agency or business. a new business has no forms or protocols for handling consumer complaints. Pilots use instrument panels & devices to constantly evaluate & make course corrections. etc. Open systems Boundary The line or point where a system or subsystem can be differentiated from its environment or from other The nursing unit. parents measure their children's height & weight & may adjust the child's diet. health care agencies use TQM or Quality Assurance programs. manufacturing plants. the managerial system. the occupational therapy department. Can be rigid or elementary school. Examples How many cars were produced? How many had to be recalled to correct errors? How many mistakes were made? Why were mistakes made? HealthCareReportCard. They are self-regulating. Fixed. sales reports. people. statement. With a proper meta-model a foundation for knowledge creation is provided. The ontology of any system theory contains three principal constituents: unity. it is commonly recognized that systems perform a transformation process and may have inputs and outputs. each of the parts may be seen as a system of their own. These concepts apply for both physical as well as for abstract systems. Equifinality Objectives can be achieved with varying inputs & in different ways. etc. The objects See subsystem set (parts. Further. enabling analysis and synthesis of systems.3 A General System Model . Unfortunately there is no common definition of systems and every other author (including ourselves) adds a new definition. An observer makes observations such as sensations on the sense organs. A particular situation of the system that the observer can recognize if it occurs again. on the left side. elements. plane. An observation is the act of choosing an element from a set of The programmer. Observer Black box White box State Quality (property) 2. without looking inside of it. A way of grouping states of a system Analyzing a electronic circuit by understanding the workings of its internal components such as resistors. providing a meta-model of the world. System A set of objects together with relationships between the objects and between their attributes. A traveller could take the interstate or back country roads & still arrive at their destination. These ideas can be used as a core of a system model describing a system. The observer can An astronomer studying the universe. owner or maintainer of an possible observations of that type for information system. that observer. in the literature of GST there are some core ideas that are repeated and seem to be established in the theory. attribures. readings from instruments. Also. user. The traveller could go by train. An approach to understanding the system in which the inside of the system is revealed. parts and relationships.Ola Larses and Jad El-khoury Views on General System Theory Term Definition Examples A nursing assistant assigned to empty catheter bags on a unit could begin in the middle of the hall. based on what is believed to be the important features of the system. However. etc) are the undefined primitives of systems thinking. etc. bus or car & still arrive at desired location. on the right side. A model of a system in which the system can only be known through observing its behaviour.Relating the concepts of GST At the core of GST is the system definition. The system concept can be applied recursively at any level of aggregation. transistors. 11 . The on/off states of a lamp The quality of mass defined by the states in which masses are the same or different. at the front or back of the hall & still end up with all the bags emptied. The objects defining a system come from the mind of an observer. however decide on the scope and range of observation. and so a system is relative to the point of view of an observer. 2. He finds the origin of this problem both in the system concept of GST as well as in the related methodology applied. but rather a theoretical model or ‘schema’ determined by the combination of system principles and the subject matter. 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. the system and the environment must be two separate entities and thus the system is outside the environment (The paradox of system environment). we know the total of parts contained in a system and the relations between them. For the concept of unity to be justified.” (Bertalanffy 1969) System unity Part relationships Part Part Figure 3 Ontological picture of system in GST (Dubrovsky 2004) It is possible to arrange the concepts relating to unity. (Guberman 2002) “If. A metaphor is provided by Dubrovsky (2004): 12 . Further. Skinner avoids the paradox of environment by claiming that an organism is not a system but rather a “locus of behaviour”. in the Activity approach the relationships are created in a process of synthesis. A well formulated and interesting criticism of GST is given by Dubrovsky (2004).3. is contained in two paradoxes. thus being general. The problems pointed out by Dubrovsky also explain the lack of a structured core in the books of GST (Skyttner 2001). The whole is more than the sum of its parts. focus is placed on the properties of the whole. (Dubrovsky 2004). as the system is interacting with the environment. if unity is seen as a system boundary then what is outside that boundary is called the system environment. as defined by Bertalanffy. Unity is represented as an entity of its own.1 A Criticism of Bertalanffy Beginning with the view of Bertalanffy. the behavior of the system may be derived from the behavior of the parts. Bertalanffy claims that there is no such thing as emergent properties of systems. it must have properties of its own. A system is not a matter of empirical observation. however. Unity is not emergent but exists prior to the relationships of parts.3. However. The criticism of the concept of system.Ola Larses and Jad El-khoury Views on General System Theory 2. Kant emphasizes the priority of unity over the relationships of parts. Dubrovsky points out that the core of GST fails to formulate a single systems principle applicable to all systems. However. According to Shchedrovitsky (1966) (as referenced by Dubrovsky). parts and relationships according to figure 3 in line with the ideas of von Bertalanffy. and the parts are defined in a process of analysis.2 The Analysis-Synthesis system method Kant provides an interpretation of unity that avoids the emergence paradox. but equal to the sum of its parts and the relations between the parts. the paradox of emergence and the paradox of system environment. 2. He classifies five types of systems: Transcendental. The first procedure concern decomposition of an object into parts and is the opposite composing. Designed physical and Designed abstract systems. so it breaks (‘analysis’) into pieces (parts). 13 . is illustrated in figure 4. with unity as a complementary representation to parts and relations. The third is the insertion of an element into the object’s structure. the glue symbolizes a new addition (Relationship) that was not present in the teacup before it was broken. Checkland notices that the purpose is in the eye of the designer. Jordan. Human activity. that they are goal-oriented and strive towards some end. but had to be added in order to restore the cup. One then glues the pieces together (‘synthesis’) in such a way that one can drink tea from it again (restored Unity). The goal-orientation of systems has been criticized. In this metaphor. This system definition. A mountain range is non-purposive while a road is defined as purposive.3 The goal orientation of systems Bertalanffy claims that systems are teleological. builder and user of the road and not intrinsic in the road itself. three oppositions are defined as form-content. distinguishes purposive and non-purposive systems. System unity Part Analysis Synthesis Part Part Part Part Parts and relationships Part Figure 4 Logical relations among System constituent according to Kant (Dubrovsky 2004) Further. as referenced by Checkland (1999).Ola Larses and Jad El-khoury Views on General System Theory Suppose one drops a teacup (unity). complex-simple and external-internal. Based on these oppositions three system procedures are found. opposed by the extraction of an element out of the structure. He then proposes more useful distinctions that can be used to further clarify the goal orientation concept. Natural. The second is the measuring of aspects of parts and wholes that is the opposite of configuration.3. The similar reasoning can be applied for the other abstractions in the MOF model. Checkland makes a clear distinction between activities (or systems) that simply serve a purpose. inherent in the system in the Bartalanffy version. The decomposition and relations are. The models are represented as UML class diagrams (OMG 2002).4). 3 GST and the MOF In this section. and designed systems can be analyzed and redesigned. In this model. and activities (or systems) which are the result of a willed choice by human beings. This model is obviously more elaborate than the GST model. when conscious human action is involved purposeful. Figure 5b further develops the model. relation Unity contains 1 * * Unity/Part * contains (b) 1 * * Part * relation (a) Figure 5 A UML class representation of the GST Figure 6 shows the definition of the MOF (version 1. This model is valid for both Batalanffy’s and Kant’s system view. A designed system has a function designed for a purpose. according to Dubrovsky. the MOF. Natural systems can be analyzed. and the latter. the composition of Parts into a Unity is represented using the composition relation “contains”. and formed by the beholder in the Kantian version. as a unit with its own decomposition into parts and relations. The difference lies in how the Unity and Parts are formed. This simple model does not however model the recursive definition of each part. Checkland chooses to label the first type serving a purpose as purposive. human activity systems can be analyzed and influenced. we compare the ideas presented in Dubrovsky (2004) with the meta-metamodel of the UML language.Ola Larses and Jad El-khoury Views on General System Theory Transcendental systems are beyond knowledge. unknown to man and can be ignored for our purposes. Bertalanffy’s GST model as interpreted by Dubrovsky are visualised in the class diagram shown in figure 5a. The generalization between Model Element and Namespace and the ‘DependsOn’ association can be seen as instances of the ‘relation’ association in the GST model. 14 . illustrating the recursive nature of the system definition. The ‘contains’ aggregation between Unity and Parts in GST maps to the ‘contains’ aggregation between Model Element and Namespace in MOF. It is possible to compare the two and find the GST concepts in the MOF. aggregation/composition is used to describe a decomposition property inherent in the system itself. A unity/part maps to a class.Ola Larses and Jad El-khoury Views on General System Theory Figure 6 Key abstractions of the MOF model (v1. The GST model is very close to that of a simple Class diagram. while a relation maps to association. since the former is used in GST to manage the complexity of the system model. This is the exact criticism addressed to Bertalanffy by Dubrovsky. The problem generally encountered in class diagram is the flat structuring of the system. which is handled in the GST using the ‘contains’ relation. with the assumption that the generalisation and aggregation relationships are seen as special types of association relationships between classes. while in the latter. This ‘contains’ relation should however not be confused with the composition/aggregation association in class diagrams.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). 15 . (2000) The Engineering Design of Systems: Models and Methods. ISRN/KTH/MMK/R-03/41-SE. A. 153-166. OMG. Vol. L. John J. B. ISBN 981-02-4175-5 Weinberg. Gillies. IEEE-Std 1220-1998. New York. (2002) Reflections on Ludwig von Bertalanfy’s “General System Theory: Foundations.. Skyttner L.L. Systems Engineering. NY.Ola Larses and Jad El-khoury Views on General System Theory 4 References Ahari P. Wiley & Sons. & Hodgson M. P. Saunders Company. Systems practice: Includes a 30-Year Retrospective. No. 2. Doctoral thesis. v1. von (1969) General System Theory. Development. Checkland. 1998.. World Scientific Publishing.). New York: Brazilier. John Wiley & Sons. General Systems 11. New York. (2004) Toward System Principles: General System Theory and the Alternative Approach. (2004) A Systems Engineering Framework for Integrated Automotive Development. (2001) General Systems Theory. (1966) Methodological problems of system research. Department of Machine Design. 7. p. 2004. 2001 16 . TRITA-MMK 2003:41.4. Vol. April 2002. Applications” Proceedings of the 5th European Systems Science Congress. Philadelphia: W. 56-74. John Wiley & Sons. 6. 13. OMG .G. October 2002 IEEE. Buede. (2000) A Brief Review of Peter B. Dorset House Publishing Co. (1998) IEEE Standard for Application and Management of the Systems Engineering Process. ISSN 1440-1179. p723-731. KTH. Leany. Crete.Meta Object Facility. Nursing management a systems approach. Systemic Practice and Action Research. (1999) Systems thinking. IEEE. pp 109-122. G. D. Flood R. Systems practice. Checkland’s Contribution to Systemic Thinking. (2003) A Living Systems Approach to Product Design and Development. (1982).. G. (1981) Systems thinking. no. Vol 21. Shchedrovitsky GP. P. Dubrovsky V. Guberman S. (2001) An introduction to general systems thinking (silver anniversary ed. D. Singapore. Systems Research and Behavioural Science. Checkland. P. Loureiro. 2000. dec 2003 Bertalanffy. 2002. Inc. and then a summary of the book according to the structure of the contents is posted. Weinberg is an older introduction more focussed on content than on providing complementary theoretical views. 32 000 titles were found at Amazon in 2003 (Ahari 2003). 17 . Skyttner and Checkland) share a common format. Three of the book reviews (Bertalanffy. while others deal with general system theory with a generic definition of system. different literature targets either the description or the analysis or the synthesis of systems. or possibly a combination thereof. Ludwig von Bertalanffy is seen as one of the fathers of the theory. providing a methodology labelled the soft system methodology (SSM). At the end of each section some concepts and ideas of the book is summarized by keywords.Ola Larses and Jad El-khoury Views on General System Theory 5 Appendix – Book summaries 5. and the material in his book contains many of the original sources of GST. The fourth review is a more comprehensive summary of the book by Weinberg. Checkland targets systems that are hard to analyze due to complexity or with unclear purposes. Further. providing a deeper insight into the general system ideas. system theories and system design. The chosen books should give a good overview of the field. Skyttners book is a recent textbook covering several theories.1 Book reviews There are many books written about systems. Some of them define systems in a narrow sense and in a domain specific context. First they give a short evaluation of the contents. In this chapter a range of books about systems in the widest sense are reviewed. which is rather rare in the field of general theories. 