Modelling Innovation for Creative Control by Bayesian Syllogism
SYSTEMS + MATHEMATICS, ANTHROPOLOGIES, HISTORY, COGNITIVE SCIENCES
Hellmut K. Löckenhoff,
Research Consulting D-71522 Backnang BRD
email: LoeckenhoffHellK@t-online.de
Abstract
Successful innovation, irrespective if entrepreneurial
or in worldwide societal transition, requires the
management of different resources. Its organisation
follows formal rules and anthropological ones, that is
individual/group/team behaviour, institutional choice
and cognitive/ emotional regularities as e.g.
motivation/mobilisation. Systemic considerations
concerning the system ‘as it exists’ need be
complemented by lessons learned from history.
Innovation begins with the stimulation to create new
ideas. It ends but with successful ‘marketing’ within
a continuous historical helix of performance and
management evolution. Innovation is approached
here as an inter- and transdisciplinary challenge. To
benefit from the broader scientific domain it is
approached from the comprising view of societal
transition and rejuvenation. It touches aspects of
meta-methodology and theory of science. The
Bayesian Syllogism [Nalimov 1985] provides an
heuristic and highly flexible concept.
the qualities for self-organized learning processes on the
societal level. Innovative learning will be termed Guided
Evolutional Control Learning (GECL) [Fig.1,5]. When
constructing a basic GECL model for societal systems
control, a particular version of the Bayesian Theorem is
employed. The seminal ideas were proposed by the
renowned Russian polymath V.V. Nalimov [1985] as
‘Bayesian Syllogism’. Relying on experiences in field
research (Loeckenhoff 1997) we suggest variations of a
simple graphic model. Its potentials and transfer into CA
simulation programs were discussed elsewhere.
PHASE SPACE
History
Indigenous
Cases
For all possible courses
under conditions given
Innovation: option/
action space
Past
TIME
Futures
Policy
Support
ti
PHASE SPACE
Key words: Innovation Control, Societal Transition,
Modelling, Bayesian Syllogism.
Prologue
The topic may be formalized as modelling of complex
societal phenomena for purposefully creative control. It
will be attempted from the systemic approach,
comprehending (meta)methodology and theory of
science. It must addresses complexity and how to cope
with it. Disciplinary aspects will be but commented.
A comprehensive systemic approach is attempted. It
comes critical about sources of data, models and their
evaluation as to situation and purpose; critical also
against hidden assumptions in design and operation.
Likewise, it appears pragmatic. It appeals to scientific
common sense avoiding the tides of specific research
paradigms, i.e. to control complex innovative/ transitional
processes. ‘Pragmatic’ points to whether and in which
respect the actual systemic approaches support learning,
in particular societal learning.
The necessarily transdisciplinary approach extends the
discipline or project boundaries. It undertakes to question
11609754612.02.1620:47
Probability fields , structured,
by past and possible futures
Fig 1 Phase space: Bayesian syllogism for Policy Support
To assess the model’s powers of explication,
explanation or even (weak) causal connections, the basic
assumptions need be scrutinised both of the model and its
interpretation. Which networking seems appropriate?
Which models of perception, consciousness, cognition,
understanding ourselves in our world, will be sufficiently
‘realistic’ to the purpose? A short recall of the change of
scientific paradigms focuses on (formal) mathematics,
anthropology, cognitive sciences, history and their cooperation in conceptualising societal transition control.
1. Understanding the Actual Historical
Systems State for Control
Recalling the trivial: Modelling serves a purpose; it refers
to a system owning the qualities at an historically given
state. Controlling is aimed to alter actual states following
a given aim, objective, mission, strategy; taking the actual
state as the point of departure. Any modes, methods and
proceedings and planning /controlling must be shaped by
the particular qualities denoting the status quo of the
system. The basic preconditions of any (also partly)
activity of societal controlling include a thorough
understanding of the societies’ historical state. It inheres
in particular the qualities influencing the propensities of
the system to react to controlling impacts. Success or
failure of a policy decided upon will depend on the
degree the ‘manifest history’ has been identified.
Pandora’s box open, in the systemic context some
crucial questions may be selected.
