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Systems Thinking View on the Situation of Unemployment in the USA

Diplomarbeit 2003 160 Seiten

BWL - Wirtschaftspolitik



1 Abstract
1.1 Part I “THEORY”
1.3 Endnotes Chapter

2 Part I “THEORY”
2.1 The Philosophic Background or “Why System theory?”
2.1.1 From Laplace’s Daemon to System Theory
2.1.2 Endnotes Chapter
2.2 Systems Thinking - an Overview
2.2.1 The development of Systems Thinking
2.2.2 Definition of Systems Thinking
2.2.3 The tools for Systems Thinking Causal Loop Diagrams applied within the Thesis Determining the Loop Polarity Naming and Numbering of CLDs within the Thesis The capability of CLDs The Paper Computer (PC) applied within the Thesis Description of the Paper Computer (PC) Ascertainment of the Steering Possibilities Design of the Intervention Summary of the Practical Approach to Systems Thinking
2.2.4 Endnotes Chapter
2.3 Introduction to Conflict Management
2.3.1 About Conflicts
2.3.2 About Consensus
2.3.3 Application of Conflict Management in the Thesis Hypotheses Concerning Aporias in the Observed System
2.3.4 Endnotes Chapter
2.4 Motivation for the Thesis “Systems Thinking View on the Situation of Unemployment in the USA”
2.4.1 Endnotes Chapter
2.5 Summary of Part I

3.1 Enquiry of the Situation of Unemployment in the United States
3.1.1 Unemployment Insurance (UI) in the United States Objectives of unemployment insurance Types of Unemployment Structure of the Unemployment Insurance System in the US Overview of the State Program Administration The Mechanism of Unemployment Insurance (UI) in the U. S The Current Situation in the United States (May 2003)
3.1.2 Different Views on Unemployment View on the Social-Psychological Effects of Unemployment View on the Macroeconomic Standpoints towards Unemployment
3.1.3 “The President’s Plan for Jobs & Economic Growth”
3.1.4 Endnotes Chapter
3.2 The Interview Research
3.2.1 Definition of the Systems of interest Key of Abbreviations Used for the Model
3.2.2 Model of the Interview Research
3.2.3 Setup of the Interviews Circular Questioning in General Circular Questioning shaped for this Thesis
3.2.4 Structure of the interviews Chronology General Research Questions
3.2.5 Analysis of the Interviews Interview Results Summary Subsystem Training_[sst] Interview Results Subsystem Unemployment_[ssu] Interview Results Subsystem Unemployment Work_[ssuw] Interview Results Subsystem Work_[ssw]
3.2.6 Endnotes Chapter
3.3 Elaboration of the Basic CLD of the Unemployment Situation in the United States
3.3.1 List of Variables Used in the Basic CLD of the Unemployment Situation in the United States
3.3.2 Description of the single Causal Loops of the Basic CLD of the Unemployment Situation in the United States
3.3.3 The Basic CLD of the Unemployment Situation in the United States
3.3.4 Paper Computer Analysis of the Basic CLD of the Unemployment Situation in the United States
3.3.5 Ascertainment of the Steering Possibilities
3.3.6 Interventions According to the Rules to Evaluate System Intervention
3.3.7 Summary of the Practical Approach of Systems thinking

4 Discussion of the Research
4.1 Discussion of the outcome related to hypothesis
4.2 Discussion of the Outcome Related to Hypothesis
4.3 Discussion about a Practical Application for the Thesis
4.4 Future Outlook
4.5 The Vision
4.6 Endnotes Chapter

5 Explanation of Important Vocabulary

6 Bibliography
6.1 Books involved in the thesis (secondary literature)
6.2 Additional Books related to the issue (tertiary literature)
6.3 Tools Related to Systems Thinking

7 Index

8 Appendix Questioning Forms
8.1 Interview Question Form for People Involved in the Subsystem Work_[ssw]
8.2 Interview Question Form for People Involved in the Subsystem Unemployment Work_[ssuw]
8.3 Interview Question Form for People Involved in the Subsystem Unemployment_[ssu]
8.4 Interview Question Form for People Involved in the Subsystem Training_[sst]

Figures and Tables

Figure 2-1 Diagram out of G. Ossimitz’s Book “Entwicklung Systemischen Denkens”

Figure 2-2 Example out of P. Senge’s book The Fifth Discipline

Figure 2-3 Locally Stable Equilibrium

Figure 2-4 Locally Unstable Equilibrium

Figure 2-5 Simple Example of a Causal Loop Diagram with a Key to the Notation

Table 2-1 Link Polarity: Definitions

Figure 2-6 Example of Marking a Delay within a Link

Figure 2-7 How to Trace an Effect of a Change

Table 2-2 Example of a Paper Computer Matrix

Figure 2-8 Example of a Diagram Developed from the Paper Computer Matrix

Figure 2-9 Steering Model of a System

Table 2-3 Catalog of Arrangements

Table 2-4 Rules to Evaluate System Intervention

Figure 2-10 G. Schwarz, Plato’s Two Different Kinds of Problems

Table 3-1 Type and Level of Minimum Qualifying Requirements in the USA

Table 3-2 Weekly Benefit Amounts of the 52 States of the USA

Table 3-3 Types of Duration Provisions Appearing in the USA

Table 3-4 Federal Extended Unemployment Benefit Programs. 1958 to

Figure 3-2 Advanced Basic Structure Showing the Occurring Communications

Figure 3-3 The Three Layers of the Interview Research

Figure 3-4 Reinforcing Loop R1 duration, family, learning

Figure 3-5 Reinforcing Loop R2 duration, motivation

Figure 3-6 Reinforcing Loop R3 duration, problems, prejudice

Figure 3-7 Reinforcing Loop R4 community, education

Figure 3-8 Reinforcing Loop R5 business, employment

Figure 3-9 Reinforcing Loop R6 science, efficiency

Figure 3-10 Reinforcing Loop R7 governmental jobs, communities

Figure 3-11 Reinforcing Loop R8 economy on its own

Figure 3-12 Reinforcing Loop R9 education, self confidence

Figure 3-13 Reinforcing Loop R10 information, society

Figure 3-14 Reinforcing Loop R11 information, employers

Figure 3-15 Reinforcing Loop R12 problems, prejudice

Figure 3-16 Reinforcing Loop R13 benefit, cheat

Figure 3-17 Reinforcing Loop R14 education, skills

Figure 3-18 Reinforcing Loop R15 government, shifting the burden

Figure 3-19 Balancing Loop B1 natural unemployment

Figure 3-20 Balancing Loop B2 science costs

Figure 3-21 Balancing Loop B3 governmental jobs, costs

Figure 3-22 Balancing Loop B4 benefit, unemployment problems

Figure 3-23 Reinforcing Loop B5 school system, costs

Table 3-5 Mistakes in Dealing with Complex Systems

Figure 3-24 Basic CLD of the Unemployment Situation in the United States

Table 3-6 Matrix of the Variables used for the Basic CLD of the Unemployment Situation in the United States

Figure 3-25 Diagram of the Paper Computer Results of the Basic CLD

Figure 3-26 Steering Possibilities for the System Country

Table 3-7 Catalog of Arrangements for the System Country

How to Read the Thesis

Following these instructions helps to optimize the reading of the current paper.

