tripleC 11(1): 46-66, 2013
http://www.triple-c.at
Communicative Modelling of Cultural Transmission and
Evolution by Using Abstract Holographic Cognition
Ambjörn Naeve
The Royal Institute of Technology (KTH), Stockholm, Sweden, amb@kth.se,
http://kmr.csc.kth.se/wp
Abstract: This article addresses the problems of specialization and fragmentation that are presently threatening the future
of human civilization as we know it – with the aim to contribute towards enabling a more informed and unified perspective on
the ‘big’ questions that confront us today, the answers to which will determine our future. We present communicative ways
to model the transmission and evolution of the processes and artefacts of a culture as the result of a sequence of interactions between its members - both at the tacit and the explicit level. The overall purpose of communicative modelling is to
create models that improve the quality of communication between people, and we try to do so here by providing a set of
semantically rich conceptual ‘placeholders’ for modelling the intra-, inter-, and supra-actions of any organizational or cultural
entity that is considered to be “important enough to deserve attention” within a certain context. In order to capture the subjective aspects of Gregory Bateson’s definition of information as “a difference that makes a difference,” the article adds
novel features to holographic cognition by abstracting away from the underlying neurophysics of Karl Pribram’s Holonomic
Brain Theory. Instead we introduce an abstract Holographic Cognition Model that uses holography exclusively as a metaphor or analogy for human cognition - with the object beam of holography corresponding to the first difference (the situation
that the cognitive agent encounters), and the reference beam of holography corresponding to the subjective experiences
that the agent brings to the situation, and which makes the second difference - the “holographic interpretation pattern” unique for each agent. Hence, we do not assume the in-brain existence of counterparts of the patch holograms in Pribram's
model, but we note that the metaphor of patch holograms provides a basis for modelling human biases and limitations in
noticing things, as well as in recalling the memories of those things, at the individual, organizational and cultural levels. This
inclusion of both individual and collective human biases increases the scope and the psychological plausibility of the present
models. Moreover, by combining our abstract HCM with a semantically rich and recursive form of process modelling, based
on Ikujiro Nonaka’s SECI theory of knowledge creation, we arrive at a way to model cultural transmission and evolution
processes that is consistent with Wolfgang Hofkirchner’s Unified Theory of Information and the related Triple-C model with
its emphasis on intra-, inter- and supra-actions.
Keywords: Communicative Modelling, Culture as Process, Difference That Makes a Difference, SECI Theory of Knowledge
Creation, Learn-Err Model, Unified Theory of Information, Cognition, Communication, Cooperation
Acknowledgement: This article has been made possible by funding from the TEL-Map project, which is a Coordinated
Support Action within EU-IST-FP7 that focuses on Future Gazing for Technology Enhanced Learning. Moreover, the author
is grateful for the detailed comments and suggestions from the reviewers of the tripleC journal. This feedback has helped to
focus the content and improve the clarity of presentation of the initial version of this article.
1. Introduction, Background, and Contributions of This Article
1.1. The Problems of Specialization and Fragmentation
Around the turn of the last century, there was a sudden explosion of abstraction which had an
enormous impact that is still being felt throughout modern civilization. In fact, this ”intellectual supernova” marks the beginning of the present age of specialization. Today it is impossible for any
single mind to even begin to comprehend the totality of what is going on in our culture in order to
obtain some kind of scientifically based ‘world-view’ in the sense that motivated the thinkers of the
19th century. Instead, we have to content ourselves with much more humble ambitions in our understanding of the human condition.
Unfortunately, this age of specialization has fostered an attitude where the attempts of interdisciplinary understanding have been largely abandoned - giving way to the opposite attitude, the
well-known way of the ’specialist’. In one of his philosophical essays, Science and Humanism, Erwin Schrödinger (1951) discusses, among other things, the problems of specialization. He refers
the reader to an article of the Spanish philosopher José Ortega y Gasset, called La barbarie del
especialismo, where he paints the picture of the specialized scientist as the typical representative
of the brute ignorant rabble - the hombre masa (mass-man) - who endangers the survival of true
civilization. In the translation of Schrödinger (ibid., 110), Ortega writes:
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Ambjörn Naeve
He is a person who, of all the things that a truly educated person ought to know of, is familiar
only with one particular science, nay even of this science only that small portion is known to
him in which he himself is engaged in research. He reaches the point where he proclaims it a
virtue not to take any notice of all that remains outside the narrow domain he himself cultivates, and denounces as dilettantist the curiosity that aims at the synthesis of all knowledge.