First, a system may be seen at a given point of time
and as a temporary result of an evolutionary process,
determined by the past and, (as e.g. Anticipatory
Computing has shown [Rosen 1985; Dubois 2002]) by its
possible future(s) as well. How do past and future
culminate in a in situ propensity of the societal system to
(re-)act? For example only: a formal technique to find
out, among other features, the ’eigendynamics’ of a
system is explored by simulation of societal phenomena.
Historical analysis, case based or systemic, will
complement. Qualitative analysis, e.g. Qualitative
Opinion Research (GABEK, [Zelger,2000]) covers value
systems, evaluation attitudes and resulting behavioural
inclinations.
Second: the system’s actual state does not necessarily
comprehend all crucial indicators. Which networking
qualities constitute a social system, a society? Are there
‘universals’, and can taxonomies of societal systems, e.g.
relating to cultures, historical states, be hypothesized?
Naturally the other part, not to be neglected, is made of
the ‘specifics’, the ‘situationals’ any action has to start
from.
Third: An accord of the complexity of living systems
is needed. Included are the complexity of societal systems
as such, and controlling as a mode to cope with
complexity. Three mutually complementing approaches
to complexity (for a fourth one see e.g. St. Wolfram
[2001]) become obvious: the mathematical one, the
approach from anthropologies/humanities; cognitive
sciences and the evolutional / historical.
Below, when critically questioned, the manifold
attempt will shed light to hidden assumptions held by
man understanding himself in his world and trying to
cope with it. This will stimulate investigations into the
cognitive/emotional powers to understand and control
evolutionary learning within and by society. Not least,
some pains of the design and transfer into practice of a
systemic policy support will reveal themselves.
2. Faces of Society, Societal Complexity and
Societal Innovation
Society emerging from a diversifying evolution and
history (e.g. Diamond [1997]), is shaped by different
cultures. Globalisation has to deal with all evolutionary
states: primeval tribe, feudalism, religious empires,
technically based states (Western style) in between. So,
what constitutes a society? What holds a society together,
which tensions, imbalances drive it apart (HEITMEYER)
[Fig. 2, 6]? Which enable it to creative rejuvenation?
From the intention to focus on aspects, not to reduce
11609754612.02.1620:47
(Procrustean reduction), three main factors may be
discerned. (Self ) observation enhances and lets develop
identity likewise of the individual and the acknowledged
member of community. Communication, continuous
networking of opinions, values, material information
assures the imbedding in the societal networks,
stimulating mental/material sharing. Last, a shared
mission, a historically rooted understanding of what the
‘nation’ is distinguished by constitutes an emotional and
creative glue. The three factors establish and stabilize
identity on the individual, the group, the institutional and
the societal level. They assert the role(s), the societal
imbedding and the trust into a future worth to innovate
for .
Past /actual
systems: Preconditions,
Inclinations
PHASE SPACE
Universals, Principles.
Generic Courses...
Self-Organisation....
Innovation: probability
structures
TIME
Actual historic
Structures,
Probabilities
ti
PHASE SPACE
Systems Positioning
Indigenous Properties
Eigendynamics
Fig 2 General Principles, Universals
The selected aspects serve as examples how societal
complexity ought be approached from equally complex
concepts. Consequences, f. ex. of role inconsistency,
insecurity, reduced or restricted communication and the
loss of belief in a mission are underlined. As e.g. anomie
research has shown, dynamic change may dissolve
and/or imbalance the network. The known obstacles to
evolve adapting innovatively to new life conditions, to
reform obsolete structures, may be approached from here.
The Knowledge won from such aspectual investigation
rises new questions, but gains interfaces and vistas for
innovation sensitive modelling. Insight into the roots of
creativity will modify the basic issues of innovation
research.
The concepts are designed in the systemic mode; they
start from an holistic view. The systemic approach opens
to complementing approaches. It qualifies for
dynamically changing systems, owing constitutional,
dynamic and actual complexity [Mulej et al 2003]. It
favours, a
transdisciplinary multilevel–approach
appropriate for policy support.