The main parts of my thesis, Part I “THEORY” and Part II “REALIZATION AND RESULTS” are explained in the “Abstract”, chapter number 1.

The reason why I choose the structure explained below was to fulfill two demands.

1) I wanted to provide a paper that is easy to read and focuses pragmatically only on the issue itself. The reader that is just interested in the researched field of unemployment should not be distracted through too much theoretical background of system theory and economic theory.

So if you are just interested in the core issue skip all the parts that have the heading “Endnotes Chapter x”.

2) I also wanted to give the reader who is interested in the philosophy and the background of system theory and economic theory a possibility to go deeper into it.

If you want to explore some theoretical background and more specific details on the researched field of unemployment in the United States read the entire paper.


In my thesis I use footnotes and endnotes in a specific way. Footnotes are used to show the referred literature (like usual in such papers). In the chapter of the endnotes I quote the several authors and write my comments to that. Footnotes are written on the bottom of the page where it appears. The numbering of Footnotes is with Arabic numerals starting with 1, 2, 3…, n. The numbering of footnotes is continued to the end of the whole document. Endnotes are written under a heading that looks like: {“Endnotes Chapter x”} The x is the number of the chapter the endnotes are relating to. Endnotes are indicated through a capital letter of the alphabet starting with A, B, C, …, Z. Endnotes start with “A” again if a new chapter is started. So every part of my text that is based on special literature has a combination of a footnote and an endnote. Example: {...(1, A)} the footnote, in the example 1, leads to the bottom of the page. The text at the bottom of the page related to the footnote shows the source of the information. The endnote, in the example A, leads to the heading “Endnotes Chapter x”, at the end of the chapter x, and the text related to the endnote is the exact quotation my thesis is referring to. Additional to the quotation I further write down comments to explain how I see the quotation in the context. In some places I use endnotes in combination with footnotes, in some places footnotes alone. Every endnote has its related footnote. Footnote and related endnote are always in parenthesis and divided by a comma. In the text there are some footnotes without endnotes. But there are no endnotes without footnotes.

1 Abstract

The thesis is divided in two parts. Part I “THEORY” explains the theoretical background used for developing the work, part II “REALIZATION AND RESULTS” is the description of the realization and the discussion of the results generated.

1.1 Part I “THEORY”

The diploma thesis has its roots in the general system theory and emphasizes on a holistic view on the situation of unemployment in the United States. The chosen approach is a combination of state of the art scientific knowledge from the fields of sociology, psychology and economy. In the previous work performed for the thesis it was detected that the diversity of the basic approach is necessary in meeting the complexity of the issue. The many different factors influencing the chosen topic of unemployment are widespread. Contributing to the theory of systems thinking[1] the goal of the thesis was to find and describe an existing pattern that makes it possible to see the dynamic of the system. The systemic view takes in account that everything is interconnected and hence interacting. Systems thinking states that there are effects and influences on- and by unemployment that are only visible in applying a holistic view. The reason why the present paper is groundbreaking is not so much because of the used scientific knowledge, which is state of the art, but because of the combination of this knowledge. This combination is meant to regard to one of the tasks given by cybernetics[2] in increasing differentiation instead of increasing growth.([3], [A]) The basic standpoint is that the quantity of existing knowledge is already enough to create possible approaches for ways to optimize the current situation. Only lacking is the understanding of how all the fragments are connected. Systems thinking, an application of system theory, in this context, is seen as a tool that makes it possible to develop a model to generate this understanding.


Because of the vast field that is covered by the topic, and the limited time available, the thesis finally shows

a) The rough pattern of unemployment as an overall picture in the United States of America.
b) The approaches of the United States Department of Unemployment (USDOL).
c) The main streams in economic theory related to unemployment.
d) “The President’s Plan for Jobs & Economic Growth” to prevent and to minimize unemployment.

It will be discussed whether or not the current existing approach is appropriate from a systems thinking point of view. As a very relevant point there is the hypothesis of a basic conflict about unemployment in society. The hypothesis states that the basic conflict that society faces is the question of how much unemployment is a fact given by the “nature” of economy, the “nature” of people and the “nature” of society which includes an aporia[4] that is impossible to resolve with logical approaches.

The thesis concentrates further on the development of a systems thinking model of the unemployment situation. This includes the “Basic Causal Loop Diagram of the Unemployment Situation in the United States”[5]. It includes a computation of the indices of influence[6] to show possible interventions from a systems thinking viewpoint.

The basis is the researched information mentioned above in a) to d). And 20 interviews in four different subsystems related to unemployment. A focus here is the question of effects of vocational training on unemployment. Of special interest is the relation of trainers and trainees in vocational training centers (vocational training centers that do training of unemployed people) and the problems those two groups are facing within the whole system of unemployment in the United States. Here we meet again the hypothesis of an aporia. The hypothesis states that there is an aporia the trainers and trainees are facing, concerning the outcome of the training.[7] The aporia is by its nature the same aporia that society faces as is mentioned above. The exact formulation of the two hypotheses is given in

The above explained research in terms of laws and politics, in terms of science, and in terms of the different viewpoints in the society had the intention to find out the existing situation in the United States. The accumulated outcome in form of the “Basic Causal Loop Diagram of the Unemployment Situation in the United States” and the computation of the indices of influence is finally the background for the summary (see 3.3.7) of the situation. The summary includes the critical standpoint of the systems thinking approach.

Finally a conclusion about the hypotheses and a future out view developed from the systems thinking position is stated in the discussion of the research (see 4).

The thesis is the effort to breakdown state of the art theory to make it practicable in the field. And, to produce results of innovation, in communicating the generated and collected knowledge to the people concerned.

1.3 Endnotes Chapter 1

[A] In his book Frederic Vester states: “…in einem Umschwenken vom selbstzerstörerischem quantitativen Wachstum auf qualitative Umstrukturierung…”.