It comes to pass that he, secluded in the narrowness of his field of vision, actually succeeds in
discovering new facts and in promoting his science (which he hardly knows) and promoting
along with it the integrated human thought - which he with full determination ignores. How has
anything like this been possible, and how does it continue to be possible? For we must strongly underline the inordinateness of this undeniable fact: experimental science has been advanced to a considerable extent by the work of fabulously mediocre and even less than mediocre persons.
Schrödinger closes his discussion of the specialist-generalist dilemma with the following words:
(ibid., 112):
Never lose sight of the role that your particular subject has within the great performance of the
tragi-comedy of human life; keep in touch with life - not so much with practical life as with the
ideal background of life, which is ever so much more important; and, Keep life in touch with
you. If you cannot - in the long run - tell everyone what you have been doing, your doing has
been worthless.
In the opening statement of the first chapter of his book “Wholeness and The Implicate Order”
(Bohm 1980, 1), the theoretical physicist David Bohm has the following to say about the problems
of fragmentation:
It is especially important to consider this question today, for fragmentation is now very widespread, not only throughout society, but also in each individual; and this is leading to a kind of
general confusion of the mind, which creates an endless series of problems and interferes
with our clarity of perception so seriously as to prevent us from being able to solve most of
them.
The worries expressed in these citations from Schrödinger and Bohm are strongly related to the
Unified Theory of Information (Hofkirchner 2009; 2010), which also addresses the potentially lethal
problems of specialization and fragmentation. According to Hofkirchner (2010, 6):
There has been a qualitative change in the role information can play for the development of
society, and this change is unprecedented in the history of humanity. Information has become
the bearer of survival, the key to our future.
[…] In a word, the continued existence of humanity has shaped up as impossible without conscious and cautious intervention in the process of its own development. This intervention that
is moving towards the reconnection of our disintegrating world – which is falling apart owing to
a process of heterogenization, fragmentation and disintegration – is informational in nature,
but as it extends from the human sphere to the living sphere to the material sphere, it necessitates a deep understanding of the information processes going on in the world we inhabit.
1.2. Culture as Process
There is often a temptation to treat culture and cultural differences as a "thing". However, research in cultural social psychology and related disciplines has demonstrated that culture is
better thought of as a process. Social psychologists have demonstrated that, even when cultural patterns appear to be relatively stable over time, culture is continuously produced and
reproduced in the dynamic interaction between individuals and their social and natural envi1
ronments.
Praslova (2006) reviews several approaches to understanding culture and introduces an integrative model of Culture as Unfolding Process (ibid., 53). She also gives an overview of the struggles with the concept of culture, and on page 50 she cites Lonner (1994), who claims that there are
over 200 definitions of the term ‘culture’, none of which have been embraced by a substantial number of scientists. On the same page, Praslova also cites several sources as evidence that “psy1
Quoted from the Call for Papers Special Issue of Social Psychology on “Culture as Process: Dynamics of Cultural Stability and Change” with deadline June 30, 2011. Accessed June 27, 2012. http://recherche.univlyon2.fr/greps/spip.php?article270
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chologists increasingly tend to see culture as a dynamic process rather than as an index or an entity”. However, the expressive power of Praslova’s CUP model is rather weak, mainly because of
two reasons. First, the model has no visual semantics, and second, it has no explicit time dimension, which means that temporal aspects of the cultural unfolding process cannot be expressed in
the model.
1.3. Contributions and Structure of This Article
The present article introduces a semantically rich and temporally explicit way to model the process of cultural transmission and evolution - based on a set of modelling techniques that is called
communicative modelling (Naeve 2011). Some of these techniques have been described in various
2
earlier publications , but the present article represents an attempt at bringing several of them together into a coherent whole.