3.Modelling Complexity: Innovation Phase
Spaces in the Bayesian Syllogism
Modelling complexity turns out a multi-approach
activity. Societal models proposed from different
scientific positions can be arbitrarily classified

Mathematics and Formal Systems/Cybernetics:
Math;
General
Systems,
AI,
AL…..
[Starkermann 2003]

Formal–statistical: Social Physics, (formal)
Synergetics,
e.g.
Econometrics,
Demography……

Biological-ecological–evolutional:
Sociology, Behavioural Sciences,
Ethology, Ethnology, Geo-History;

Positioning, roles: Gender, Family, Hierarchy
accepted,
Religion,
Ethnology
…..
(‘Structuralistic approaches’);

Anthropological:
(Hyphen-)
Sociologies,
Psychologies, Psycho-History, Philosophy….;
Cognitive Sciences; Consciousness;

Historical: Rise and Fall of Societies

Systems and systemic: in particular sociocybernetics….; cultural systems…;

Political;
Rawls…

Mathematically founded (chaos) systems
models: distributed intelligence;
swarm
intelligence (using various approaches as e.g.
cellular automata), anticipatory systems …
constitution: Platon,
BioHuman
Montequieu,
Another taxonomical approach would discern between
more universal or more situational-historical aspects etc.
(For Modelling and simulation of societal phenomena see
CONTE; HEGSELMANN [1997])
Compiling modelling approaches leads to an complex set
of multi-facetted models intrinsically networked.
Valuable as frames of reference and for heuristic
exploration, they offer per se small pragmatic use. Simple
modular models as at the base of simulation programs as
e.g. VENSIM, presuppose also qualitative models they
can be applied to.
A practicable model should display but few crucial
variables/non-variables in a generic fashion [Fig.1].
Basically, the Bayesian approach is two-dimensional.
One dimension is occupied by the arrow of (historical)
time, past, present, future. The other carries probability,
the dimension of possibilities, of possible future events.
The with distance widening field of probabilities is
supposed as structured – see universals and principles.
Within the netted multidimensional structures a path of
the most probable future(s) may be postulated, assessing
the (joined) impact of future-shaping factors. It is
presupposed that past structures will at a certain level be
analogue to the futures ones. They also include typical
courses of evolution/ development as short, middle and
long ‘waves’ of development : E- (evolution) and S- (
short range) curves. Examples are given by economic
short, middle and long-range cycles (Yuglar, 7-year,
Kondratieff). Possible revolutions, phase transitions or
catastrophes must be accounted for. For two-dimensional
graphic display of the actual multidimensional
complexity the concept of phase space, proposed be the
French mathematician H. POINCARÉ, is proposed here.
Similar applications are found throughout sciences. The
other concept used is the Bayesian Theorem, proposed by
the English statistician BAYES (19th century). It is
11609754612.02.1620:47
employed in a version adapted by the author as Bayesian
Syllogism formed by the Russian polymath V.V. Nalimov
[1985]. In essence it suggests that the future development
is influenced by the past, a quality to be used to
hypothesise defined structured probability fields as
described above.
The first advantage of the phase space concept permits
to account for any regularity, which might influence the
path within the probability fields. It qualifies as generic
model, providing the frame for comprehending range of
factors, structures, courses etc; for the purpose in
question ‘sufficiently complete’ [Mulej, Kajzer, 1998]
and to be revealed in the future.
A second advantage lies in its appropriateness to
incorporate learning processes, in particular of societal
innovative learning. It closely ties to the generic quality
of the model, stimulating materially /formally creative
imagination. Though the model is open to all concepts of
learning, practice has shown favour to the learning model
of the (radical) constructivism. Proposed by Maturana/
Varela, as ‘self-organizing’ learning it leads to an actual
developmental path following the ‘natural drift’ [Fig.3].
Natural drift describes a learning process of active
adaptation between the learner and the environment. Such
a procedure furthers optimum co-evolutional innovation.
Openness
Bifurcat
Chaotic
Courses
Events
Limiting curves: general principles
etc.
Natural Drift
Actual course
ti
TIME
Uniqueness
of actual
constellation
Innovation
Reform.....