18 . 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. However. the taxonomy of Jordan and the recursive application of the Viable System model of Beer. Some of the classifications seem very adapted to popular notions of science. 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 next chapter. Many of the described system theories fit into one proposed distinct classification hierarchy defined by the respective authors. Skyttner summarizes some basic ideas in GST. Some general laws. 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. some interesting general ideas can be extracted. The book may be a nice introduction for people looking for an overview of system theories and inspirational for further research. We are placed in the system age where problems (in theory) are addressed in a holistic and interdisciplinary way. 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.1 General Systems Theory – Lars Skyttner (2001) World Scientific Publishing. Skyttner states that it is impossible to be efficient with one theory and that several views are necessary. The concept of general systems theory remains unclear after reading the book. and occasionally quite ad hoc. usually inspired/guided by the physical size of the systems. The third chapter provides a summary of 14 existing system theories. but it includes more detail in application specific topics than necessary and less insightful generalization and details of general system theories than expected. especially for people collecting curiosities. and avoiding generalizations. theorems and hypotheses are also posed. principles. The first part of the book. for example from the subsystem definition of Millers Living Systems Theory. 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 typology of Checkland. Summary of the contents The first chapter of the book provides a historical summary of the view on science since the 15th century. Many of the theories aim for a classification system of systems.Ola Larses and Jad El-khoury Views on General System Theory 5. this may be true and also explains the general impression of the book as probing much into the details of the applications. which make them feel less relevant and useful. titled “The theories and Why”. 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. The confusion may be attributed to the deficiency of a clear conclusion in the book.1. instead of the prior reductionist method of breaking down problems into parts. Singapore. He introduces a range of definitions and concepts of systems that are used throughout the book. Much of the material here is immediately derived from the work of von Bertalanffy. It is however nice to read. gives a good overview of related thinkers and the historical development of more abstract and general theories. For my taste this would have been more valuable than a chapter with 14 different theories. 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. What is lacking in this section is a proper conclusion. Interrelationship and interdependence of objects and their attributes – Unrelated and independent elements never constitute a system. 19 . especially as the book lacks a good bridging section in the text. Soft methodologies are best applied to ill-structured problems with unclear objectives and purposes. The chapter brutally begins with defining the difference of communication and information.Ola Larses and Jad El-khoury Views on General System Theory After the overview one chapter is spent on describing the communication and information theory. the methodologies are however too briefly described to be well understood. The ninth chapter on informatics is again very application-specific and full of technical details that add very little value for the informed reader. Some useful concepts and categories are introduced. At first this dispositions feels strange. chapter details the theory of organization and management. The only interesting content is a short summary of a suggested lifecycle model for evolutionary development of information and communication networks. 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. Holism – Some properties exist only at system level and cannot be detected by analysis of the components of the system. The overview and points made are relevant. 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. Hard methodologies are goal-oriented and solve well-defined structured problems. 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. specifically referring to Bertalanffy and Litterer are summarized in a list of 10 points. The hallmarks of a general systems theory according to Skyttner. how they are conducted. A management related topic is covered in the next chapter. chapter 2 “Basic Ideas of General Systems Theory” provides a range of useful concepts and definitions. “decision-making and decision aids”. 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 distinction between soft and hard methodologies based on the ideas of Checkland is established and the described methodologies are classified accordingly. but as the contents develop the chapter feels highly relevant and important for a wider understanding of systems. This chapter concludes the first part of the book titled “The theories and Why”. The next. 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. and how computers can be used for supporting decisions. Chapter ten returns to the general theories and summarizes the application of a few system methodologies. 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 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. Theoretical concepts and definitions Even though the book contains much application specific information. and seventh. The chapter provides theories on what decisions are. In the end the system theories will prevail as the only sustainable way to implement science. The general points of information are then elaborated in the next chapter in a human application context discussing theories of brain and mind. The concepts are highlighted in the text. that in turn are systems of their own. non-living). Black. feedback is a requisite for effective control. Equifinality and multifinality – systems have alternative ways to achieve the same goals (convergence). For example. near-decomposable or non-decomposable – Based on the dependence of subsystems. Regulation and feedback – All systems have regulatory mechanism. Transformation process – Systems transform inputs into outputs. 20 . Unfortunately the presentation of the material could have been more structured. A living system can for a finite time use energy to create order (negentropy). Hierarchy – Systems exist of subsystems. which is very good. closed or isolated – Depending on the relation to the environment. Static or dynamic – Based on the activity of the system. Chapter 2 provides a filtered summary of the collected work in systems theory and provides a toolbox for system reasoning. A hierarchy of systems exist. Inputs and outputs – Open or closed to the environment of the system. conceptual or abstract – Depending on the tangibility of the system. Open.Ola Larses and Jad El-khoury Views on General System Theory Goal seeking – Systems strive for a final state or equilibrium. Besides the summary of the general systems theory a set of useful classification frameworks and concepts are provided. systems can be: Concrete (living. Differentiation – Specialized units in the system performs specialized functions. but a good structuring or overview of the concepts are lacking. grey or white box – Depending on the knowledge of the internals of the system. and can obtain different mutually exclusive goals from a similar initial state (divergence). Entropy and negentropy – All systems tend toward disorder. Decomposable. but the system definition and ontology with related general system principles is not that clear and crisp. The book has good value as a collection of papers from one of the named “fathers” of GST.1. A mathematically based GST is expected to be developed. but he also strongly acknowledges the use of soft. first published in German with the translated title “An Outline of the General Systems Theory”. The second chapter details the shortcomings of disciplinary science and introduces some basic concepts.2 General System Theory – Ludwig Von Bertalanffy (1968) George Brazillier. however the use of soft and verbal models should not be underestimated. 21 . ISBN 0-8076-0453-4 (295 pages) Von Bertalanffy is considered one of the founders of the general system theory and the book. a simple example is the total weight of a mechanical system. the references to trends and people in academia in the 50’s and 60’s also make the section hard to read. 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. For summative characteristics the system is no more than the sum of its parts. goal orientation and purpose are concepts that must be added. being a revised edition. The models and concepts are rather soft. and only a few hints of what these mathematics should look like is given. also titled “General System Theory” is a collection of papers and book excerpts by his hand. However. In addition. Bertalanffy brings out a range of concepts related to general systems. The constant increase of entropy is contradicting the possibility to build organized complex systems. It is noted that contemporary science only works with closed systems and theories must be developed also for open systems. Further. especially at higher levels of abstraction. Summative properties are independent of other entities. the contemporary science is referred to as mechanistic and only working with causality. in order to be useful. The usefulness of differential equations for this purpose is developed and some basics of mathematics and control theory are given. Also. It contains some of the original formulations of some basic system concepts. repeats some of the historical background and the motivation of the field. The core of the theory is given in chapter 3 based on a paper authored in 1945. The summative characteristics concern properties independent of relations. also an introduction. Mathematics is acknowledged as a general theory as it can be applied to a variety of problems. is introduced by a preface where some additions are made on the background and at the time current flows of the theory. Summary of the contents The book. the theories presented need more elaborate contents. New York.Ola Larses and Jad El-khoury Views on General System Theory 5. A harsh interpretation of the work is that is only says that entities and relationships should be represented by mathematics. the idea of an open system consuming energy remedies this conceptual problem. The first chapter. by von Bertalanffy referred to as organization. qualitative models which is seen as an intermediate step in the theory building process. First an important distinction between summative and constitutive characteristics of elements is introduced. which Bertalanffy acknowledges and motivates by claiming that this is the first steps towards a more rigorously defined theory. the distinction of information as a complement to energy is necessary for some theoretical constructs. The ideas of Bertalanffy are surprisingly focused on mathematics. With some previous background on the subject the introduction becomes somewhat long and tedious. The third chapter expands the mathematical content of the theory. It quotes the laws that entropy is always increasing. The next chapter. Then there is dynamic teleology indicating a directiveness of processes. Bertalanffy recognizes two methods of general systems research: the empirico-intuitive followed by himself. By this specialization the dependencies among the entities increase and over time a system becomes more bound together. A few applications in biology and their maturity in developing mathematical models are referenced. The process is referred to as progressive centralization. and the deductive developed by Ashby. feedback and mathematical relations are suggested to be complementary and equally important theoretical contributions. Chapters eight and nine continues to exemplify applications of the general systems concepts through the sciences of man. all models are said to be approximations of what they describe. psychological and social systems interactions among elements decrease over time. Another related interesting note is that systems often have a leading entity. specialized causal chains. Equations are representing a theory if all parameters of the equation can be confirmed by independent experiment. First it is established that the contemporary theories of man as a robot is 22 . Then a case showing the development of a mathematical description for metabolism and growth exemplifies the reasoning. The emprico-intuitive approach looks for laws in each discipline and then compares the results to find laws that hold across systems in general. 