Towards this aim we present a de-linearized version of the SECI model of knowledge creation
(Nonaka 1994) and connect this model to the Triple-C model, which was introduced by Hofkirchner
(2002), and which is described by him in the following way (Hofkirchner 2010, 18):
We come across information in three areas of society:
• In the area of cognition, where the content of consciousness are produced by individuals.
• In the area of communication, where common understanding is produced by interactions (individuals), and;
• In the area of cooperation, where sense embodied in societal structures is produced
collectively by individuals acting in a balanced way.
An important strength of the Triple-C model is that it connects the concepts of intra (looking
downwards), inter (looking ‘widewards’), and supra (looking upwards) with the relative position from
which one is looking at the acting systems, as depicted in Figure 1. This makes the corresponding
concepts of cognition, communication, and cooperation recursively applicable at every level of human and organizational activity. In section 2.3 we will see how to take advantage of this recursiveness by modelling it explicitly.
Figure 1: The Triple-C model from a process perspective.
It is this pragmatic focus on the uses of information – for respectively “intro-spection”, “interspection” and “supra-spection” - that provides the UTI with the representational power to model
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concepts such as the intention(al stance) of the agent/agency behind a certain approach to the
concept of information itself. This is needed in order to integrate and unify such diverse views on
information as those of Shannon (1948) and Bateson (1972; 1978). Shannon intended to account
for the complexity involved in transmission of information by machines, whereas Bateson, with his
definition of information as “a difference that makes a difference,” intended to capture the subjective aspect of information, the “making” of the (second) difference, from the perspective of a human
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subject.
2
Notably Naeve (1997; 2005) and Naeve et al. (2007).
To use a term coined by the philosopher Daniel Dennett for the level of abstraction in which we view the behavior of a
thing in terms of mental models. See http://en.wikipedia.org/wiki/Intentional_stance (Accessed on 27 June 2012).
4
These two intentions are more or less mutually exclusive, and by naming his seminal paper “A Mathematical Theory of
Communication”, Shannon (1948) in fact introduced a hitherto unfamiliar connotation of the term ‘communication’.
3
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Ambjörn Naeve
The present article is structured as follows: Section 2 introduces the SECI modes of knowledge
conversion (Nonaka 1994) and shows how to ‘de-sequentialize’ the original SECI model and align it
with the Triple-C model. Section 3 introduces the learn-err model, which models learning as driven
by errors or surprises, i.e., differences between theoretically expected and experimentally inspected phenomena. This section also discusses the OADI-SMM model of Kim (2004), which builds on
the concepts of single-loop and double-loop learning (Argyris and Schön 1978) and connects these
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two forms of learning between the individual and the organizational level . Section 4 reviews the
basics of Pribram’s Holonomic Brain Theory and introduces an abstract Holographic Cognition
Model, which maps human cognition to holography and compares subjective interpretation of a
given situation with “holographic interference patterns” between an external “object beam” and an
internal/subjective “reference beam” that consists of individual and collective experiences, beliefs,
and assumptions. Section 5 assembles the pieces and explains how to model cultural transmission
and evolution in a communicative way. Finally, Section 6 presents conclusions and future work.
2. Modelling Individual and Organizational Intra-, Inter-, and Supra-action
2.1. The SECI Modes of Knowledge Conversion
According to Nonaka (1994) the key to the creation of new knowledge lies in the following four
(SECI) modes of knowledge conversion, which occur when tacit and explicit knowledge interact
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with each other :
Socialization, which is the process of sharing experiences (tacit knowledge), thereby creating
new tacit knowledge.
Externalization, which is the process of articulation and conversion of tacit knowledge into
explicit knowledge.
Combination, which is the process of restructuring and aggregating explicit knowledge into
new explicit knowledge.
Internalization, which is the process of reflecting on and embodying explicit knowledge into
tacit knowledge.