[“…to change sides from a self-destructive quantitative growth to a qualitative restructuring…”]. F. Vester is pointing out that quantitative growth can not help solve complex problems. In putting this to the issue of unemployment it states that it can not be the answer to increase production. If it was that simple there wouldn’t be any unemployment in the world. It is the intention of the thesis to show an alternative approach in using systems thinking. In principle the idea is that the approach should rather lie in diversification. In his book F. Vester provides some examples of diversification, related to environmental problems.

2 Part I “THEORY”

When we view the world around us

Parts and whole must not confound us.

Nought's internal, nought's external,

Each is other, both eternal.

Swiftly seize then, here revealèd,

Sacred secret, ne'er concealèd.

Müsset im Naturbetrachten

Immer eins wie alles achten:

Nichts ist drinnen, nichts ist draussen;

Denn was innen, das ist aussen.

So ergreifet, ohne Säumnis,

Heilig öffentlich Geheimnis.[8]

Johann Wolfgang von Goethe

2.1The Philosophic Background or “Why System theory?”

This chapter gives a rough picture of the philosophy of system theory. [9]

2.1.1 From Laplace’sDaemon to System Theory

To explain the philosophical background I want to start with determinism[10] in natural science.([11], [B])([12], [C]) Mathematicians and natural scientists, among them S. Laplace started to develop an idea of a deterministic behavior of space. This thought of determinism did also spread to other sciences like philosophy and even sociology. There was a strong believe of many scientists that it could be possible to find out how to have such accurate methods of measurements that life could be steered more or less like a simple mechanical device. Many humanistic sciences like philosophy, sociology, psychology started to search for mathematical formulas and methods to reach the goal of developing methods to navigate humans and societies. The idea of a possibility to operate mankind and thus to gain power over societies on earth, generated a lot of efforts amongst scientists. For historical grounds the humanistic science did develop its concepts very close to natural science and natural science was with determinism in the mood that it is upon to explain the universe and its reason. Some of the darkest decades of those efforts were happening within the last century where an oversimplification of the idea of eugenics[13] ([14], ) led to several inhuman and cruel trials in science. In the early years of the 20th century the sciences, especially the natural science began to doubt determinism. This leads us to Gödel[15], defining truth as a relation stronger than provability.[16] Again mathematicians, one of the most famous was K. Gödel, brought up theories that should change natural science.([17],[D] ) This new viewpoints were very important for mathematic itself (and the roots of chaos theory) and rocked the standpoint of a possibility to produce artificial intelligence out of a mechanistically model of mankind. Prove of the failure of determinism in nature science was finally done in physics by W. Heisenberg[18] and his uncertainty principle.([19],[E]) At that point natural science finally accepted that determinism is a dead end street. Natural science did realize the limits of its efforts and the necessity of new viewpoints. ([20], [F])

The attainment of natural science did again have a huge impact on humanistic sciences. This time it was the initiation of self conscious emancipation. ([21], [G] ) This was historically the moment when several theories occurred that tried to develop a concept that would fit better than determinism. One of those approaches was cybernetics.[22] The efforts finally converted into a general system theory. General system theory has the advantage to be universal applicable in science without putting the different disciplines in danger of being too narrow minded or too restrictive. And, if general system theory is applied in the right way, it does not disturb or mislead the involved scientific parts. ([23],[H]) The vision of system theory is that it enables science to take responsibility, and to be in charge as far as the state of the art allows it to be, to serve mankind as a whole, without being presumptuous. ([24] [I], ) We now finish this journey into the philosophical background of system theory. One of the practical basic approaches of system theory is systems thinking. The aim of the thesis lies in applying a practicable approach for systems thinking in the field of unemployment in the USA. Therefore we have a closer look on the development of systems thinking and on existing methods of applied systems thinking in chapter 2.2.

2.1.2 Endnotes Chapter 2.1

[A] The following explains determinism in detail. “…The theory of determinism states that all events, including human decisions, are completely determined by previously existing causes. Pierre-Simon Laplace framed its classical formulation in the 18th century. For Laplace, the present state of the universe is the effect of its previous state and the cause of the state that follows it. …”

[B] A very common item used is Laplace's Daemon which meaning is:

“…Laplace imagined a perfect being, Laplace's Daemon who could measure the position and velocity of every particle in the universe at a single instant of time, and having once done so would be able to predict the future for ever more. …”

[C] The bold and arrogant viewpoint of nature science, that it finally found the key to influence nature, influenced science as a whole and lead to assumptions that resulted in the effort of science to annihilate the evil on earth. “…The first half of the 20th century saw extreme coercive application of such principles by governments ranging from miscegenation laws and enforced sterilization of the insane in the United States and other nations to the Holocaust of Nazi Germany. Regulated eugenics continues in some parts of the world; China enacted restrictions on marriages involving persons with certain disabilities and diseases in 1994. …”

[D] In the beginning of the 20th century scientists were already heading forward to accept that determinism was a failure. It were again mathematicians and nature scientists that caused the turn. “… In 1931 K. Gödel published his famous Incompleteness Theorems in Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme. In this article he proved that for any axiomatic mathematical system that is powerful enough to describe the natural numbers it holds that:

It cannot be both consistent and complete. (It is this theorem that is generally known as the Incompleteness Theorem.)

If the system is consistent, then the consistency of the axioms cannot be proved within the system.


[E] “…The more precisely the position is determined, the less precisely the momentum is known in this instant, and vice versa.

--Heisenberg, uncertainty paper, 1927 …

…This is a succinct statement of the "uncertainty relation" between the position and the momentum (mass times velocity) of a subatomic particle, such as an electron. This relation has profound implications for such fundamental notions as causality and the determination of the future behavior of an atomic particle. …”

[F] The following four points according to W. Heisenberg’s book “Das Naturbild der heutigen Physik”, give a summary of the development in natural science:


1 Die moderne Naturwissenschaft zeichnet sich in ihren Anfängen durch eine bewußte Bescheidenheit aus; sie macht über streng begrenzte Zusammenhänge Aussagen, die nur im Rahmen dieser Grenzen Gültigkeit haben.
2 Im 19. Jahrhundert geht diese Bescheidenheit weitgehend verloren.
Die Erkenntnisse der Physik werden als Aussagen über die Natur als Ganzes betrachtet. Die Physik will Philosophie sein und verschiedentlich wird gefordert, daß jede wahre Philosophie Naturwissenschaft sein müsse.
3 Heute macht die Physik einen grundsätzlichen Wandel durch, als dessen hervorstechender Zug die Rückkehr zu ihrer ursprünglichen Selbstbescheidung erscheint.
4 Der philosophische Gehalt einer Wissenschaft bleibt aber nur dann gewahrt, wenn sie sich ihrer Grenzen bewußt wird. Die großartigen Entdeckungen über die Eigenschaften einzelner Naturphänomene sind nur möglich, wenn man noch nicht verallgemeinernd das Wesen solcher Phänomene im voraus bestimmt. Nur dadurch, daß die Physik offenläßt, was Körper, Materie, Energie usw. in ihrem letzten Wesen sind, gelangt sie zu Erkenntnissen über einzelne Eigenschaften der Erscheinungen, die wir mit diesen Begriffen bezeichnen, zu Erkenntnissen, die dann zu wirklichen philosophischen Einsichten überzuleiten vermögen.