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. A discussion on dependent and independent properties of entities is given. The individuality and the leading role of the leading entity. Chapter seven is a case study of general systems theory in biology. Open systems. The machine-like behaviour of the sum of the independent chains is referred to as progressive mechanization. It is also mentioned that the same final state can be reached from different initial conditions and through different paths. called “the model of open systems” discusses how most (contemporary) theories are based on closed systems without import and export of matter. The fourth chapter expands the advances in GST and exemplifies how several fields of theory introduce system concepts like organismic analogies. The last section of the chapter lists a set of theories and relates them to the previously described concepts of general system theory. entropy may be reduced by import of matter. Mathematical models are shown to be useful but not conclusive. This is a direction of events toward a final state. and interdependencies of entities rather than causality. Systems will be arranged in a hierarchical order of centralized systems. The first type is static teleology or fitness. There is also true finality or purposiveness. This leading entity defines the individuality (from indivisible) of the system. where the system properties are the dependent properties that make the whole more than the sum of its parts. Bertalanffy also make a warning for oversimplification in the models. In the fifth chapter the mathematical parts are expanded with definitions of equifinality and concepts related to open systems. and if predictions of yet unobserved facts can be derived from the theory. this is labelled equifinality. increases as specialization of entities progress in the system. possibly based upon the structure of the system. Negative entropy (or negentropy) is identified as information. from wholeness to independent. Then the concept of finality and types of finality is introduced. This is followed by an interesting note that in biological. for example in man made systems where it is fitness and structured working of machines due to a planning intelligence. The deductive approach begins with a definition of system and from this definition general laws are deduced. but recognizes that for an open system. Chapter eight details the benefits of using a system model in the social sciences. which means that a given system seems to be useful for a given purpose.Ola Larses and Jad El-khoury Views on General System Theory Then a range of system concepts are established. In an open system entropy may increase locally due to the inflow of material. Below are a few of the core ideas: Isomorphism – Along the disciplines of science there exist common ground. They are not necessarily mere causal machines. This does not imply that mathematical laws are useless. In chapter nine. The model of man as an active organism is further extended and exemplified. and can obtain different mutually exclusive goals from a similar initial state (divergence). Equifinality – systems have alternative ways to achieve the same goals (convergence). Examples of cultural differences in the number of existing words for a given phenomenon.Ola Larses and Jad El-khoury Views on General System Theory incomplete. Progressive mechanization – Systems strive towards specialized entities. however. It is also implied that the brain may perform such changes. time and space. Bertalanffy again warns about oversimplification and interpreting models to strictly. 23 . 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. higher layers can be introduced with new leading entities and subsystems also have leading entities. and even in how time is treated are given. Also. Hierarchical Layers – Each leading entity creates a system layer. Progressive centralization – In the process of mechanization each system is organized around a leading entity. Goal orientation (active systems) – Systems may actively strive for a goal or final state. Homeostasis (feedback) – Systems strive for a desirable steady-state through regulating itself based on feedback of information. for example. The ideas of progressive mechanization and the development of leading entities are repeated. An example is given with a table. This property is referred to as isomorphism. seen as a system of atoms by the physicist. and must be replaced with a view of man as an active open system interacting at the social system level. Open system – Systems where material flows in and out are open systems. a system of wood by the biologist and a unit of capital by the economist. and is exemplified by the evolution of creatures in nature. adding higher layers of behaviour with each layer including a leading entity. possible to cover by a common theory. system theory in psychology and psychiatry is discussed. Theoretical concepts and definitions Being an overview of the early theories of GST several concepts and definitions are introduced. This is in line with the levels of Maslow. different backgrounds and domain knowledge also influences our view of a system. 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. historical events can often be explained by applying a law of social behaviour. specifically from the Eskimo Hopi culture and American Indian cultures. A methodology is a collection of methods from which you choose the appropriate ones for a given situation.) After an historical overview of proposed methodologies from the author. situation-driven and in interaction with the system. a systems thinking part and a systems practice part. Chichester U. formulates it mathematically. but the book places nice bounds on the domain of systems engineering. The actual notation in the models is very simple but elaboration of modelling is suggested if it improves understanding of the system. graphical models to visualize parts of the system and create a common ground for discussions. it is a metatheory that can be applied in the same way as science. giving a brief outline of the soft systems methodology (SSM) comparing it to hard systems engineering. the current modelling methodology is outlined.3 Systems Thinking. which is the first part of the volume. However. Studying the ideas reveals a loosely defined methodology that occasionally seem arbitrary and non-systematic. Ultimately SSM should be used iteratively. Summary of the contents The 30-year retrospective.1.Ola Larses and Jad El-khoury Views on General System Theory 5. The book gives a nice perspective on the benefits and limitations of system ideas in practical application for undefined problems. not sequential. However. 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. 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. from the Greeks (as always) to Einstein. guidelines for performing this modelling is given. in fuzzy situations with unclear entities and where the objectives are uncertain and contradicting soft systems thinking can be useful to clarify the picture. 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. The purpose of the model is either to implement change or to understand a complex process. the selection of extended language is left as an open issue. exploring the underlying process. The general idea is to draw simple. this is maintained as one of the strengths of the methodology as it enables a discovery of the actual underlying processes. Checkland points out that hard systems thinking have been successful in engineering technical systems with clear purposes and objectives. (This can be applied to social systems but also architecture design and similar situations. For technical systems with clear requirements a hard systems engineering approach is suggested. Systems theory is not a science. The main text is divided in two parts similar to the title. begins by discussing the notion of hard and soft systems. and makes 24 . methodology-driven and intervening with the system. A scientific approach breaks down a problem. Checkland proposes that soft problems should be explored by modelling the transformation in focus. Systems Practice – Peter Checkland (1981/1999) John Wiley & Sons Ltd.. Checkland underlines the methodology aspect of soft system methodology (SSM). SSM advocated by Checkland is seen as complementary to systems engineering to deal with complex situations. This section is one of the most interesting parts of the book. Chapter two is the first of the systems thinking part and gives a thorough and well told walkthrough of the development of science.K. Each layer contains systems or ‘holons’ that are organized and linked. occasionally using purposive designed systems as tools. These definitions together with a short conclusion on basic systems thinking summarize the first part. The information is a view of the system that allows entities within it to react. The value and usefulness of this approach are appreciated as monumental. 25 . Second. human-made physical systems. Monitoring a system changes a system which means that results depend on the method of measuring (compare the uncertainty of Heisenberg). The fourth chapter “Some Systems Thinking” begins with describing some basic ideas that are recurring in different theories. and also for some human-made systems where there exist conflicting goals and purposes.Ola Larses and Jad El-khoury Views on General System Theory repeatable experiments to verify the validity of predictions. not to say impossible to properly measure and repeat experiments. layered arrangements where some properties emerge at a given layer of abstraction and each layer contains laws that need to be studied separately. In a system not only energy but also information is flowing. This assumption holds for human-made systems in general but breaks down for human activity systems. and its’ limitations. The one shot nature of monitored events inhibits a scientific approach. Applied systems thinking is performed in (1) ‘hard’ systems. (2) decision-making problems and (3) ‘soft’ systems. “Hard” methods like systems engineering assumes that the system have a clear purpose and is optimized towards this purpose. human-made abstract systems. Then the typology of Checkland himself is described. human activity systems and unknown transcendental systems. A third problem is the issue of management where decisionmaking is an instant interaction and the situation is rarely repeated. according to Checkland. A systems description is always related to an observer. Systems belong to one of five types: natural systems. relationships/coherence. is underlined. For these problems Checkland proposes a “soft” methodology which is elaborated in the next chapter. Human activity systems are. First Checkland introduces the concepts of Emergence and Hierarchy. while the human-made systems are designed for a purpose. Systems are hierarchical. multi-variable problems where it is impossible to isolate a small set of variables for analysis. Human activity systems are made up of purposeful activities. for biological systems this control exists and can be modelled by a systems approach. lacking in Jordans model. In technical systems this control is designed by the control engineer. These problems call for a different approach to cope with some human activities. Next. a control mechanism (defining the entity’s identity) and is part of a hierarchy. Some entities exert control on others with respect to given control variables. Further. defined by the information content. a boundary. for higher level systems like social systems it is difficult. The scientific approach has problems to cope with complex. This background serves as a foundation for the discussions of the systems approach covered in the next chapter. Communication and Control are introduced as an important systems concept pair. Natural systems are evolutionary made. distinguished by the free will which makes humans unobservable and unpredictable. large. The third chapter introduces some problems for science. The description itself contains entities. they are goal-oriented. Checkland produces and overview of the systems movement where he recognizes three problem solving applications related to the theoretical development of systems thinking. The second part “Systems Practice” begins with an overview of “Hard” systems thinking. The theory of Boulding’s hierarchy and Jordans’ taxonomy are referenced and the importance of the observer in a system description. the conceptual model is an account of the activities the system must do in order to be the system of the root definition.Ola Larses and Jad El-khoury Views on General System Theory The soft systems methodology has seven stages. The root definition is an account of what the system is. Models. and further examples are given in chapter seven. to capture diverging views of the system. How(Q). In the stage of mapping between the two the link must be understood. In the soft systems methodology a root definition must be formulated. as much information as possible is collected from a variety of sources to make the richest possible picture available. After the examples. Stages 1 and 2 concerns expression of the problem. consist of verbs specifying activities which actors carry out. First the importance of understanding the Weltanschauung (viewpoint) of the root definition is discussed. In this chapter Checkland places his theory and methodology in the context of social science and related work of other authors. Theoretical concepts and definitions In the typology of Checkland there exist five types of systems: Natural systems – Found in nature and developed by evolution. this is one of the purposes of the root definition. according to Checkland. Do the activities of the conceptual model fit the existing system or what are the differences? Based on the results. In phase 5 the derived conceptual model is compared to reality. the root definition is a formulation of the function of a given system (a ‘what’). Human activity systems – A purposive system which expresses some purposeful human activity. Human-made abstract systems – Conceptual systems like mathematics. In stage 3 a root definition of relevant system(s) are formulated. Based on the root definition a conceptual model is developed. norms and values are rated as highly important for the success of a study and the development of a useful conceptual model. 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. Root definition – A position in a means end hierarchy of Why(R). Human-made physical systems – Tools and machinery existing in the real world. the comparison with reality reveals how the desired function is performed. The importance of understanding the difference between what a system does and how the system does it is also important. the root definition can be focused on a given primary task or a more general issue of a system. and understanding of the power and politics derived from roles. Depending on the viewpoint of the person formulating the root definition the contents of it will be different. The eight and final chapter is presented as a third part of the book containing conclusions. 26 . What(P). It is always possible to go up or down in this hierarchy. stage 6 contemplates feasible and desirable changes and stage 7 implements changes through action. A root definition should meet the requirements of including the six elements of CATWOE: Customers – The beneficiaries or victims of the system. The mnemonic CATWOE elaborated below is given to support the construction of root definitions. chapter seven introduces some conclusions from the research. The conceptual model derived from the root definition is an abstract formulation of what the system does. Trancendental systems – Systems yet unknown to man. Further. The importance of elaborating the initial study is underlined. At the end of the book are two appendices with some hands-on advice on the topic. The methodology is illustrated by an example. collected under the headings “building conceptual models” and “a workbook for starting system studies”. Ola Larses and Jad El-khoury Views on General System Theory Actors – Agents who carry out the activities of the system. Ownership – The guarantee of the existence of the system. Environmental constraints – Impositions that the system takes for given. especially its main transformation. 