•
•
•
•
As illustrated in Figure 2, which is taken from Naeve (2005), a knowledge-creating spiral occurs
when these modes of interaction between tacit and explicit knowledge are elevated from the individual, to the group and organizational levels. Organizational knowledge creation, therefore, should
be understood as a spiralling process that organizationally amplifies the knowledge created by
individuals and crystallizes it as a part of the knowledge network of the organization. This process
takes place within an expanding “community of interaction” which crosses intra- and interorganizational levels and boundaries.
Figure 2: The SECI spiral of knowledge creation.
7
5
Organizational double-loop learning is a characteristic of the Learning Organization (Senge 2006).
The concept of tacit knowledge was introduced by Polanyi (1967).
7
The triangle-shaped arrow connecting ‘Explicit’ and ‘Tacit’ to ‘Knowledge’ is a UML-like notation for specialization.
Hence, explicit knowledge and tacit knowledge are modelled as two different kinds of knowledge (Rumbaugh et al. 1999).
6
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2.2. Combining the SECI Theory With Process Modelling
By combining learning process modelling (Naeve et al. 2005; 2008b) with the SECI theory of
knowledge creation, we can create a SECI process framework (abstract model) for the description
and classification of knowledge-creating learning processes. In Figure 3 we have introduced the
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four different kinds of ba, as well as their corresponding tools of support. Socialization occurs in
originating ba, where experiencing and empathizing activities are supported by community-building
tools. Externalization occurs in dialoguing ba, where articulating and conceptualizing activities are
promoted by discussion supporting tools. Combination occurs in systemizing ba, where connecting
and deducing activities are supported by conceptual modelling tools. Internalization occurs in exercising ba, where reflecting and embodying activities are supported by reflective analysis tools.
Figure 3: The SECI process framework.
In each of the four SECI knowledge conversion stages a learning process takes place. As
shown in Figure 3, which is taken from Naeve et al. (2005), sharing experiences in the socialization
process, with input from visions, challenges and activities, produces new individual understanding
of the issues at stake. This new individual understanding is then externalized and articulated into
new collective understanding of the same issues. Then the combination process deductively produces increased collective understanding, which is then internalized by reflection and embodied
into increased individual understanding.
As described in Naeve et al. (2005; 2008a), the SECI process framework provides a methodology for researching the structure of knowledge-creating learning processes and how to best support them with various tools. An attempt at such a classification, based on the SECI process
framework, has been carried out by Yli-Luoma and Naeve (2006).
2.3. Aligning the SECI Model With the triple-C Model9
The sequential nature of the SECI model is not well adjusted to describing what is actually going on in knowledge creation. Nonaka et al. (2000, Figure 5) indirectly acknowledge this problem
when they describe the knowledge-creating process as a collection of intertwined SECI spirals of
various sizes that interact with each other.
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Nonaka and Takeuchi (2005) introduce the Japanese concept of ba (which roughly means “place for interactions”) as
a crucial enabler for effective knowledge creation. The Japanese word ‘ba’ is a concept that unifies physical space (such as
e.g., an office space), virtual space (such as e.g., e-mail), and mental space (such as e.g., shared ideas). Within an organizational context, it is the role of middle managers to maintain the necessary manifestations of such ba in order to support
the knowledge creation spiral and make it efficient for the purposes of the organization.
9
This section is based on Naeve et al. (2007)
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Ambjörn Naeve
Figure 4 shows a non-sequential, SECI-based way to model the communication process at two
different organizational levels, called intra and inter. An important aspect of this model is that it is
recursive, i.e., applies to communication across any boundary level of an organization, as well as
between different organizations. For example, the intra-level could refer to two individuals communicating at the inter-individual (= group) level, or two groups communicating at the inter-group (=
department- or organizational) level, etc. We will return to this topic below.
Figure 4: SECI-based inter- and intra-action processes.