Modern nature science is in its beginnings characterized by conscious modesty; statements of nature science are only about strictly limited coherences, which are valid only within these limits.

Within the 19th century this modesty is widely lost. Cognitions of physics are seen as statements about the whole nature. Physics wants to be philosophy and on various occasions it is claimed that every true philosophy should be nature science.

Today physics is going through a fundamental change where the outstanding character seems to be the recurrence to its original modesty.

The philosophical content of a science remains only when the science is conscious about its boundaries. The excellent developments about the properties of phenomenon of nature are possible only if one does not place generalized predictions on the entity of such phenomenon. Only thereby that physics leave it open what the entities of body, matter, energy etc. finally are, physics comes to results about single attributes of the phenomena that we refer to that terms, which are then capable to lead over to true philosophical insights.


Though the words of Heisenberg show the new moderate mainstream of science there is still a lot of the deterministic ideas remaining in society. One can observe this in discussions about genes and their impact one people’s character and fate. The standpoint of systems thinking concerning to that issue is explained in part II of the thesis.

One of the mathematical approaches that are heading towards a possibility to handle complexity and uncertainty in a way that seems promising is chaos theory.

Very important here is that one has to accept that the methods used in science still have their validity and that this is not a change in a sense that everything thought before that time is no more useful but much more it is a change in how to use the developed methods and how to position those methods in science and society.

[G] It took a long time for the social sciences to emancipate. And it is still a stigma on social science that contains the doubt of seeing social science as a science of its own. As H. Willke puts it “… Der methodische Minderwertigkeitskomplex der Sozialwissenschaften ist völlig verfehlt. Das besinnungslose Nacheifern naturwissenschaftlicher Exaktheit durch künstliche Reduktion von Zusammenhängen auf wenige Variable schlichter Anachronismus. Denn heute geht es für beide Wissenschaftsbereiche darum, ein neues Instrumentarium für die Analyse hochkomplexer organisierter Systeme zu entwickeln, seien dies Biopolymere, Zellen, Organismen, Gruppen oder Gesellschaften. Und gerade dies ermöglicht und erfordert neue Formen der transdisziplinären Zusammenarbeit, wie sie beispielhaft der Entstehung und Weiterentwicklung der Allgemeinen Systemtheorie zugrundeliegt. …”

[“…The methodically inferiority complex of social sciences is totally mistaken. To follow suit nature science precision unconscious by artificial reduction of coherences to few variables is plain anachronism. Today it is for both areas of science about to develop a new instrument for the analyses of highly-complex organized systems, being this bio-polymers, cells, organisms, groups or societies. Just this enables and demands new means of trans-disciplinary collaboration, how it is based exemplary in the appearance and the further-development of general systems theory. …”]

[H] Another advocate for the general system theory and the potential that lies in it is D. J. Krieger’s who states “… Erinnern wir uns daran, dass Systemtheorie Funktionsanalyse ist. Sinn als System zu betrachten stellt um, nicht nur von Was- auf Wie-Fragen, sondern öffnet den Blick für funktional äquivalente Problemlösungen. Auf Grund der Analyse, wie Sinn bzw. Erkenntnis funktionieren, läßt sich fragen, ob nicht Anders-Sinn und Anders-Erkennen besser wäre. Selbstreflexiv, das heißt auf die Systemtheorie als Wissensform selbst bezogen, wäre die Wie-Frage falsch, das heißt nicht-viabel beantwortet, wenn man daraus einen agnostischen Relativismus ziehen wollte und diesen dann als Legitimation dafür nützte, sich in eine neue, ideologisch verdeckte Form des westlichen Säkularismus zurückzuschanzen. Dies würde die Erschließung eines für die heutige Weltgesellschaft nötigen primären Codes blockieren. Statt dessen könnten sich die Systemtheorie und der Konstruktivismus ihre Viabilität gerade dadurch ausweisen, daß sie den Mythos des Logos als solchen entlarven, ihre eigene Verwurzelung, in Kunst, Mysthik und Weisheit anerkennen und somit die festgefahrenen Grenzen zwischen Wissenschaft, Kunst und Religion aufheben. …”

[“…Let us remind that systems theory is functional analysis. To consider meaning as system changes, not only from What- to How-questions but also opens the view for functional equivalent problem solving. Based on the analysis how meaning respectively cognition functions it can be asked whether or not else-meaning and else-cognition would be better. In a self-reflexive view, that is, based on systems theory itself as knowledge would the How-question be wrong, that is, answered in a not-viable way, if one would conclude an agnostic relativism from that and use it to legitimate to barricade behind a new ideology covered form of western secularism. This would block the development of a new primary code necessary for today’s world society. Instead of that, systems theory and constructivism could prove their viability just thereby, that they debunk the myth of logos as such, approve their own roots in art, mysticism and religion and thus release the deadlocked borders between science, art and religion. …”] Very important in Krieger’s argumentation is that he involves religion as a vital factor of science. In fact I believe that religion has missed to evolve and to adopt to today’s society. I think it should be a responsibility for the religious leaders to do so and to serve humanity. But in observing the existing situation it is more the concern of those leaders to keep their power and to hoard their property. Anyways it would go too far to get involved in this issue for the thesis.