Target
function
Fig. 3 Drift Learning; Innovative Intervention
The pragmatic base for the gradually emerging
conceptualisation of the model presented above is rooted
by intensive practice of strategy planning in industry and
the societal domain. The seminal models were, by
societal field research, extended to social and societal
planning [Atteslander et al. ed. 1999]. The author’s
practice of strategic controlling fostered a model
conceptualising controlling as an continuous learning
process adapting active policy to the phase space for
natural drift.
4.Assumptions Behind: Principles,
Universals and Uniqueness
As often, the concept already works effectively while the
assumptions behind are but loosely confirmed.
Corroboration seems necessary when the model is
extended in analogue to proximate domains of
application. Both a (meta-)methodical clearance of the
general model and the general assumptions behind, and of
the specific application seems paramount. Else failures in
assessing the power of explanation and of the validity are
pre-programmed.
The difficulty lies again with the dynamic complexity
of innovation phenomena. With the rise of
systems/cybernetics, the systemic approach, and with the
advanced application of computer methodical problems
are pressing. They refer to integration of different
scientific paradigms and differing disciplinary
methodologies. To conceptualise and to model complex
societal phenomena at least three different methodologies
need be integrated. Which are, for purposes of
comparison, the kernels of the disciplinary modes of
scientific working? Which are the differences seen from
a unifying principle beyond? Which are the interfaces, to
be transmuted into bridging methods?
In the actual case formal sciences (paradigmatically
systems and math), social, historical and cognitive
sciences need contribute. How do the different
approaches co-act, in general and in the actual case
[Fig.2]? The question aggravates when recalling that
there is no ‘mathematic’ as a closed bloc, but rather
loosely connected ‘mathematics’. Non-linear math arises
specific questions. In contrast, social sciences began as
historical case studies, mostly qualitative in their essence.
Fitting aspects increasingly are formalized by mathematic
formulas, leading e.g. to social physics, socio-systems
and socio-cybernetics. [For development after WW2 see
G. de Zeeuw [2003]; recent examples in ‘Cultural
Systems’ in [Cybernetics and Systems ed. Trappl 2002];
also related concepts as Synergy [Corning 2003],
Synergetics [Haken 1978].
Related aspects of
perceptional and cognitive/ emotional bases of scientific
methodology are discussed. The impetus emerges in part
from hybrid; ‘hyphen’ and transdisciplinary approaches.
In part is stimulated by cognitive sciences. They include
the cognitive branch of systems sciences, cybernetics II
to ..?, but also systems biology, artificial intelligence and
behavioural aspects of social systems. This activity
within the ‘cognitive domain’ seem to form a set of new
paradigms; from its physiological (brain), its
psychological (mind) and its mathematical (AI, AL)
aspect. [Lazlo 1996; Capra 1999 2002]. The systemic
sciences/ philosophy inquire, how wo-man may be
understood from her/his interfaces to inner and outer
environments of the as such perceived ‘reality’.
Systemics claim a holistic approach. What does
holisticity mean, methodically? Arguing methods as
above: universals have been mentioned afore. Field
research and experiments confirmed their existence.
Brilliant research [Corning 2003] elucidated the laws
governing ‘becoming’: synergy and competition co-acting
in the comprising context of cosmic co-evolution. What
may be the reasoning behind the existence of universals?
For example fractal structures are detected in virtually
any domain from physics up to psychology and
philosophical texts. These and other self-organizing
recursive structures permit to assume a set of general
principles the world is emerging, in our eyes, upon. Can
such an assumption be accepted as a hypothesis – or is it
scarcely more but a mere speculation? To be explored
need not so much versions of an ‘unified theory’, but of
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reasonably related systems of principles, proven as
‘universal’ in a defined mode. The ‘fuzziness’ inherent to
all living systems needs be accepted. Positively, fuzziness
provides ‘free action and option space’ for actual
arrangements according to situational preconditions. It
seems paramount to fill in the phase space of the
Bayesian syllogism model with actual data from actual
cases as to evaluate for singularities and similarities.