Note that the SECI spiral has been modified in such a way that the S- and C-parts (Socialization
and Communication) are going on in parallel, while the I- and E-parts (Internalization and Externalization) are feeding information back and forth between the explicit (= formal = conscious) and tacit
(= informal = subconscious) knowledge levels. Moreover, the intended semantics of the model is
that also the I- and E-parts are considered to be going on in parallel. This is shown more clearly by
the notation, which is introduced in Figure 5. The C- and S- parts run in parallel, and so do the Iand E-parts. As mentioned above, the Triple-C model identifies the intra, inter, and supra levels of
action with respectively cognition, communication, and cooperation.
Figure 5: Combination and Socialization correspond to the Explicit and Tacit parts of a process.
In Figure 6 we show how to make use of the modified SECI-model in order to model cognition
within, communication between, and cooperation among two groups within the same organization.
The vertical (dotted) lines refer to the environmental interfaces of the groups and the organization,
while the horizontal lines refer to the cultures (= meaning and memory) of the groups respectively
the organization. These cultures represent the shared mental models (“world view”) and routines
(“the way we do things”), respectively within the organization and within the groups. The filled dots
refer to dominant stakeholders, while the unfilled dots refer to sub-dominant stakeholders.
As described in more detail in Naeve et al. (2007), the interpretation of the model of Figure 6 is
that the group to the left is driven mainly by some overall organizational goals and to a lesser extent by its own group-specific goals, while the opposite is true for the group to the right. Also, the
left group is drawing its main support for its actions from the organizational culture, while the right
group is drawing its main support from within its own group culture. Hence, the model implies that
the left group is acting more for the overall benefit of the organization, while the right group is acting more in its own self-interest.
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Figure 6: A recursive SECI-based model of a ‘unselfish’ and a ‘selfish’ group that are intra-, inter-,
and supra-acting within the same organization.
There is an aspect of this model that requires clarification. Although only individuals are capable
of internalization and externalization, these processes can take place in different contexts (at dif10
ferent levels) . The E- and I-parts in the centre of Figure 6 represent what is externalized and internalized (by individuals) at the inter-group level, e.g., in a meeting between the two groups. The
left and right E- and I-parts represent what is externalized and internalized at the intra-group level
(by individuals) in meetings within each separate group. The horizontal arrows between the E-parts
indicate that what is externalized at the meeting depends on what has been externalized by each
separate group before the meeting, i.e., what each group has discussed in advance and decided to
express at the meeting. The horizontal arrows at the I-level indicate that although some internalization goes on at the meeting, each group also brings back “something to think about” from the meeting.
Figure 7: Individual, Group, and Organizational levels of the recursive SECI-based model.
As mentioned above, a great merit of the non-sequential SECI-based model presented here is
that it applies recursively to intra-, inter-, and supra-actions at any level. As we have seen, this is
consistent with the relativity of the intra-inter-supra perspective that underpins the Triple-C model of
the Unified Theory of Information. In Figure 7 these actions have been expressed at three different
levels, the individual level, the group level and the organizational level.
2.4. Simplifying the Notation of the Modified SECI Model
It is important to note that the model allows for socialization and combination to go on within a
single individual. This makes it easier to handle refinements of ideas that take place in solitude.
10
A dynamic model of the interplay between these processes in the context of creating and transforming organizational
culture is presented in Figure 10 (Section 3.2).
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Ambjörn Naeve
Figure 8: Simplified notation for the recursive, SECI-based model.
Having established a precise notation for the modified SECI-model, a simplification will now be
introduced in order to allow us to concentrate on the elements of interest. It is often desirable to
suppress the E- and I-parts of a process and focus on the C- and S-parts. In a work process there
is always a formal part, which represents the production of some product or service, and an informal part, which represents the supporting (tacit) context within which the formal production process
takes place.
Figure 8 displays this simplified notation. The basic idea is to divide the traditional process symbol into an upper and a lower part, and let the upper (C) part represent the formal (explicit) part of
the process and the lower (S) part represent its supporting informal (tacit) part. Moreover, the Eand I-parts are assumed to go on during the entire C/S process and to transform knowledge between the tacit and explicit levels whenever this is needed.