[I] A very interesting point is how the potential of system theory is seen. H. Willke about the potential of systems theory. “…Was Theorien über Systeme organisierter Komplexität leisten sollen, ist denn auch vorsichtiger anzusetzen. Nicht die Voraussage zukünftigen Verhaltens im Detail ist das Ziel, sondern die Voraussage von Verhaltensmustern (pattern prediction), Funktionszusammenhängen, Problemkonfigurationen und Entwicklungslinien, deren Kenntnis nur die Wahrscheinlichkeit erhöht, bestimmte Ereignisse und Ergebnisse herbeiführen oder verhindern zu können. Nicht methodisch perfekte Modellbauerei und Algorithmisierung, die immer exaktere Erfassung von Irrelevantem, ist das Gebot der Gegenwart, sondern der transdisziplinäre Aufbau einer Theorie und Analysemethodik komplexer organisierter Systeme. Grundlagen einer solchen Theorie und Methodik kann dann nicht mehr nur die Logik von Ursache-Wirkungs-Kausalitäten sein, sondern darüber hinaus zusätzlich die Logik komplexer Systeme (treffend dazu Dörner 1989). …”

[“… What theories about systems of organized complexity should accomplish is to put in a more cautious way. It is not the goal to predict future behavior in detail, but the pattern prediction of function-coherences, problem configurations and lines of development, the cognition of which only increases the probability to effect or to circumvent certain events and outcomes. It is not about methodically perfect modeling and put into algorithms, the more and more exact acquisition of the irrelevant; that is not the present command, but the trans-disciplinary assembly of a theory and of a method of analysis for complex organized systems. Basis of such a theory and method can not only be the logic of cause-effect-causalities but furthermore the logic of complex systems (accurate for it Dörner 1989). …”]

The quotations above give an idea of the philosophical background of the general system theory today.

2.2 Systems Thinking - an Overview

“…And all things are ordered together somehow (not all in the same way) -- fishes, birds, plants. The world is not such that one thing has nothing to do with another, but they are connected.”…”All things must at least come to be dissolved into their constituents [from which other things can then be made], and there are other functions similarly in which all things share for the good of the whole.”[25]


“We propose to consider, first, the several elements of our subject, then its several parts or divisions, and, finally, the whole in its internal connection. Thus we proceed from the simple to the complex . But in this subject more than in any other it is necessary to begin with a glance at the nature of the whole, because here more than elsewhere the part and the whole must always be considered together.”[26]

Karl von Clausewitz

In this chapter of the thesis we show the development of systems thinking as part of system theory and come finally to a definition for systems thinking that is taken from G. Ossimitz[27] and seems to be a promising approach. Though G. Ossimitz’s emphasis is on systems thinking in education, his efforts of gathering an overview of the existing situation in the field of system theory and especially his pragmatical focus on systems thinking, brought up a solid foundation from which to operate. This chapter gives also an introduction to causal loop diagramming. Causal loop diagramming is a systems thinking tool that helps to communicate the developed ideas.

2.2.1 The development of Systems Thinking

The growing complexity of life and society initiated automatically the dawn of system theory. Implicitly many philosophers and scientists already knew that they have to encounter complexity in their thoughts. This understanding of scientists made them focus on the interdependency of items. Many books and thoughts through history already focused on the feedback structure of nature. It took until the beginning of the 20th century to develop an explicit theory. Nature science and human science, as well, started to design theoretical structures. The challenge for system theory was to handle complexity in a way that makes it possible for mankind to intervene systems, to take advantage of them. System theory is not very popular and declined after reaching its very height in the 1970s. Though the 1990s brought a revival of system theory and it is starting to attract science in a larger range again. Out of the wide range of system theory the thesis is especially interested in a certain part of it, called systems thinking. In his book “Entwicklung Systemischen Denkens”[28] [Development of Systems Thinking], G. Ossimitz gives a precise overview of the development of systems thinking. Ossimitz also shows today’s situation.([29], [K])[30] [31] of systems thinking. He discusses the most important literature([32], [L]) to the issue. The diagram out of G. Ossimitz book “Entwicklung Systemischen Denkens” gives an overview of the current situation of systems thinking (see Figure 2-1 Diagram out of G. Ossimitz’s Book “Entwicklung Systemischen Denkens”).[33] The basic approach of the thesis is placed in the left area of the diagram.[34] It is a summary of the development of systems thinking related science, starting with J. W. Forrester in the beginning 1950s. Ossimitz points out the two different concepts of systems thinking:

a) Systems thinking (1) tends to be related to system dynamics and is therefore not that universal. One can see it as systems thinking within system dynamics. Thus systems thinking (1) is quantitative oriented.
b) Systems thinking (2) is a more universal approach. Systems thinking (2) is qualitative oriented.

Ossimitz finally generates a definition of systems thinking – which did not exist explicitly at that time. The basic approach of the thesis is based on G. Ossimitz definition. A description to the basic approach of the thesis and the definition of system thinking you find in 2.2.2.

Important is to underline that the very pragmatic part G. Ossimitz emphasis on is only a fragment of system theory. Ossimitz’s main goal is putting theory to practical use.[35] His approach therefore is systems thinking. A further big part of system theory (especially in Europe) is covered by Niklas Luhmann[36] whose scientific field was sociology. Luhmann’s approach is, contrary to the very pragmatic methods mentioned in G. Ossimitz’s book, philosophically much more in depth and much more theoretical. A continuation of Luhmann’s theoretical basic approach is done by authors like H. Willke[37] and D. J. Krieger[38] (to name only a few). Especially for the goal of this thesis to be of practical use, the highly theoretical viewpoint of Luhmann is not suitable; therefore I don’t go deeper into this part of system theory and just use those sources to quote it in spots where it fits the thesis. T. Pfeffer’s[39] “Das “zirkuläre Fragen” als Forschungsmethode zur Luhmannschen Systemtheorie” is another book this thesis is based on. The interviews made within the thesis are related to the approach of T. Pfeffer. He shows in his work that there is a lack of methods within sociology that contributes to system theory. And he suggests an approach for sociology that he generates out of family therapy combined with the theories from Niklas Luhmann. This approach was taken up for the thesis. ([40], [M]) With his very fundamental and profound work of system dynamics, J. D. Sterman[41] provides further knowledge for applying system theory. It shows a possible practical approach, how to apply systems thinking as a method to influence the quality of a complex system. The book describes very accurately the situation of system dynamics. It gives further a lot of examples for the use of system theory in various fields of economy and society.

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Figure 2-1 Diagram out of G. Ossimitz’s Book “Entwicklung Systemischen Denkens”

2.2.2 Definition of Systems Thinking

The already quoted book of G. Ossimitz “Entwicklung Systemischen Denkens” gives a very accurate definition of systems thinking in science today. In his book the author is discussing many of the different current approaches to systems thinking in literature. And, in relation to that, sets up a definition of systems thinking. The basic approach for the thesis described in this chapter is based on Ossimitz’s definition of systems thinking.([42], [N])

G. Ossimitz four central dimensions of systems thinking are:

1. Networked Thinking: Thinking in feedback loo ps .
2. Dynamic Thinking: Thinking over time.
3. Thinking in Models .
4. System-Compatible Acting .