27 . Weltanschauung – The viewpoint of the person formulating the definition. Transformation – The core process of the root definition. and then apply science/quantitative work.Ola Larses and Jad El-khoury Views on General System Theory 5. we need simplifications and assumptions. What is science? To answer this. why use this model for science? Because it allows us to reduce our complex systems to simpler ones. we informally reduce them to simpler ones first by ignoring insignificant parts. without being able to control its effects.1 The law of computation is not only about the limits of computing devices. General Systems Theory is brought about because science has been a success. In other words. you start qualitatively to get your model. To be able to formally solve large systems. Science and technology have revealed a complexity that it could not deal with. etc. 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. Square Law of Computation Without simplification. NY. we examine physics/mechanics. New York. 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. Inc. Hence. but is a problem for too many parts. Page 3: The GS movement has taken up the task of helping scientists to unravel complexity. 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. we get satisfactory results that match observed data. This works for 2 or 40 parts such as bridge. As expressed by Karl Deutsch. Need to be able to simplify. That is..2. He goes through the process by which a scientist forms his model and uses this to suggest useful models for other sciences. why not use GST? 1 28 . 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 … “.1 The Problem The Complexity of the World Science and engineering have brought about an unprecedented speed of change. there is a limit to how much computations we can do in money and time. The general system thinker’s task is to understand the simplifying assumptions of a science. 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. The brain is also a computing device and we need to handle the amount of information given. 2001 ISBN:0-932633-49-8(279 pages) 5. technologists to master it and others to learn to live with it.2 Extended book review: An Introduction to General Systems Thinking – Gerald M.. So. they build a common model that can be applicable by all sciences. Weinberg Dorset House Publishing Co. assumptions. the amount of computation increases at least as fast as the square of the number of equations. By studying GS thinker produces a set of tools of simplifications that are common enough to be useful for all scientists. In practice. but can study average properties such as volume. we can expect that large fluctuations. (Computers. will happen. hence we resort to systems theory. which states that “the inaccuracy in an average statement is in the order of the square root of N. The technology of government has drawn upon statistical mechanics. 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.) Science is a very useful tool. pressure and temperature. To a first approximation. taking averages. that are sufficiently random in their behaviour so that they are sufficiently regular to be studied statistically. 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. II Randomness I III Complexity I – organised simplicity – machines II – unorganised complexity – populations III – organised complexity – systems III is too complex for analysis and too organised for statistics. where N is the number of the population on which the study is performed. Consider the properties of a gas in a bottle. the more likely we are to observe values that are close to the predicted average values.) The importance of this law lies in its scope of application since we are surrounded by such systems. etc.”. we can handle complexity.Ola Larses and Jad El-khoury Views on General System Theory the methods of simplification that have succeeded and failed in the past.” (Anything that can happen. dealing with complexity by reducing the number of parts. The law of large numbers states that “the larger the population.2 Statistical Mechanics and Law of Large Numbers Scientists may sometimes be interested in average properties rather than exact proprieties of a single item. We need not look at the specific molecules. Maybe that is why we can use GST in our engineering work? 29 . Randomness is the property that makes statistical calculations come out right. but its fruits are simple fruits. “Simplicity” is as slippery a concept as randomness. the number of objects is a measure of complexity – the complement of simplicity. irregularities and discrepancies with any theory will occur more or less regularly. The concept of “randomness” is most important for systems thinking. 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. forests. Statistical mechanics deals with “unorganised complexity” – complex systems. humans.” A more useful rule of thumb is the Square Root of N Law. creating simplicity by dealing with people in the structureless mass. The technology of machines has drawn its inspiration from mechanics. 2 The Approach Organism. etc. Thinking is done in completely personal. One method of simplification applied in technology.Ola Larses and Jad El-khoury Views on General System Theory simplifications.2. many overt categories of thought exist. is to focus on the parts of the system. 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. while paying less attention to the connections between these parts and to the rest of the system.) These primitive things are not questioned. Animistic religions explain the behaviour of everything by referring to its unique spirit. but it should not be carried to extreme. Organismic thinking explains things through analogy. so much so that how it is done is incommunicable. Similarly. By possessing a common set of standard categories of thought – symbolised by special words or phrases – groups can simplify the process of internal communication. Mechanists explain everything in the primitives of physics. but a collection together with the relationships between them. Science is the study of things that can be reduced to the study of other more primitive things. Page 20: Science is unable to cope with MNS. Many ills of society came from a too good an application of these fruits. and it is expected that a scientist have faith in them. physical chemistry. that he notices the different category Isn’t that what we are also experiencing when designing a truck? The parts (disciplines) are very clean but the connections are getting weak. its contribution is to be in limiting the excesses of other approaches to complexity. (Explaining everything through god is not scientific. it is recognised that a system is not merely a collection of parts. Analogy and Vitalism GST aids thinking about Medium number systems (or organised complexity) by finding general laws. Then. a new level of technology is reached. The organismic thinking – the use of analogy – is not to be discarded. Even scientists still use it to simplify thinking. the set of primitives cannot be too small or too large. 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. This may be carried to great extremes. What is important is not to stop at the analogy and to render it into a precise. This separation of function is useful. These laws are stated informally to aid understanding. Compare to the organismic approach that turned to living systems for analogy to handle complexity. leading to multidisciplinary engineering (Mechatronics). To be part of the group. A physicist generally possesses the thoughts of celestial mechanics as well as that of auto mechanics and have no problem switching between them. 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. Revolutionary movements recognise the importance of the connections and synthesise them into a new field of knowledge (new part) such as electromagnetism. the vital essence. GST is scientific in its thinking. predictive model. Need to synthesise. 4 30 . though its success with systems of its own choosing mislead many into thinking of science as a way of dealing with ALL systems. They may reduce everything to a single primitive. but they must be supportable by vigorous operations on vigorously defined models. This is to avoid previous mistakes by other approaches. one must master the internal category of thought. For it to be a science.4 5. From time to time. 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. idiosyncratic terms. However. GST is not going to yield the kind of control expected over MNS. The problem is pushed from the parts to the connections. Only when difficulties arise. we want to make the engineers aware of these categories. These categories may change while ‘normal scientists’ work within a given scheme or paradigm. We guess and hope to be right. he will identify the ‘foreign’ language of auto mechanics as the source of difficulty (ethnocentrism). For this to work. constantly making a fool of himself. but by foregoing detailed analysis.6 Page 35: To be a good generalist. 7 A generalist approaches a system with a certain naïve simplicity. In aida2. Each time this succeeds.5 Scientific disciplines. as opposed to the colonist who imposes his paradigm on the cultures he needs to live with. but it is taken from a much higher vantage point. Each approach has its errors. ‘Revolutionists’ create new ones and destroy old ones. we are exposed to certain errors. And. be it analytical or synthetic. The slow-but-sure method of analysis may only guarantee that we cannot possibly arrive on schedule. But we are willing to take the risk of error since there is an explosive growth of knowledge. The advantage of being a mechatronics is that we already know a bit about many disciplines so we have better chances of finding similarities. When he does. This same revolution may be performed by ‘interdisciplinarians’ on many different disciplines by carrying the change intact from one to the other. will also use this attitude when generalising engineering. and even try not to blame the foreigner a better engineering world if we are all equal. By taking the grand leap based on the faith in the order of the second degree. 5 31 .Ola Larses and Jad El-khoury Views on General System Theory schemes. from which all the disciplines are seen to be alike. search for similarities and then announces the new law of law. He adapts to the other paradigms instead of applying his paradigm on the new discipline. and all people also understand that no one is superior. ‘Interdisciplinarians’ differ from ‘generalists’ in that the former knows one thing that they apply over and over again. have category schemes to facilitate internal communication. The generalist jumps to conclusions based on insufficient evidence. But this does not always work since induction does not always work. a belief in the unity of these disciplines is needed. we use general impressions as guides to In Aida2. We form a general impression of the whole before going into the details. like social groups. but at least we shall find out soon. The generalist starts with the laws of different disciplines. in the hope of being more useful. The primary way of discovering GS laws is by induction. the general law is strengthened. as long as we do not get ashamed and are willing to back away from conclusions when proven wrong. But we still need to move from interdisciplanarians to generalists. engineers are also expected to have this believe and try to have this high vantage point. How is that done? They too have a single paradigm. Every article of faith is a restriction on the free movement of the generalist among the disciplines. we may often be wrong. Like the anthropologist that adopts to live with many different cultures. while the latter knows many things. The Main Article of General System Faith Nobody can live without faith. 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. but this only diminishes his chances of making a revolution or moving to another discipline. 7 The order of the engineering thinking should be lifted to a second degree order. so that they can identify the source of miscommunication quickly. but obscured by different languages. GS approach simply replaces one set with another. One should be careful in assuming that one paradigm is more ‘real’ that another. No approach. can guarantee flawless search. one should not have faith in anything.” A generalist finds laws about laws. it should be ok to jump to conclusions. 6 In aida2. The induced laws can then be used to draw conclusions about cases not yet observed. It may be essential for a scientist to have faith in the truth of his discipline. (When lost in slightly familiar territory. Improving the thought process. we may miss dinner. For example. category schemes.”8 While these laws apply to any generalising behaviour. When measurements are found incompatible with a well-established law. In scientific laws. 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. instead they yield insight. each law should be followed by at least two ‘happy particularities’ in order to demonstrate it. 32 . and hence can afford to be wrong. look at scientific laws. the more general and useable the law is. but can also be applied to the models of GS. Laws play the roles of guides to measurement. but never throw away the law” GS laws are not designed to yield answers. Don’t have to follow laws strictly. He implies that there are 3 sorts of activities involving models: 1. The GS movement did not start as a discipline but is 8 We will use these laws in that spirit also. but it is an advantage since he is not afraid of the unfamiliar.Ola Larses and Jad El-khoury Views on General System Theory more familiar territories. 3. studying special systems. since otherwise the statement becomes too long. A second type of GS activity is the application of it in different fields or special systems such as biology. define terms. 