3. Modelling Individual and Organizational Learning
3.1. The Learn-Err Model
To err is to make mistakes. According to Senge (2006, 143), Edwin Land, the creator of Polaroid corporation, had a sign on the wall of his office that read: “A mistake is an event the full benefit
of which has not yet been turned to your advantage”. This statement highlights the idea of the mis11
take as a learning opportunity, which is the essence of the learn-err model depicted in Figure 9 .
Figure 9: The learn-err model.
At the top of this diagram there is a large process symbol, which represents the scientific process. Inside the scientific process, the Now is modelled as a process that takes the Future as input
11
In the learn-err model, the horizontal division of the process symbol of the Learn process does not indicate a separation between explicit and tacit levels of communication. Instead, this division should be interpreted as a separation between
effectiveness (knowing why) and efficiency (knowing how). In this article, the learn-err model represents the only exception
to the semantics behind the notation of the modified SECI model (Figure 8).
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and gradually transforms it into the Past. Above this process there is an Actor/Reactor agent representing a scientist, which in our time is a role model for acting and reflecting human beings in general.
As scientists we construct fantasies (normally called theories) about the workings of the world
that surrounds us. If we want our fantasies to have predictive power, they must always be directed
towards the future, and try to answer questions such as “What would happen if …?” Such fantasies
naturally create expectations that lead us to construct experiments that can test our fantasies in
order to verify or falsify them. This experimental process is always directed towards the past and
tries to answer questions such as “What happened when …?” The results of this experimental “in12
spection process” are referred to as inspectations .
If things turned out (= were ‘inspected’) exactly as expected, i.e., if the inspectations agreed with
the expectations, then this lack of surprises strengthens the tested theory. In this case, the diagram
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representing the scientific process is said to commute (or be commutative) , a fact which is expressed by the # sign in Figure 9. In contrast, if things did not turn out to have become exactly as
expected, then there were some surprises occurring. Either something unexpected happened (=
was inspected), or something that was expected did not happen (= was not inspected). In this
case, the scientific process diagram does not commute, which is expressed by the ¬# (not commutative) sign in Figure 9. In the learn-err model, this is when the need for learning arises and the
Learn process “kicks in”.
In this context, making mistakes is equated with generating surprises, i.e. not creating expected
results, or creating unexpected results. Hence, the learn-err process loop is driven by surprises,
i.e., the lack of agreement between expectations and inspectations. This fact is represented by the
decision-box to the right. As long as there is a difference, i.e., as long as there is an ‘error’ in our
expectations, then there is something to learn in order for our fantasies to become improved. Here
‘effective’ means doing (= fantasizing about) the right things, and ‘efficient’ means doing the things
right, i.e., fantasizing about them in the right way. Hence, effectiveness is concerned with
goals/impact efficacy (knowing why), while efficiency is concerned with process efficacy (knowing
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how) .
As modelled in Figure 9, improving our knowledge of why, hopefully leads to doing better things,
and improving our knowledge of how, hopefully leads to doing things better. Any one of these improvements will inform the scientific process and help us to construct more effective (effectiver?)
and/or more efficient (efficienter?) fantasies. The ‘hopefully’ part is modelled by the question marks,
which can only be eliminated by testing of the updated (and hopefully improved) fantasies.
The learn-err process stops when the testing of our fantasies does not produce any more surprises. Then we experience that we are doing the right things right. Of course, there is a pragmatic
element involved in the testing of the fantasies. In practice, the learn-err process stops when we
are experiencing that we are doing enough right things right enough.
3.2. Single- and Double-loop Learning for Individuals and Organizations
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The OADI-SMM model (Kim 2004) incorporates the concepts from Argyris and Schön (1978)
of single-loop and double-loop learning - on both the individual and the organizational levels. In
contrast to single-loop learning, which only involves applying previously acquired knowledge and
skills to dealing with the problems that are encountered by an individual or an organization, doubleloop learning also involves surfacing and challenging deep-rooted beliefs and assumptions that
have previously been inaccessible, either because they were unknown, or because they were
known but undiscussable.