These 4 dimensions are applied in the thesis as follows.

- Networked Thinking: Thinking in feedback loo ps .

This means thinking in feedback loops. It is important to understand interconnected structures and to see not only the cause and the effect, but also the back-effect on the effect. An example is shown in Figure 2-2 Example out of P. Senge’s book The Fifth Discipline[43] below.

In looking at the non-systemic diagrams it occurs obvious that there is either

- the United States to blame and the Soviet Union just has to react to escape the threat of the U.S. Arms.
- the Soviet Union is to blame and the United States just has to react to escape the threat of the U.S.S.R. Arms.

A look at the system thinking view shows the scene as a vicious circle where no one is to blame and both of the parties are caught. A perpetuation of the politics originated in the non-systemic view could never lead to a solution.

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Figure 2-2 Example out of P. Senge’s book The Fifth Discipline

- Dynamic Thinking: Thinking over time.

In applying dynamic thinking the possible development of the observed system should be identified. Occurring significant delays will be involved in the model. Important here is to see processes of change rather than snapshots.[44] Path dependence[45] ([46], [O]) will be of a specific interest. The approach for path dependence is as follows. Path dependence arises in systems with locally unstable equilibria. In Figure 2-3 Locally Stable Equilibrium below you see a locally stable equilibrium. If you go through the causal loop diagram[47] you see the following. The system is governed by negative feedback: the greater the displacement of the ball from the equilibrium P*, the greater the force pushing it back toward the center and equilibrium. A ball placed anywhere in the bowl eventually comes to rest at the bottom; perturbations don’t affect the equilibrium reached.

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Figure 2-3 Locally Stable Equilibrium

If you look at the sketch of the bowl, in Figure 2-3, below the causal loop diagram you see that the lowest point of the bowl is an equilibrium – a marble placed there will remain there. The equilibrium is stable: pushing the marble of the equilibrium creates a force opposing the displacement. A marble dropped anywhere in the bowl will eventually come to rest at the bottom (though it may roll around a while first). The equilibrium is not path dependent: the marble comes to rest at the same spot no matter where it is dropped and no matter its initial velocity (as long as it stays within the bowl – the equilibrium is only locally stable).

The Figure 2-4 Locally Unstable Equilibrium below shows a locally unstable equilibrium. If you go through the causal loop diagram[48] you see the following. The system is governed by positive feedback: the greater the displacement of the ball, the steeper the hill and the greater the force pulling it away from the equilibrium at P*. The slightest disturbance causes the ball to fall off the peak. The initial perturbation determines the path taken by the ball and perhaps the ultimate destination – the system is path dependent.

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Figure 2-4 Locally Unstable Equilibrium

If you look at the sketch of the bowl (now inverted), in Figure 2-4, below the causal loop diagram you see that the top of the bowl is still an equilibrium – if the marble is balanced exactly at the top it will remain there. However, the equilibrium is now unstable. The slightest perturbation will cause the marble to move slightly downhill. As the slope increases, the downward force on the marble increases and it moves still further downhill, in a positive feedback. The system is path dependent because the direction taken by the ball depends on the initial perturbation: A small nudge to the left and the marble rolls to the left; an equally small shock to the right and it moves farther right. The path dependent nature of the ball’s motion near the equilibrium point means it can come to rest anywhere. And the particular spot it reaches depends on that small, initial disturbance. The inverted bowl shows another important feature of path-dependent systems: lock in. When the ball is balanced at the top of the bowl, all equilibrium positions are equally likely. You can influence where the marble comes to rest with the slightest effort: Blow gently to the left and the marble rolls to the other side. Once the ball has started to move down the slope a bit, however, it takes a great deal more energy to push it back to the top and over the other way. The farther the ball has moved and the faster it is going, the harder it is to alter its course.

At the dawn of automobile age it didn’t matter which side of the road people drove on. But as traffic density increased, the importance of a consistent standard grew. The more people drove on one side, the more likely it was that new drivers in adjacent regions would drive on the same side, thus increasing the attractiveness of the side still further, in a positive loop.[49]

For the thesis this means that in identifying the different variables of interest every variable will be scanned concerning path dependence and the possibility of lock in behavior. Contrary to the quantitative approach of path dependence and lock in presented in Sterman’s book, the thesis discusses it qualitative. It is verbally expressed whether or not path dependence is likely to appear in the designed model. CLDs that have a pattern of path dependence will be indicated.

- Thinking in Models

This point will be matched in further discussions of the finished thesis. It relates to the fact that the designed CLDs are models and have therefore the nature of drafts. The intention of the thesis is to show a promising possible approach – it is seen as a basis for further discussions and workings in the field of unemployment. The design is not to develop a specific quantitative model but to create a basic approach to address the problem of unemployment. The tools used to apply thinking in models are the causal loop diagram and the paper-computer. Both methods are described in 2.2.3. The important characteristics of the methods are

- easy to understand for everyone – therefore easy to communicate

- capable to handle highly complex relations

To apply a quantitative approach it would be necessary to split the observed system into smaller parts.

- System-Compatible Acting

This dimension does exist implicitly in the effort of this thesis. It is my intention to do this basic approach using my theoretical knowledge of systems thinking to learn how to apply systems thinking in practice. To say it with P. Senge’s Words: There is simply no more effective way to learn a language than through use, which is exactly what happens when a team starts to learn systems thinking.[50] Though I am not acting in a team my focus is to develop my theoretical knowledge about systems thinking further and to apply it in practice in writing this paper. [51]

2.2.3 The tools for Systems Thinking

There are different tools used within system theory to depict and explain system behavior. The two main categories are quantitative and qualitative tools. Within the thesis a qualitative tool called “C ausal L oop D iagramming” (CLD ming) and a semi quantitative tool called “P aper C omputer” (PC) is used. The design of the research is totally qualitative based. Several qualitative and quantitative software tools for modeling are available – see 6.3. For drawing simple C ausal L oop D iagrams (CLD s), without any intention for further mathematical modeling, the “Drawing” tools of software like WinWord® (Microsoft®) are sufficient enough. To apply the P aper C omputer (PC) every spread - sheet software is useful. For draft purposes it is enough to have a pencil and a sheet of paper (as the name of the method tells).