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. we can readily correct. He uses the general paradigms for thought and communication. reject the facts or change the definitions. The content that maybe understood from the new discipline might be small. the last thing to be changed is the law itself. So. A third activity is the creation of new laws and refining old ones. In order to avoid hollow generalisations. engineering. Before explaining the use of laws in GS thinking. called GS research. The fewer the if-clauses. (Few people might be engaged in creating new GS laws (3)). the Law of Conservation of Laws: “When the facts contradict the law.” (These laws seem contradicting. They will never be used for precise analysis. If we are mistaken. (Contrary to the believe that one negative case invalidates a scientific law) We formulate a GS law. the laws will be stated in the more memorable definition rather than the accurate version. there are laws applying to the typical ‘systems’ part of GS thinking. as opposed to GS thinking and GS application. which makes them hard to forget!) Of what use are GS laws? Since they are very general and since systems are complex. it’s what we know that ain’t so.” and the Decomposition law: “The part is more than a fraction of the whole. they will not be helpful at making exact predictions. the GS approach’s largest contribution is to improve thought processes. 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. This is demonstrated by seeing how a generalist approaches a new subject. If we insist on reading every house number.” 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. the Composition law: “The whole is more than the sum of its parts. remind us to look for things we have not noticed and predict behaviour. 2. generalisation are tools of GS thought. In this framework. This leads to the law of Happy Particularities: “Any GS law must have at least two specific applications”. creating new laws and refining old ones. But because they are general and because systems are complex.) The Nature of General System Laws Analogy. etc. Scientists have worked hard to get rid of animism/vitalism and thoughts such as ‘If I were a planet. Knowledge is ‘truth’. truth. the more sure we become and the more sure we are. it is becoming a specialisation itself. sailing through space. would I tell lies” or “If I were nature. our strong belief in their existence may be preventing their discovery. terrifying for a scientist. Absolute and Relative Thinking Statements in a language only have a meaning in relation to certain accepted meanings of the words in them. a system is a way of looking at the world. would I throw dice?”. Egocentrism is a form of animism which is a form of vitalism. how would I be attracted to the great mass of the sun?” or ‘If I were nature.The conviction that there is only one way of interpreting the visual pattern in front of us. The further along the scale we go. This too is a heuristic device. 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. ‘Accepted meanings’ implies that somebody is doing the accepting – the 33 . the more likely to suffer an illusion . since it has been so successful. but if there are. and we can make progress in science by believing in the reality of the external world. principle. but if there are. This is generalised to the ‘banana principle’: “Heuristic devices don’ tell you when to stop. We can get insight into the ideas of force and motion from our internal response to situations. the ultimate heuristic device. it cannot tell us when and where it can be successfully applied. How would we know how nature (reality) feels? Such thoughts have barred the way to scientific progress. If 2 scientists viewing the same scene have different ‘systems’ then science will be no better than poetry. 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 …’. There exists a complementary tool: “relational thinking”. The more success we have. like all heuristic devices. There may be ‘real objects’ out there in the world. the less we notice that it is a device. (Isn’t that what most people believe about science? That is provides the truth. it is not because we perceive them as real. there maybe ‘real laws of nature’. They believe in the concept of observer independent truth.) The belief in an external world is one of the most powerful thinking tools we have and we don’t intend to discard it. Similarly. But. Perception responds to both illusion and reality. We forget the Banana Principle and think we can use it forever. While it was originally intended to overcome overspecialisation. Yet. So. It is a point of view – natural for a poet. Mechanics alone cannot tell which systems will yield to mechanical analysis. 5.Ola Larses and Jad El-khoury Views on General System Theory becoming one. law. a mental tool to aid in discovery. rule. but that the belief in it is essential.2.3 System and Illusion A System is a Way of Looking at the World Page 52: as any fool knows. This concept is egocentric. yet they are not totally without use. reality. knowledge is ‘reality’ and to speak of systems in this way is not to acquire knowledge. let us see what we can learn if we occasionally suspend the belief in independent reality. depending on how far you can go before you must stop: idea. He did not say that an external world is essential. 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. Belief in an external world is a heuristic device.” There is a scale of ascending values of heuristic devices. concept. We can find cases where a property is emergent to one observer and predictable to another.’ There are 2 concealed absolutisms in this statement. not absolute. being on the outside. for more advanced applications. forcing attention to the reaons for the non-arbitrariness. It could be ‘out there’ in the real physical world. but are found in the whole. on a certain scale of observation and for certain purposes. We focus on the observer for the moment. Both arguments are right. or in the observer. but more or less an official public reason. we understand that properties will emerge when we put together complex systems. instruments and techniques. for purpose is a relation that depends on the observer. we could not say anything about truly arbitrary sytems. we may need to refine the view of the thermometer. 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. is an instrument for understanding the world. The property of ‘emergence’ no longer emerges for us. These reasons are the source of order that makes systems thinking possible. We just do not need to highlight this all the time. expands first. One has to do with the time scale of the observation. By recognising emergence as a relationship between the observer and whatever he observes. The reading actually measures the difference in expansion between mercury and glass (relative expansion. we narrow our attention to non-arbitrary systems. A system does not have a reason to exist. System writers speak of ‘emergent’ properties of a system.9 A System is a Set Even though any arbitrary way of looking a the world can be a system. words. But. the thermometer actually drops first before it starts rising. there are several types of observers designing a system. since it seems to imply instantaneous expansion. just like the public agreement on the meaning of a word. though it surprises those who take the absolute view. Properties emerge for a particular observer when he did not or could not predict their appearance. A system will consist of several points of view. like a language. rather than relationships between a system and an observer. So. They support their arguments with examples of emergent properties that turned out to be perfectly predictable. yet to the junk dealer it is there to put out scrap metal. Absolute thought is a simplification that serves well at certain times. saying that these properties are but another name for vital essence. We note that any way of looking at the system do not form an arbitrary system. Others attack this idea. General Motors to a user exists to put out cars.Ola Larses and Jad El-khoury Views on General System Theory observer. properties that did not exist in the parts. Non-arbitrariness has two sources.) and because the glass. but they are in trouble because they speak in absolute terms. though we may learn something if we examine the relative nature of some seemingly absolute statements. A thermometer. 34 . Most of the time. The appearance of absolute meaning in certain statements comes because there is an almost universal agreement on the meaning. absolute speech will not get us in trouble. A system has ‘no purpose’. Page 62: … any system is the point of view of one or several observers. in conventional situations. It works as long as we work following conventional patterns. When we use it for simple things. we can use simple language to describe what it does. as if the ‘emergence’ were stuff in the system. The good/bad is based on the purpose of each of the types of observers. The second lies in the ‘expansion of mercury’ statement. for the way 9 In AIDA2. 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. It is more forceful to speak in absolute terms. How can we avoid fallacies of absolute thought? Always remember the human origin of our models. but nothing about how to choose them. elements. Valid means it is internally consistent. 10 Each observer type would choose its own set of object types and hence we need different points of view for different observers. and if they succeed. an observer may be characterised by the observations he can make. language designers do not talk about what the elements are. … they will find themselves very much enlightened during the process…12. 11 In Aida2. but about a particular system. 12 In aida2. they cannot be distinguished from productive theories. components or variables. on a mathematical level.Ola Larses and Jad El-khoury Views on General System Theory belongs to the mind of the observer. we can speak of the argument as being true for that correspondence. moving into a mathematical representation of the system – without saying how that representation was chosen. The hyper-mathematisation. set theory and its notations would be of great convenience. Arbitray systems are hard to find since as soon as we think of one. engineers.11 As long as the members are not set. Too narrow if we exclude some of his scope or fail to make the grain sufficiently fine. Range}. In other words. but never say what they are. Others speak of parts. or the generation of great mathematical general theories is a problem since the theories so general they cannot be applied to anything (sterile) and. justifying the effort of explanation of the idea. A mathematical argument cannot be ‘true’ or ‘false’ but ‘valid’ or ‘invalid’. While they emphasise the relationships as essential parts of a system concept. for example. 35 . it becomes non-arbitrary. made no bones about saying it was a set of objects. The choice is based on the observers’ own expertise. So an observer can be characterised as a set of sets {Scope. they fail to note that the system itself is relative to the view point of an observer. The characterisation of an observer may be at once too narrow or too broad. such as sensations on the sense organs. In fact. our theorising is strictly contentless – mathematical. we will use math once we have the intuitive feeling of things and the maths will be used to simplify. We avoid using mathematics unless we intend to use it more than once. Page 69: … Let them make the effort to express these ideas in appropriate words without the aid of symbols. This diversity of names suggests that the members of a system set are one of the undefined primitives of system thinking. We know that they come from the mind of the observer.10 If systems are sets of things. An observer makes observations. 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).’ No clue is given to where did the objects come from. An observation is the act of choosing an element from a set. The mathematical view cannot distinguish between ‘sterile’ and ‘productive’ arguments. etc. Hall and Fagen give the following definition: ‘A system is a set of objects together with relationships between the objects and between their attributes. Hall and Fagen. once we say what they are. It has to make sense first. attributes. For example. We will use sets in the elaboration of out concept of observer. We introduce set theory to give ourselves a convenient way to talk about a delimited range of possibilities. we are no longer talking about systems in general. we just talk in general. Once we set up a correspondence between the mathematical argument and something ‘real’. The role of the observer is ignored in systems writing by. The mathematics of sets (set theory) tells much about the properties of sets. readings from instruments. the set of all possible observations of that type for that observer. This implies that nobody knows. Observers and Observations We have so far been vague about what the set underlying a system is a set of. System thinkers talk about these members. Each decision type is concerned with a particular type of decisions. A is consistent with B if there is a one-to-many mapping from A to B. giving each observation an arbitrary name. If we toss a penny on table. This decision comes from the set of all possible decisions for that designer. Generally. which take the sting out of words. one from either adjacent side. even if A does not. since if B makes more accurate observations that A. or cannot. During and after a revolution. Hence. B might specify a more accurate observation than A. We have no requirement that the observer be able to make individual observations ‘correctly’. neither observers dominate the other in all situations. A many-to-one mapping implies inconsistency. An observer that makes more discriminations in a situation than another is said to dominate the other in that situation. A is said to be consistent with B if A never gives 2 different symbols for one of B’s symbols. yet A’s observation is still consistent with B’s. All he must do is to recognise 2 sensations as being ‘the same’ and he is the final arbiter. we rely on mathematical symbols. we introduce the concept of ‘consistency’ which is the compatability of one set of observations with another. There is a many-to-many mapping between A and B’s observations. we shall remind ourselves how much computational capacity our model requires (Square law of computation). then the cross product model would at least not exclude any observations he might make. The first step in testing the consistency of 2 observers is to neutralise the form of their observations. he may not be able to make all combinations. To put the principle into operation.Ola Larses and Jad El-khoury Views on General System Theory We may be either not aware or interested in certain possible observations or the resolution levels. things are often renamed just to change thinking patterns. Certain elements in the product set may need to be excluded for a more precise description of the observer. If A is consistent with B. An observation is equivalent to a particular design decision he can perform. But. undefined elements and the word ‘correct’ applied to them is meaningless. because the observations are our primitive. The notion does not depend on how the observer names the observations. at eye level to both A and B. Hence. knowing what A observers does not lead us to what B observes. A’s observations add nothing to those of B. but In aida2. if A is consistent with B. The Principle of Indifference We cannot speak of an observation as correct or incorrect. In this model of the observer. For example.). We sometimes learn things from A that we could not learn from B. we can always tell what A’s observation is once we know B’s. where even though he can make each of the component discriminations. and hence their observations are not the same. if we properly characterise his scope and the grain of each component. On the other hand. the Principle of Indifference: ‘Laws should not depend on a particular choice of notation. and vice versa. an observer is replaced by a designer. they can both say if it is to their left or right. Mathematically. By including such a broad characterisation of the observer. it is not necessarily the case that B is consistent with A.’ But we are often fooled by the names of things. 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. 13 36 .13 A complete observation by an observer consists of one selection from each set in his scope. it is difficult to say much about observers and their observations. consider A and B looking at a table. we are committing an error of assuming that the observer can observe things he may not be able to do. The product set may be too broad a model for the observer. make as fine grained observations as B (That is. but without a notion like that of ‘correctness’. If A is consistent with B. (The concept of super-superobserver is like the concept of ‘reality’. in many cases. A and B can agree if the penny is on/off the table. there is a great deal of regularity.Ola Larses and Jad El-khoury Views on General System Theory not how close it is to them and each can tell if the penny is on or off the table. Each type of observer/designer/discipline makes his set of observations and there may be overlap in common observations/properties. in that it contains all possible observations. b = (1. 2) and G = (1. 2. our point of view.) The things you observe on the box are a red light (R). This is the role of x-disciplinary analysis. 4. we can talk about different points of view if we are willing to introduce an explicit fiction – the superobserver. But. However. The whistle can have one of six tones. 2). we can reach further understanding of the system (emergent properties) that neither observer could have contributed to. 6). since it only takes a small set of properties from each view. The superobserver needs to have enough viewing capacity which covers the abilities of the other observers (but not more). and vice versa. each will make a contribution to our understanding of where the penny lies. grow much faster that the other observers. and all possible pairs is this the product set which has 14 In Aida2. and there will be an infinite number of possible sequences. j = (1. W = (1. capable of seeing what ordinary mortals cannot.). If the penny is on the table. (notation 1 or 2).14 We have been assuming a special position for ourselves. and that you are a supersuper-observer. etc. you can dominate ‘any’ other observer. Combining these observations. 2. Combinatorial growth is a critical flaw in any discussion of multiple points of view. If we use their observations properly.4 Interpreting Observations States Imagine that you walk into a strange room with a big black box. The superobserver powers. there is little chance of having one in complex situations. 2). Common observations need to be consistent. which is the scope of your observation S = {R. The cartesian product of your ranges produces all possible states of the box. or constraints. which we call the ‘states’ of the light. The range of the light observations is hence. G. we generalise the concept to involve types of observers. That is. in the sequence. The lights can be either on or off. such as a = (1. 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. It is easy to slip into imagining that we can get ‘above the table’ when talking about other people’s viewpoints. In aida2. If we use these observations properly. only a subset of these combinations is needed.2. …. Every thing is always distributed. for though we can imagine that a superobserver might exist. otherwise there will be a lot more writing to do. the sequence is a pair of choices for the set of 24 states. 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. 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. For simple cases. we can learn more about the system. we cannot predict what A says from that of B. 37 . but we really have no reason to believe that we have such super powers of observations. each observer type dominates the other in one way but is dominated in other ways. a green light (G) and a whistle (W). one can ask ‘how fast does the number grow as the length of the sequence grows?’ If there are 2 observations in a sequence. 1). 4). W}. 3. We must particularly refrain from imagining that WE are the superobserver. 1. An analysis view might combine parts from different views but is not seen as a superobserver of these views. resulting in 2x2x6=24 possible states. R = (1. though finite. 1. But. 5. A superobserver’s view must dominate the view of every other observer present. we refrain from thinking or having a super discipline. How much more writing? One cannot ask ‘how many possible sequences are there?’ since a sequence can be indefinitely long.15 5. He needs to be able to dominate all other involved observers. the inventor of the ‘music box’. The box is further examined independently by two other observers. To settle the disagreements. The black box models an observer who cannot or will not influence the system to be investigated. which actually means ’yell at it’. Let us suspend the black-box rules. but is a revelation to the friend. there are 256 possible sequences of length 2. there are no ambiguities. You have been playing a game called ‘black box’. Being constraint. one can use compact means of recording the observations as a mapping from one observation to the one that follows it. such as an astronomer studying the universe. which means that he does not have to discriminate as many state as the superobserver does. traversed twice. When you ask about the lights.Ola Larses and Jad El-khoury Views on General System Theory 24x24 members. and the behaviour of the box is predictable after just one observation. concludes that it only changes between two sequences of states. If the mapping is not one-to-many. and to get the music box to change tunes. the inventor explains that the music box plays three national anthems. Based on the purpose the designer has. you have no power at all (impotent). We may believe the world to be independent of the percipient observer. you give the box a tap. the purpose of the box is known. So far. 16We could map your superview onto his (Each of your states maps to one of his states). upon observing the box. and other aspects are ignored. 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. may 16 AIDA2: Note the relation between the purpose and ignoring certain states in the previous two sentences. Why where there disagreements between you and the other observers? The inventor explains that the box plays six different notes. There. or (24 power n) sequences of length n. different aspects of the system are of interest. all observers walk into the room together. Instantly. When he demonstrates this. and that it is not determinate in its behaviour. For example. You touch a spring and a little door opens. since he is slightly deaf and can only hear three notes. The Eye-brain Law After experiencing being a superobserver. A friend. By applying the ‘principle of indifference’ to ‘you’ and ‘others’ in the previous sentence. the third sequence of ten states maps to a sequence of 5 states in the inventor’s view. Inside the door. but we definetly feel it depends on the participant observer. you should never be surprised to see something that others do not see. he sees only six states – the six notes. it works just the way I said’. Human beings often interact with the systems they observe. Now the mystery is cleared. which you know. but that the last sequence is 5 states long. we get another insight that is a little harder to accept. 38 . As a superobserver. The inventor. everything is OK. To him. The structure of the two views is also different. As long as one of them is on. Therefore. or else be lucky and see highly constrained sequences. Since the inventor ignores the lights. but not visa versa (Each of his states maps to more than one of your states). everyone declares in unison: ‘See. A stranger. and endow you with limited powers of interaction. a sign says ‘KICK ME’. he says that they are nothing to do with the box. the inventor replies. 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. A superobserver would need a super memory if he’s to remember everything he sees. ‘what lights?’ When you show him the lights. you are not omnipotent (all powerful). you have been a passive observer. for example. agrees that there are three sequences. where the observer is impotent to manipulate the box by looking ‘inside’. all you have to do is to ‘kick it’. So. Although you are omniscient (all seeing). See-it-all does not mean know-it-all. mental power can compensate for observational weakness. As systems get more complex. they are lucky that the measurements are not overly precise. based on what he believes are the important features of the system. there may be situations in which the system exhibits behaviour to which all observers agree. the divergence of views between different observers increases. Discriminating too many states has been defined as undergeneralisation.Ola Larses and Jad El-khoury Views on General System Theory see an A or a D state after state A. but he can decide on its scope and range of observation. Memory is of no use unless the future is like the past. there would be no psychology. if the inventor can remember the previous two states. since observations do not entirely depend on our observation characteristics. But. Mathematically. stating that z depends on. he can predict the next state. Newton might have simply said F = 39 . the experienced doctor needs fewer laboratory tests than the intern to make the same diagnosis. unless specific constraints exist to keep them from occurring. the BrainEye Law is: ‘To a certain extent. The substitution of mental capacity for observing power is an illustration of a general law about observers. which if they were made more precise (as precise as can be made today). we must forgo some potential discrimination of state. Science deals with repetitive events and each science has to have ways of lumping the states of the systems it observes. 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. The problem of science is to find the appropriate compromise. The observer is not allowed to change the box. Newton based his law of universal gravitation on certain observations. for knowing means knowing how to ignore certain details. his work would have been much more difficult. Functional Notation and Reductionist Thought The blackbox model of observation gives a passive view of the investigation process. in order to generate repetition. called the Eye-Brain Law: ‘To a certain extent.’ If psychologists saw every white rat as different. we mustn’t try to learn everything. he may miss the big picture. b and z as far as we know or care at present. Although the superobserver sees the fine details. x). the latter the product of the interaction of the body possessing certain primary qualities with the sense organs of a human or animal observer.’ Through symmetry. 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. despite their different observational powers. this can be represented in ‘functional notation’ such as z = f(a. no two situations would be exactly alike. The balance between ‘eye power’ and ‘brain power’ cannot be pushed too far in either direction. b.’. in order to learn anything. observational power can compensate for mental weakness. A state is a situation that the observer can recognise if it occurs again. This is the Lump Law: ‘If we want to learn anything. The Generalised Thermodynamics Law Page 99: Galileo distinguishes between primary qualities of matter and secondary qualities – the former inherent in matter itself. but the intern can substitute a good laboratory for the years of experience lacking.’ Given raw. (second law). some possibility to learn everything. Thus. This Law will not work if there are no constraints at all on the observations. but in practice.’ As examples of the Eye-Brain Law. However. Scientists are envisioned making the most precise measurements as a basis of theories. detailed observations of the world. and only on a. and hence no state will ever occur again unless we lump many states into one. This is put into the Generalised Thermodynamics Law: ‘More probable states are more likely to be observed than less probable states. There are two main fallacies that can be committed during this reduction. Such a limit leads to a situation called ‘complementarity’ of observation. g(…)) shows the deeper levels of dependencies in a compact form. Consider the earlier observations of the black box at the start of the chapter. or he would need to observe two successive states. To make his view state determined. giving Vt+1 = h(Vt. The inventor’s observations are presented as Vt+1 = h(Vt. c). and we are left with the arbitrary choice between different views that fit the observations. …) or refine our observations. since science explains by reducing one phenomenon to the terms of other phenomena. even if we do not know what these quantities are. in the sense that it is not state determined. M)/r2. Vt-1). and speaking of the relationship as ‘wrong’ means that the specific equation of T is not in this set. tells us that our view is incomplete. If T sometimes changes while a remains constant. The box is black and we cannot see inside to say which is the ‘true’ structure. 