5. The Powers to Support Evolutionary
Guided Control Learning for Innovation
The crucial processes constituting living systems are
those of learning. The term comprehends a multitude of
procedures. The Bayesian Syllogism is combined with a
simple learning model (GECL, see Prologue), as it
developed in industrial strategy practice to stimulate
innovation in planning and control [Fig 4.,5].
History
Cases
Guidance: Targeted by Innovation
Laws of Evolution, Development
Control: Systematic Planning, Direct
Innovative CoEvolution
Result GECL curve
Attractor
Past
ti
Innovative Learning: continuous
intentional, experience related
Fig 4 Guided Innovation Learning curve
Its subsequent steps denote four main phases. They
begin, first, with ‘orientation’, with analysis and
prognosis. They set up a tentative probability structure of
the phase space for the future course of events (see
Anticipatory Systems: systems containing a model of
themselves). Second, the option and action space within
the phase space gives opportunity to plot a desired course
and decide on a target function.. To realise the target
function the third step ‘planning’ inquires the
preconditions necessary and sets up a system/course of
transfer measures, the ‘plan’. The plan, fourth, is
implemented by actual actions realizing the objectives.
Results achieved, are compared with the objectives set.
The difference, the ‘delta’, is stated and explored as to its
reasons and causes. Which presuppositions, which
estimates have proven wrong, and why so? Which would
have been the ‘right’ assumptions? The causes of
differences found out provide a base how to react to
unsatisfactory results: strengthen, abandon or redesign the
efforts? Abandon non-realistic targets? The decision
made on the new premises is then implemented by order
and intervention, by actual control. Hence the notation of
control learning. The phase space model proves an
essential tool to master the complex learning procedures
systematically.
Note: Control learning can be depicted in the shape of
the well known helical proceeding along an (emerging)
developmental curve. The curve denotes evolution, hence
evolutionary control learning. The course follows
conscious
orientation,
planning,
decision
and
implantations: hence ‘guided’[Fig 4]. In the long run
development curves approximate the ‘Natural drift’.
Evolutionary Guided Control Learning [Fig.5] is
hypothesised as a model of creative, self-organized
learning in (living) societal systems. Ubiquitous in all life
processes, it constitutes a densely meshed network of
learning circles/helices. It governs the control of
procedures both of institutional, big and small group,
team and individual actions. It applies to strategy and to
operation; linking the levels of the processes of life.
Intricate, complex. continuous ‘learning’ controls all
levels, phases and steps.
The Bayesian Syllogism appears a comprising yet
transparent, easy to handle system of Guided Evolutional
Control Learning. It offers transparent, retraceable modes
to structure probability fields in the space face. By power
of the helical, networked learning dynamics (including
learning to learn better) actual, individual probability
fields can be anticipated and designed. Following the law
of ‘requisite holism’ [Mulej et al.2003] one may
recursively plot and implement the path of ‘natural drift’
[Maturana/Varela, 1987]. Not least phase space models
suggest a transparent mode to define and attempt
sustainability by planning. Sustainable development, to
recall, needs be founded on continuous innovation
[Ecomivic, Mulej, Mayur 2002].
6. Pains to Transfer Innovative Policy
Support into Practice
The worldwide, ubiquitous problems mankind faces arise
from vastly distinct actual causes, closely interdependent
and densely meshed. Both globally and locally science
should attempt to support policy decisions accounting for
the complexity of the situation. For orientation and
planning the actual situation needs be defined and
positioned in phase space; thus supporting decisions how
to use the given option/ action space for policy [Fig.3]. In
most cases such positioning will reveal the ad hoc state
located at the cross point of several developmental trends
each in a different phase. The classical example is given
by the overlapping economic cycles and their
interrelation with any other societal developmental
curves.
To serve the concrete issues, a system of models is
needed referring to the systems in question, specified
within an agreed typology. So are developmental curves,
f.ex. derived from evolution curves. So is knowledge of
the laws of self-organisation, from fractals to catastrophe
theory. Factual evidence complementing formal models
will come from historical cases. Even laboratory
experiments can be conducted [The Economist 2002;
Gersemann 2003]. From historical examples in particular
also systemic interdependencies may be studied. Methods
like
Integrated
Systems
Method
(ISM;
[SCHWANINGER,1997]) will specify systemic effects
connected with the quality of procedures followed in
project implementation. To understand possible futures
the past has to be understood from the inherent future.