Below I explain, in general, the use of CLDs and of the PC that are applied in the thesis. It is necessary to know the basics of CLDming to understand the CLDs that are designed in Part II “REALIZATION AND RESULTS” of the thesis. The information about CLDming is taken out of John D. Sterman’s book “Business Dynamics, Systems Thinking and Modeling for a Complex World”, Part II, Tools for Systems Thinking, 5 Causal Loop Diagrams, page 135 to page 190. The information about the PC is mainly[52] taken from F. Vester’s and A. v. Heseler’s work “Sensitivity model”, page 273 to page 277. Causal Loop Diagrams applied within the Thesis

Causal loop diagrams (CLDs) are very common in system dynamics they are easy to understand and show the feedback structure of system dynamics concepts very accurately. There are several more diagramming tools used within system dynamics (stock and flow maps for example) the reason why CLDs are finally used for the thesis is that there should be a communication between persons out of very different professions and the structure of CLDs seemed therefore the best. With J. Sterman’s words: “…CLDs are excellent for

- Quickly capturing your hypotheses about the causes of dynamics;
- Eliciting and capturing the mental models of individuals or teams;
- Communicating the important feedbacks you believe are responsible for a problem.


A causal diagram consists of variables connected by arrows denoting the causal influences among the variables. The important feedback loops are also identified in the diagram. Figure 2-5 Simple Example of a Causal Loop Diagram with a Key to the Notation shows an example and key to the notation. Variables are related by causal links (See Key), shown by arrows. In the example, the customer base is determined by both the sales from word of mouth and the customer loss rate. Each causal link is assigned a polarity, either positive (+) or negative (-) to indicate how the dependent variable changes when the independent variable changes. The important loops are highlighted by a loop identifier which shows whether the loop is a positive (reinforcing) or negative (balancing) feedback. Note that the loop identifier circulates in the same direction as the loop to which it corresponds. In the example, the positive feedback relating sales from word of mouth and customer base is clockwise and so is its loop identifier; the negative customer loss rate loop is counterclockwise along with its identifier. A positive link means that if the cause increases, the effect increases above what it would otherwise have been, and if the cause decreases, the effect decreases below what it would otherwise have been.

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Figure 2-5 Simple Example of a Causal Loop Diagram with a Key to the Notation

A negative link means that if the cause increases, the effect decreases below what it would otherwise have been, and if the cause decreases, the effect increases above what it would otherwise have been.[54] To avoid misunderstandings the mathematical definitions of link polarity are shown in Table 2-1 below.

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Table 2-1 Link Polarity: Definitions

To increase the information of a CLD it is also important to mark delays. Delays are critical in creating dynamics. Delays give systems inertia, can create oscillations, and are often responsible for trade-offs between the short- and long-run effects of policies. Causal loop diagrams within the thesis including delays are marked like it is shown in Figure 2-6.

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Figure 2-6 Example of Marking a Delay within a Link

The example in Figure 2-6 shows that there is a delay between an actual quality shortfall and the time quality improvement programs are built. Whenever there is this indicator of a delay on an arrow it indicates there is a significant delay in the systems behavior. In looking at the delays of the system one can analyze the chronologically dynamic. Because of the highly conceptual nature of the thesis there are only variables with a delay or variables without a delay. The resolution is therefore very coarsely. The time range where a delay starts will be marked in the explanation of the several CLDs. Determining the Loop Polarity

The way to determine the polarity of a loop is to trace the effect of a small change in one of the variables as it propagates around the loop. If the feedback effect reinforces the original change, it is a positive (reinforcing) loop; if it opposes the original change, it is a negative (balancing) loop. One can start with any variable in the loop; the result must be the same. In the example in Figure 2-7 we assume sales from word of mouth increase. Because the link from sales from word of mouth to the customer base is positive, the customer base increases. Because the link from the customer base back to the sales from word of mouth is positive, the signal propagates around the loop to increase sales from word of mouth still further. The feedback effect reinforces the original change, so the loop is positive (reinforcing). Turning to the other loop, assume a small increase in the customer loss rate. If customer losses increase, the customer base falls. With a lower customer base, there are fewer customers who can drop out. The feedback effect opposes the original change, so the loop is negative (balancing).

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Figure 2-7 How to Trace an Effect of a Change Naming and Numbering of CLDs within the Thesis

To prevent from misunderstandings we define the following rules within the thesis.

All the variable names used are nouns or noun phrases. The naming is consistent through out all CLDs. Every important feedback loop gets its own identifier number and name. Numbering of the loops is R1, R2, …, Rn for reinforcing (positive) loops and B1, B2, …, Bn for balancing (negative) loops. For information feedback there are always used curved lines. The more important loops follow circular or oval paths. The capability of CLDs

Link polarities describe the structure of the system. They do not describe the behavior of the variables. That is, they describe what would happen IF there were a change. They do not describe what actually happens. The causal diagram doesn’t tell you what will happen. Rather, it tells you what would happen if the variable were to change. For the feedback loop shown in Figure 2-7 the diagram states: If sales of word of mouth rise, then customer base rises (above what it would have been before), but if sales of word of mouth fall, then customer base will fall (below what it would have been before). When assessing the polarity of individual links, assume all other variables are constant (the famous assumption of ceteris paribus). When assessing the actual behavior of a system, all variables interact simultaneously (all else is not equal) and computer simulation is usually needed to trace out the behavior of the system and determine which loops are dominant. Causal loop diagrams do not distinguish between stocks and flows – the accumulations of resources in a system and the rates of change that alter those resources. Causal diagrams can never be comprehensive (they shouldn’t because modeling is the art of simplification). They are also never final, but always provisional. The maps evolve as the understanding improves and as the purpose of the modeling effort evolves. It is important for the thesis that the focus lies on a qualitative description of the system. The main question is whether or not the existing efforts concerning unemployment are, compared to a systems thinking view, consistent. The thesis should be a basis for a communication about, and for developing further methods to prevent unemployment. For achieving this goal CLDs are a very suitable tool. [55] The Paper Computer (PC) applied within the Thesis

Above it is shown how to visualize the structure of the variables of a complex system by designing CLDs. To get a rough picture about the chronologic dynamic of the variables the delays were adopted. To broaden the insight in the behavior of a complex system it is also important to know about the strength’s of the mutual effects of the influencing factors. A very simple but though very effective tool to evaluate this is the paper computer. The PC combined with an ascertainment of the steering possibilities (see completes the set of tools used in the thesis. Description of the Paper Computer (PC)

The PC is a semi quantitative method to design a qualitative overview of a complex system. The strength’s of the mutual effects of the influencing factors is assessed in terms of the following grading:

0 = no effect
1 = slight effect
2 = moderate effect
3 = great effect

The elements of every pattern of interactions (those which are most active, passive, and critical and have the greatest buffering effect) are calculated by integrating these evaluations into the matrix by summation and by building the percentage of the sum of the columns and the rows related to the maximum value that appears.