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. M. such as stating z = f(x. On the other side. The observations of the superobserver can be describe as St+1 = f(St) where St is the state observed at time t. Is T not varying because T is not dependant on one or more of the quantities. or because the situation will not admit further reduction. the reduction must eventually stop either because of the limited capacity of the observer. b. I. overcompleteness occurs if we observe that T does not depends on a at all. Given a function T = f(W. This can only be concluded from the observation of the behaviour of T and a. D). where T remains constant irrespective of the value of a. …) because his views are not state determined. We can use functional notation to show our intentions to do so. …)/r2. y. even thought they can themselves be dependant on other quantities. we cannot discriminate between the two cases. …). we might desire to further refine the model by expressing D as a function of other quantities. Similarly. 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. I. His observations are ‘state-determined’ since the state at one instance is completely determined by the state at the previous instance. b and c. D) implies that the quantities in parenthesis are independent. Consider the more complicated system where T = f(a. through its behaviour. I. even if the view is complete. On the other hand. incomplete knowledge can be denoted as F = h(m. t. We hence either expand our relationship to T = f(a. then we have to conclude that either T depends on something other than a or that T or a are measured incorrectly. t. but they do not have the information needed to make the choice. or because the effects of the quantities cancel each other out? Given the finite set of observations. Being superobservers. 40 . Secondly. The black box. functional composition such as T = f(W. for example F = g(m. by writing D = g(…). in the sense that for the given discussion. y) when it really is z = f(x. we can talk about this expansion of observation or memory power.The form T = f(W. A scientist may commit a Fallacy of Incompletness by omitting some quantity from one of the functional relationships. t. he could expand his impression of what a state is to include the lights. we are not interested in the functional dependencies of these quantities. m.Ola Larses and Jad El-khoury Views on General System Theory f(M. It cannot however tell us how to complete this view. Functional notation can be mixed with explicit formulas to show ‘intermediate stages of knowledge’. r) before he could give the exact form of the gravitational forces between two masses. The notation of decomposition of functions is appealing. and where T remains constant for a finite set of varying values of a. observers do not make infinitely refined observations. A different observer. its velocity is to be deduced by measuring the blur of the photograph. Page 121: Reduction is but one approach to understanding. and you again see the states a through x. … Reduction sometimes works. but because of what is to be observed as of primary importance. This arbitrariness ensures that different observers will have a multitude of ways in which to interpret their observations. 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. It is not always necessary to remove complementarity between two views. … Reductionism is an article of scientific faith. This is the idea of complementarity: two mutually irreducible points of view that are not entirely independent. Even two economists. Neither view can be reduced to that of the other. At any level of observation. The Generalised Law of Complementarity The second reason for the failure of reduction is that of the problem of complementarity. Because we are scientists. once it no longer yields new observations cannot resolve this isomorphism unless the box is opened. If. 5. 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. we believe that our methods will work more often. The views of our two observers of the earlier blackbox. the two models would have to fit all ‘possible’ data. Since the car is moving. there will generally be complementarity. not just because of the different choice of isomorph. and we must content ourselves with complementary views. who sets his shutter speed differently. you observe 41 . while viewing the same situations. Getting an accurate velocity measure will reduce the accuracy of the position and vice versa. Notice the complementary nature of this method.2. etc. by further reduction. are complementary. although there will be some correspondence between their views. then between any two points of view. By this refinement will eventually reach an end. An economist and a sociologist looking at the same system. This gives the General Law of Complementarity: ‘Any two points of view are complementary’. 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). nor were the views entirely independent since certain things can be derived from each about the other. for whatever reason. One way to escape this complementarity is by using more refined measurements such as a less grainy film. At the same time. the choice of isomorph is strictly up to us depending on our memory capabilities. but there is no hard scientific evidence for that – only faith. (Mathematically. for nobody has ever observed the final reduction of any set of observations. do not care if their views are reconcilable since they are aware that they are looking at different things. although it might be possible. Note that complementarity between two views does not only occur when the reduction of the two views cannot be further performed. and hence whatever shutter speed we choose will involve some compromise. Given that you have limitless mental power.) Black box observation. but we use the term in a more limited sense. might not bother to reconcile their different observations by reducing their observations.Ola Larses and Jad El-khoury Views on General System Theory Two models that fit all observed data are said to be ‘isomorphic’. will see a different – or complementary – picture. Consider again that you are a superobserver of the earlier blackbox. but we must admit that other methods sometimes work too. one among many. that is. previous knowledge.5 Breaking down Observations We will here discuss how the limited mental power of the observer influences the observations made. you are only capable to remember the last 10 state transitions of the system. talking of one thing in terms of the other. then refines and simplifies it. and hence are no longer able to see the cycle. but we do know how it feels in the presence of a red rose. By studying the past. might allow a better decomposition. We are like a handyman that carries a single toolbox to perform electrical. …)). which would be unnecessary has it not been for our limited brains. 2. The Metaphors of Science Trying to cope with complex phenomenon. This assumption is based on an article of faith. the chemist. and we hence need to fit this view into earlier experiences. Page 142: One of the problems of specialisations of the sciences is that scientists in different fields have few 17 common experiences to serve as the basis of communication.Ola Larses and Jad El-khoury Views on General System Theory that the system has a 20 state cycle. A physicist recognises entropy and density. We may not know how Burns feels about love. since it may not conform to the psychological categories we have either inherited or learn from the past. From time to time. we will use the tools described later in this chapter. or feel we know. at the expense of having to deal with 2 smaller systems instead of one. In this comparison. (loved one = f(rose. Depending on the choice of properties. the future was like the past. Can a system’s behaviour always be decomposed into independent parts? This depends on the set of qualities being observed. your understanding and grasp of the system will hence vary. the Axiom of Experience: ‘The future will be like the past. of a system. each of which is state determined. in the past. or the economist profit and marginal utility. valence and PH. whose essence is the metaphor. While these goals are often met. red rose) This works since we know. so to reduce the mental effort required. or whatever work. Science and poetry are much alike. Scientifically. 3. You have actually invented a new way of looking at the world. This decision is based on the assumption that future work will be like those received in the past. Page 134: This is the method of science. and hence a more simplified view. the handyman may be able to develop a more useful box of tools. Consider poetry. decomposing the system into independent qualities. But given that you have limited mental powers. we try to: 1. In order to deal with this problem. Burns depends on the universal experience of roses and colour perceptions. get a ‘complete’ view. 42 . You try also to only focus on the tones and succeed in observing the complete cycle of 6 states. get an ‘independent’ view. the resulting view may not be ‘natural’ or ‘satisfactory’. the decomposition into independent parts enabled you to predict the system behaviour better. since that reduces the number of states to 4 and hence increases your chances to remember the complete cycle. Our limited mental powers do not allow us to carry a different view for every moment of our lives. by lumping states that are unnecessarily discriminated so we do not overtax our observational powers. a tool may be replaced by another. carpentry. having been drugged by the inventor. (Burns: My live is like a red. You now succeed in seeing the state determined. you decide to narrow your view and only observer the two lights. A more appropriate set of qualities of the same system. Like a poet. a scientists starts with a complete view. but smaller system. get a ‘minimal’ view. if it is found to be more generally useful in the future. The ultimate reductions are finally 17 In Aida2. a metaphor is like a function. because. However. reducing the original function to a function of other things. by decomposing the system in two independent parts.’ 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’. broad enough to encompass all phenomena of interest. some properties of one thing that we can transfer over to the other. as common experiences for communication between engineers. if we are to make more specific conclusions about a system. and hence can partake of both system and environment. and particular to our experience of ‘boundaries’. etc. such a boundary ‘connects’. Not all systems exist in the physical world. These things are the possessors of ‘properties’ or ‘qualities’ that they carry around with them. not all systems can be separated from their environment in a sharp. 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. and beyond that we would need further support. for it reminds us to pay attention to the connection. A system boundary may not be infinitely thin. This metaphor is so deep that we seldom know that we are using it. we would need to progress to a more precise description of the separation. and can be isolated from other properties by isolating the thing from other things. Page 143: by examining the metaphors of science. But. We might explain one set of qualities in terms of another. 43 . but we should remember that the primordial set is obtained by ostensive definition. and not just the separation. We call such a definition by pointing ‘ostensive definition’. System thinkers use the term ‘interface’ to describe that type of the world that looks both inside and outside at the same time. difference in texture. a part is represented on paper as a closed region surrounded by a boundary. red rose’. Our choice of boundaries makes a difference in the effectiveness of our thought. These ultimate reductions must be rooted in observation of the world. we are really saying the same kind of thing as ‘My love is like a red. By the Principle of Indifference. because it is attached to it. Problems arise since our choice of boundaries is generally influenced by previous experiences. 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. While the graph implicitly says that the system has sharp. between system and environment. Our use of the ‘thing’ metaphor is closely allied to our experience of physical space. Even so. Graphs with bounded boxes are useful in systems thinking. We choose easily recognisable physical features such as a sharp change of colour.Ola Larses and Jad El-khoury Views on General System Theory assumed known and left undefined. we can draw a line around something and easily discriminate ‘inside’ from ‘outside’. Moreover. but lures us when the boundaries are not welldefined. while a connection is represented as a line or arrow. We commonly consider the hair to be part of our body. sharp boundary. however. It can hence be treated like other excrements such as perspiration and urine. we already encounter difficulties of reasoning when we are dealing with systems with physical boundaries. Conventionally. clean way. By analogy. we apply this concept to all our systems. our brains are limited to about 15 boxes at a glance. On the surface of the earth. which will come from several directions. For the physiologist. which have been excellent guides most of the times. ‘Interface’ is a more useful word than ‘boundary’. using the term ‘system’ to mean ‘inside’ and ‘environment’ to mean ‘outside’. As scientists. Qualities and the Principle of Invariance We cannot explain what we mean by a certain ‘quality’. where solid meets a liquid. since it has been secreted from the body and does not take part in the body’s physiological processes. hair is better thought of as outside of the body. we can call either one the ‘system’. for one man’s system may be another man’s environment. except by pointing to the states which have different values of this quality. we can learn about the limitations of the brains that do science. Rather than separating. x) is not in the set – then the whole idea of quality breaks down. 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. a quality will not satisfy our idea of a quality if we cannot consistently identify it with a particular state. and we can hence derive the more general Invariance Principle: ‘With respect to any given property. When the partition describes a quality. then the densities would be different. However. the attempt is faulty because this property 44 . A quality is a way of grouping states of a system. but this actually means that we are more accustomed to observing in those terms. there are those properties that are preserved by it and those that are not’. In general.’