11609754612.02.1620:47
Orientation
Policy
Decision
Actual Controlling
Implemetation
Delta Plan
Societal
STRATEGY
Vison, Target
Transfer into Action
Structure,
Organisation
Planning
GECL Societal Learning
Intervention
Fig 5 Innovation Driven Learning Cycle; Co-Adaptation
Centred at the actual case, the holistic/ systemic
approach applies to validation of models and of methods.
It must be approached from the arrow of time, understood
as a historical process of learning and improvement.
Methodical validation builds the material prerequisite for
evaluation. Just to point to: the Bayesian phase space
models prove a tool to clarify the prerequisites of
evaluation, the rules of the evaluation process. An actual
research case concerning Slovenia joining EU provides
an example. [Mulej et al. 2003].
Systemic evaluation touches the appropriate use of
analogies, valid ‘so far’ as the shared qualities will carry.
Analogies are valid but within a given context from a
specific point of view. The aforementioned interfaces for
an integrated co-employ between formal, qualitative and
cognitive sciences need, in each actual case, be re-stated
anew. Growing experience with qualitative research will
contribute. For the cognitive aspect, for values and value
hierarchies, for resulting behavioural propensities see in
particular qualitative research methods as e.g. GABEK
[Zelger 2000].
Environments Space, Time, Positioning
Energy PerforDevelopmt
Societal
mance Bilance
Phase
Universals
Life sustaining Innovation
Adaptation; Reproduction
Identity
Particulars
Constitution
Organisat. Structure
Communcation
Togetherness, Bind
Fig. 6 Input-Output Model of Innovative Society
The employ of the Bayesian phase space model has
been addressed as an heuristic tool [Fig6.]. It supplies a
useful frame of reference to understand the uniqueness of
the actual case. How to assess its value as a prognostic
tool? The normative quality of models may pose a
problem. Normative models act, in the positive,
stabilizing; in the negative they act ossifying and adverse
to self-organizing development. Religions and ideologies
provide historical and actual examples. As a consequence
inability to evolve may arise, leading to decline, anomie,
terrorism etc. Unbiased exploration in the social and
societal domain is suppressed for the precision of what
should and must happen according to ‘divine plans’ or
‘historical laws’. Normative models would contradict the
rules of fuzziness, uncertainty, of chaotic concepts. More
seriously, they would hamper innovative learning ‘along
the natural drift’.
Epilogue
Unbiased, creative learning presents the only mode to
survive, to procreate and to evolve. That is true
biologically, as the declining birth rates designate. It is
valid culturally, as we learn from the revival of religious
zeal, from migration, from the pains of multi-culturality.
Economically Central Europe seems to be loosing
essential rejuvenation bases by inadequate learning, by
non sufficient innovation rates. The economic and
constitutional decline is but a result of by quality and
amount inadequate creative learning. Innovation is
hampered, impeded, stifled by bureaucracy, normative
ideologies as e.g. social welfare systems, and a general
ossification of societal institutions including those of
democracy. Corruption, in the widest sense of the term,
of the preconditions of creative learning impedes future
development. This is obvious on the government level as
well as in societal institutions and industrial companies.
How far is deterioration an unavoidable fate? Which
option and action space is open to regain freedom for
learning, chances for choice and development? First more
consciously awareness is needed of imminent the decline.
The Bayesian and the GEC learning models are intended
to provide a transparent instrument appropriate not only
for orientation, but also for setting sustainable targets.
Thus, second, GECL should be used to navigate through
future spaces; to decide what should and could be done,
and how accomplish it.
Mathematics meet anthropologies, history and
cognitive sciences. The systemic, differentiating interplay
of the approaches will eventually help to restore obsolete
models of our world and of which part man has been
assigned to. The world has changed fundamentally.