An example of such a matrix is shown in Table 2-2 below.

Every variable gets an identification letter in the matrix. All variables are listed downwards in the first column (very left side of the matrix) and from the left to the right in the first row of the matrix. Since a variable does not influence itself the diagonal does not show any numbers but only black dots.

The matrix is the filled up with the related numbers in asking the question: Effects of Variable 1 on Variable 2? Every variable is scanned like this and the numbers are finally summed up. In the matrix below we get eight numbers for the rows and eight numbers for the columns.[56]

The next step is now to look for the largest number in the sum of the row and in the sum of the column. In the example below the largest number amongst the sums of the rows is 15. Therefore 15 is 100%. The largest number amongst the sums of the columns is 14 (14 shows up twice but this does not matter). Therefore 14 is 100%.Now the related numbers are calculated to those one hundred percent values.

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Table 2-2 Example of a Paper Computer Matrix

The percentage numbers are finally put into a diagram where the ordinate represents the sums of the columns in % of the max value of the columns. And the abscissa represents the sums of the rows in % of the max value of the rows. So every point in the diagram represents a variable of the system. As you see in Figure 2-8 every point is marked with the identification letter of the related variable. The diagram is divided into four sections which represent the type of the variables that are in the section.

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Figure 2-8 Example of a Diagram Developed from the Paper Computer Matrix

The results of the evaluation of the indices of influence in our example are as follows:

- Active variable:
C and H
- Passive variable:
B and A
- Critical variable:
D, E and G
- Buffer or inert variable:

There is no variable in this field.

It is important to look at the diagram with the knowledge in mind that the borderlines between the four sections are not strictly. The diagram gives an idea of the behavior of the variables. It is therefore seen as qualitative tool. One should never forget this fact in reading the diagram.


[1] See 5.

[2] See 5.

[3] Frederic Vester, Neuland des Denkens, Vom technokratischen zum kybernetischen Zeitalter, ISBN 3-421-0273-X, page 454.

[4] See 5 and the term aporia and how it connects to the issue above is explained in detail in 2.3.

[5] See 3.3.

[6] See and

[7] See 2.3.3.

[8] See Goethes Sämtliche Werke, Jubiläums-Ausgabe, 40 vols., volume 2, page 249.

[9] Pierre-Simon Laplace (1749-1827).

[10] See 5.

[11] "Determinism.",, 13 Apr, 2003 <.

[12], 14. Apr., 2003

[13] See 5.

[14] The Columbia Encyclopedia, Sixth Edition Copyright© 2000, Columbia University Press. Licensed from Lernout & Hauspie Speech Products N.V. All rights reserved. Page 15832.

[15] Kurt Gödel, Mathematician (1906-1978).

[16], 14th April 2003.

[17] Wikipedia the free enzyklopedia,, 14th April 2002

[18] Werner Heisenberg, founder of quantum mechanics, (1901 - 1976).

[19], 14th April 2003

[20] Werner Heisenberg, Das Naturbild der heutigen Physik, Veröffentlicht im Dezember 1955, Copyright 1955 by Rowohlt Taschenbuch Verlag GmbH, Hamburg, page 132.

[21] Helmut Willke, Systemtheorie I: Grundlagen, ISBN 3-8282-0137-7, page 197.

[22] See 5.

[23] David J. Krieger, Einführung in die allgemeine Systemtheorie, ISBN 3-8252-1904-6, page 170 and 171.

[24] Helmut Willke, Systemtheorie I: Grundlagen, ISBN 3-8282-0137-7,page 198.

[25] Aristotle, Metaphysics Λ. 10 .1075a11.

[26] Karl von Clausewitz, On War, page 3

[27] G. Ossimitz, Entwicklung Systemischen Denkens

[28] G. Ossimitz, Entwicklung Systemischen Denkens, page 40.

[29] Source:, 20th April 2003

[30] G. Ossimitz, Entwicklung Systemischen Denkens, page 41.

[31] For a detailed overview see G. Ossimitz, Entwicklung Systemischen Denkens, page 40/41.

[32] George P. Richardson, 1991, Feedback Thought in Social Science and Systems Theory, Philadelphia: University of Pennsylvania Press.

[33] G. Ossimitz, Entwicklung Systemischen Denkens, page 36.

[34] For related literature to the left area of the diagram see 6.1.

[35] G. Ossimitz, Entwicklung Systemischen Denkens, page 60.

[36] See 6.1.

[37] See 6.1.

[38] See 6.1.

[39] See 6.1.

[40] Thomas Pfeffer, Das “zirkuläre Fragen” als Forschungsmethode zur Luhmannschen Systemtheorie, page 3.

[41] John D. Sterman, Business Dynamics, Systems Thinking and Modeling for a Complex World

[42] G. Ossimitz, Entwicklung Systemischen Denkens, page 52 to page 62.

[43] Peter M. Senge, The Fifth Discipline, ISBN 0-385-26095-4, page 70 and 73.

[44] See also Peter M. Senge, The Fifth Discipline, ISBN 0-385-26095-4, page 73.

[45] For some critical notes to the viewpoint of path dependence see, 17th June 2003.

[46] See, 17th June 2003, Technological Trajectories and Path Dependence, paper by Luis Araujo and Debbie Harrison.

[47] See

[48] See

[49] The explanation about path dependence is taken from John D. Sterman, Business Dynamics, Systems Thinking and Modeling for a Complex World p 351 – 354.

[50] Peter M. Senge, The Fifth Discipline, ISBN 0-385-26095-4, page 269.

[51] For many more tools look at SD Mega Link List ( of G. Ossimitz.

[52] See also, 14th of May 2003.

[53] John D. Sterman, Business Dynamics, Systems Thinking and Modeling for a Complex World, page 137

[54] John D. Sterman, Business Dynamics, Systems Thinking and Modeling for a Complex World, page 138 to 143

[55] The content of this chapter is taken out of F. Vester’s and A. v. Heseler’s work “Sensitivity model”, page 273 to page 277 and from, 15th May 2003.

[56] It is always a quadratic matrix because every variable is connected to every variable which results mathematically always the square of the number of variables.


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Titel: Systems Thinking View on the Situation of Unemployment in the USA