. The Principle of Invariance can be restated: ‘We understand change only by observing what remains invariant. if we attempt to divide a village into groups of ‘cousins’. consider the act of dividing the system’s behaviour into qualities. the set consists of the Cartesian product of the set of states that has the given value of that quality. since it has been found more useful to observe in those terms. More generally. As we work in less familiar situations. our learned capacities become less effective. we cannot say precisely what we mean by a certain quality because there are an infinite number of possible transformations that can be performed. and thus describing a quality: ‘For every state x. it means that the quality cannot shift back and forth with time while the state remains the same. We may think that certain qualities are more ‘natural’ than others. of taking a relative quality as an absolute one. For example. The reflexivity condition prevents us from the erroneous absolute thinking. x) must be in the relation. there exists three mathematical conditions that must be satisfied. For example. then neither part have any density at all. we identify states by the shifting of quality values. The ‘sameness’ and ‘difference’ operations allow us to identify and explain the quality in question. we have to introduce another operation besides ‘sameness’ and ‘difference’ for states.’ Partitions As an example of the division of a system into parts. the pair (x. while an extensive quality depends on maintaining the full extent of the system. For example. if the block is divided into its chocolate and peanut parts. A partition is defined by a set of ordered pairs of the parts (states). there are those transformations that preserve it and those that do not preserve it’. such as mass. In order to define a ‘sharp’ partition. and permanence only by what is transformed. A quality may be characterised by the transformations that preserve it. the density quality of a chocolate block is extensive when related to the act of cutting the block in half. If we start with the idea of qualities. This is the ‘reflexive’ condition. These concepts are defined ‘relative to some act of breaking’ of the system. Physical scientists differentiate between ‘extensive’ and ‘intensive’ qualities depending on what happens to the quality when the system is divided into parts. or a transformation may be characterised by the properties it preserves. the quality of mass is defined by the states in which masses are the same or different. Clearly.Ola Larses and Jad El-khoury Views on General System Theory Qualities have a mental function for observers with limited memory. ‘Breaking into parts’ can be generalised into ‘transformations’. if the chocolate block is divided into the qualities of flavour and consistency. Hence. This leads to the mathematical condition for describing a partition. An intensive quality is one which maintains the same quantity after the system is divided such as density. If a state x does not have the same value of the quality as the last time that state was observed – the pair (x. such as ‘greater than’. If we want to ‘measure’ the mass quality. then you have probably broken the system. in which the relation between each pair describes the relation ‘has the same value of the quality’. or restated in terms of transformations: ‘With respect to a given transformation. and hence there is no complete partition. Consider trying to partition a village into groups of ‘friends’. because of our limitations. Note that the assignment of state of a two-variable system to points in a physical plane is arbitrary. and not an absolute property. 5. Considering a symmetric definition of the ‘friend’ property. Even if A is a friend of B. 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. such that a human cannot detect any of these differences. our claim will gain some strength. the white box makes it easy to uncover the source. A digital computer. then an observer may classify A as the same colour as B as well as B as the same colour as C. System can be simulated by building scale physical models. and B is a friend of C. with its advantage of being programmable. For a simulation to demonstrate understanding. But. we can never be sure that the simulating system captures all the properties of the studied system. such as in ship building and planes. But. and every state has its place.Ola Larses and Jad El-khoury Views on General System Theory is a relation between two parts.6 Describing Behaviour Simulation – The White Box In order to understand a system. and another system can be constructed to reveal its behaviour. State Spaces When dealing with systems with a large number of states. or using ‘analog computing’ in which electrical circuits model the system under study. no clear division of system into subsystems. In certain understanding of the ‘friend’ quality. The white box. where there is a place for every state. are needed to represent them. the it should also be the case that state y has the same quality value as state x. it is not necessarily the case that B considers A as a friend. no clear separation of system and environment. Transitivity may not hold with qualities involving graininess. But. If the system can be composed of two qualities. Certain arrangements may 45 . we can draw the states using Cartesian coordinates. is another approach in which the inside of the system is perfectly revealed.2. new tools. but A as not the same colour as C. then for transitivity to hold. which is not necessarily the case. Just building a white box of a system does not guarantee that we understand all of the system’s properties. even if A considers B as a friend. The third condition is that of ‘transitivity’. but once the property has ‘emerged’. without observing the behaviour – black box view – we may not have seen the property at all. For example. and B is slightly more blue that C. 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’. a black box approach can be taken in which the system could only be known through observing its behaviour. while the difference between A and C is noticeable. other than drawing the different states and the movements between them. If state x has the same quality value as state y. and assuming that reflexivity is satisfy by assuming that one is one’s own friend. but assemble a model from smaller number of parts from which the behaviour is generated. that is qualities in which the sensing device has a minimum resolution level under which it cannot detect differences between two values. Resolution levels are part of any measuring process. With graininess transitivity may not hold. is a more accessible simulation tool. if A is slightly more blue than B. then A must be a friend of C. or simulation. as we shall see. we do not simply build a model that mimics or copies the system. you do not understand how to use it. after discovering that a certain variable is missing. which indicates that something is wrong with our point of view if the system is to be state determined. Of course. we suspect that the system is uninfluenced by external factors. has to be reached again and the cycle starts again. we have the Diachronic Principle: ‘If a line of behaviour crosses itself. saving the effort of looking for inputs or trying to make the system description more complete. where I is the ‘input’. ‘project’ or ‘subspace’ respectively. The opposite of projection is that of ‘expansion’. Every finite state determined system has cycling behaviour since eventually a state. we may want to find such an assignment so as to reduce our effort at describing the behaviour. where the ‘something else’ is unknown to the observer. or ‘randomness’. …). Behaviour in Open Systems Scientists prefer to study a state-determined system because its behaviour is simple. Sx. and hence ensures that no cycles or crossings occur. any closed system is state determined. a crossing poses no problem. It). The addition of time also allows us to project each other dimension onto a two dimensional graph as a function of time. may be seen in the system either if the qualities observed are not complete. Note however that in a projection. and this determinism is created by trying to fully enclosing the system. Operations such as ‘projection’. we look for an input. We speak about the set of behaviours of an open system as ‘Behaviour’ – capital B. but a set of behaviours selected by the input. ‘dimensional reduction’ can be performed on such a space producing a ‘section’. called a ‘state space’. since it becomes a projection of the new set of dimensions. Page 196: If you cannot think of three ways of abusing a tool.Ola Larses and Jad El-khoury Views on General System Theory appear to yield a continuous line of behaviour. If we insist that the system is closed. We might as well say St+1 = F(St. Generally. Indeterminism. An observer has no way of determining the cause of this randomness. A crossing represents two different paths emanating from the same point. but we should remember that this appearance is a consequence of our assignment of numbers to quality values. we will assume that there exists ‘randomness’ in the system. The need for a new dimension may be discovered when realising that the line of behaviour of the system seems to cross itself. Projections and other transformations help us overcome the limitation of our brains to handle many dimensions. This is exactly the same form we would give an open System St+1 = F(St. or we are viewing a projection – an incomplete view. since depending on the inputs from the environment the behaviour of the system part varies. or if the enclosure is leaking and hence the system is open. An open system has normally not a single line of behaviour. A state determined system is represented by St+1 = F(St). Such operations may be useful in order to reduce the system complexity or the mental power needed. Time has the property of always moving in one direction.’. and that the external factors are too small to influence the system. Old work needs not be thrown away. in which a new dimension is added. or influenced by cyclic external factors. For behaviour represented in a state space. The closed system fiction is a useful heuristic device. in general. a system with n qualities can also be mapped onto an n-dimensional space. the ‘system’ part will no longer be state determined. then either the system is not state determined. Another way of handling the surplus of dimensions is by introducing the dimension of time. at the expense of loosing certain information. This may be necessary when studying a system. If we see non-cyclic behaviour. while a ‘random’ system is represented by St+1 = F(St. 46 . From this point of view. When we see cycling behaviour. ‘sectioning’. If we partition a state determined system into a ‘system’ and an ‘environment’. Rt) giving the name R for that randomness. is the ‘behavioural’ view. W. which says that the only way we know ‘structure’ in the first place is by observing behaviour. and behaving: state spaces. and to discard those ways that do not. randomness and the black box. from being to behaving.Ola Larses and Jad El-khoury Views on General System Theory Given that the behaviour of an open system is influenced by inputs from the environment. or ‘function’. unless he has white box knowledge of the system. From this view. Invariance in time helps to identify the significant units of a mature system. a pattern of matter in space. from form to function. along a longitudinal section in time appear the transient and reversible changes. Openness complicated prediction and observation. This gives us the Used Car Law: ‘A way of looking at the world that is not putting excessive stress 18 In aida2. that constitute ‘becoming’ or developing. 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. boundaries and the white box. as observers. from pattern to process. and how particular structure leads to the production of particular behaviour through the execution of ‘programs’. chronological graphs. diagrams of structure.2. 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.18 We are perfectly entitled to identify systems in any way we choose. this is only a convenience. The recipe for effective thinking is to use those ways of identifying systems that focus on what interests us. from the timeless to the temporal. 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. Being is the cross section of an entity in time. We have discussed the ways we picture ‘being’: the notion of set. it lets us gain predictability by allowing us to act on the system. 47 . as well from its own behaviour. a pattern of events in time. We believe in the Law of Effect: ‘Small changes in structure (white box) usually lead to small changes in behaviour (black box)’. which is the variable functioning of the permanent ‘structure’ P. we tend to partition systems into a fixed and a variable part. 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’. are entangled with what we observe. that constitute ‘behaving’ or functioning. there should be really no precedence or favouritism between the structural and behavioural models of our systems. input. In V we see the ‘behaviour’. properties.7 Some Systems Questions Page 227: R. 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. in which the fixed part – or structure – is the ‘source’ of its behaviour. And with this shift in time there occurs a shit in entity of concern – from an object. Although we may identify the system by its functioning. These are two complementary ways of looking at the world. the Law of Effect can be restated as: ‘Small changes in behaviour will usually be found to result from small changes in structure’. for the ‘real’ identity lies in the ‘structure’. We partition the system into two sets of variables. often repetitive. We have investigated the role of the observer in these things with the conclusion that we. entangled in ways that leave ultimately indeterminable which is being and which is believing. Because of our belief in the Law of Effect. yet at the same time. Gerard: … But the real shift here is from a focus on organisation to a focus on action. and the enduring and irreversible changes. to a behaviour. often progressive. an observer cannot tell the reason for certain behaviour. 5. Conversely. Complementing this ‘structural’ view. P and V. Look for one about engineering. Ann Arbor: Society for General Systems Research.8 Further readings 1. we adopt the principle that we want to reduce the stress on the observer. 1956-1974.2.ca/u/mbldemps/systems/systemdef. 48 .htm for system definitions 19 In Aida2. Look at www.fes.Ola Larses and Jad El-khoury Views on General System Theory 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. Specific examples of the application of GS are found in Ludwig von Bertalanffy and Anatol Rapoport Ed... General Systems Yearbook. 2. Vols 1-19.uwaterloo. by choosing view points that best fit their view of the world.