Accordingly our perceptive models, our issues, our
opinions on responsibility and eigenverantwortung need
change. What can be gained and consumed has to be reearned continuously by innovation and, in case, has to be
defended. That refers also to fundamental cultural values,
to culture, life style, to beliefs and convictions. It is high
time to recognise and to respond actively. The Bayesian
space time model will, as a chance of elucidation,
contribute. There is no other choice: Innovate, or decay
and disappear. The real danger for life and culture is the
future not met by innovative learning and control
Selected References
[Atteslander 1999] P.Atteslander; et al. ed. : Comparative
Anomie Research. Ashgate 1999
[Buber; Zelger, J.; ed. 2000] R. Buber J. Zelger,. ed.:
GABEK II. Zur Qual. Forschung. Innsbruck 2000
[Capra 2002], F .Capra. Verborgene Zusammenhänge.
Scherz 2002
11609754612.02.1620:47
[Economist 2002] Try it and see (no author). in: The
Economist March 2nd 2002, pp 72,73
[Conte R. et al ed. 1997;]..: Simulating Social
Phenomena. Springer 1997
[Corning, 2003]. P. Corning. Natures Magic, Synergy in
Evolution…Cambridge Univ.Press 2003.
[R. Trappl 2002] R. Trappl ed. Cultural Systems in
Cybernetics and Systems, p. 367 –432; Vienna 2002
[Diamond, 1997] Jared Diamond. Guns, Germs and Steel.
The Fates of Human Societies. Norton 1997
[Dubois, 2003] Dubois D.M. New Developments in
Computing Anticipatory Systems; p.3 Proceedings
Casys 03, Chaos Liége 2003
[Ecomovic et al.] Eximovic T Mulej M Mayur R Systems
thinking and Climate Change Korte 2002
[de Zeeuw 2003], G de Zeeuw.; Discovering social
Knowledge, Cybernetics and Human Knowing 10,No
3-4 2003
[Gersemann] 2003,Olaf: Wirtschaft im Windkanal.
Wirtschaftwoche No 51, p 84 ff 2003
[Haken , 1978] H.Haken. Synergetics. Springer 1978
[Heitmeyer 1997], W. Heitmeier Hrg.: Was treibt die
Gesellschaft auseinander .suhrkamp Frankfurt 1997
[Laszlo 1996], E Laslo. The Wispering Pod. Element B.
Ma. USA 1996
[Loeckenhoff 1997] H. Loeckenhoff.
Measuring
Sustainable Performance for Societal Control. Paper,
14th Intern. Conf. WACRA Europe e.V. Madrid,
Espana Sept 16-19 1997
idem.: The Emergence of the Qualitative on the Base of
Quantitative Sciences. Att. Exp. Op. Nat. Research
Found. Rep. South Africa, Pretoria 2001
[Mulej, M. et al 2003]: A Worried Look at CEEC’s: e.g.
Slovenia’s Accession to European Union After 15
Years of the Two Generation Cycles of Law. Manus ;
Maribor 2003
[Mulej, Kajzer 2003] Mulej M., Kajzer S. Ethics of
Interdependence and the Law of Requisite Holism. In:
STIQUE 1998, Inst. of Syst. Res. Maribor 1998
[Nalimov 1985] V.V. Nalimov. Space, Time and Life.
The Probabilistic Pathways of Evolution. ISI Press.
1985
[Maturana, Varela 1987] H. Maturana, F. Varela.: Der
Baum der Erkenntnis. Scherz, München 1987
[Rosen, 1985], R. Rosen.
Anticipatory Systems,
Pergamon NewYork 1985
[Schwaninger 1997], M. Schwaninger.
Integrated
Systems Methodology: HInt. Trans. Oper. Res Vol.4
No. 4, pp 109-123, 1997
[Starkermann 2003] Rudolf Starkermann. Freund und
Feind Zwei Archetypen der Evolution. Eine Brücke
zw. Physis u. Psyche. Editions à la Carte Zürich 2003
[Wolfram 2001], Stephen Wolfram. A New Kind of
Science. Wolfram Media Inc. 2001
[Zelger] in R. Buber. J. Zelger, ed.: GABEK II. Zur Qual.
Forschung. Innsbruck 2000