Individual double-loop learning is traced out in Figure 10 as the process through which an individual’s learning affects the individual’s mental models that in turn affect the individual’s future
learning. Organizational double-loop learning occurs when changes in individual mental models
become incorporated into the organization through shared mental models, which can then affect
organizational action. In both cases, double-loop learning provides opportunities for discontinuous
steps of improvement where reframing a problem can bring about radically different potential solutions. In the formulation of Kim (2004, 48):
As mental models are made explicit and actively shared, the base of shared meaning in
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to emphasize the duality with the expectations created by a theory.
Compare http://en.wikipedia.org/wiki/Commutative_diagram (Accessed on 27 June 2012).
These definitions are consistent with Peter Drucker’s distinction between efficiency and effectiveness, cited in Haas
Edersheim (2007, 13): “Efficiency is doing things right, effectiveness is doing the right things”.
15
Observe, Assess, Design, Implement – Shared Mental Models.
13
14
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Ambjörn Naeve
an organization expands, and the organization’s capacity for effective coordinated action
increases.
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Figure 10: Individual and organizational single-loop and double-loop learning .
4. Physical and Abstract Holographic Cognition
As mentioned above, Gregory Bateson (1972; 1978) defined information as "a difference that
makes a difference". The making of that difference is the key to the workings of the process of exformation (Nørretranders 1991; Lefrère 2011), which is the process of disregarding what is unimportant (inessential) in a given situation. This process is always biased, i.e., it depends on some
form of pre-judgements (= experience = prejudice). We will now introduce a powerful and intuitive
way to model such human biases in terms of a Holographic Cognition Model.
4.1. What is Holography?
As described by Wilber (1982, 6):
Holography is a method of lensless photography in which the wave field of [coherent] light
scattered by an object is recorded on a plate as an interference pattern. When the photographic record – the hologram – is placed in a coherent light beam like a laser, the original
wave pattern is regenerated. A three dimensional image appears. Because there is no focusing lens, the plate appears as a meaningless pattern of swirls. Any piece of the hologram will
17
reconstruct the entire image .
4.2. The Holonomic Brain Theory
The renowned brain researcher Karl Pribram has proposed a Holonomic Brain Theory (Pribram
1987; 2007) for brain processes, including memory storage and retrieval, which implies that the
18
deep structure of the brain is essentially holographic . Instrumental in helping Pribram to arrive at
his theory was the theoretical physicist David Bohm, who had speculated that the nature of the
universe might be analogous to a hologram, a realm of underlying frequencies whose interference
patterns create the experience of concrete reality (Bohm 1980, 144-147). According to Wilber
(1982, 7-9):
16
Modelled from Kim (2004, Figure 2.7).
This process was first described mathematically by Denis Gabor in 1947, for which he received the Nobel price in
physics in 1971. Making use of the newly invented laser, the first realistic holographic images were created by Emmeth
Leith and Juris Upatnieks in 1964.
18
For more details on the HBT (described in non-specialist terms) see the online interview with Karl Pribram by Daniel
Goleman (2007). Accessed on 27 June 2012 at http://www.sybervision.com/Golf/hologram.htm#memory
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An impressive body of research in many laboratories has demonstrated that the brain structures see, hear, taste, smell and touch by sophisticated mathematical analysis of temporal
and/or spatial frequencies. An eerie property of both hologram and brain is the distribution of
information throughout the system, each fragment encoded to produce the information of the
whole. […] Karl Pribram’s research and theory encompass the whole spectrum of human consciousness: learning and learning disorders, imagination, meaning, perception, intention, paradoxes of brain function.
In one of his more recent formulations of the HBT, Pribram (2007) writes:
The Holonomic Brain Theory describes a type of process that occurs in fine fibered neural
webs. The process is composed of patches of local field potentials described mathematically
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as windowed fourier transforms or wavelets . The fourier approach to sensory perception is
the basis for the holonomic theory of brain function. Holonomy, as its name implies, is related
to the unconstrained Fourier co-ordinate system described by holography.
4.3. Introducing an Abstract Holographic Cognition Model
We will now abstract away from the underlying neurophysics of the HBT and introduce a purely
conceptual and non-physical Holographic Cognition Model. In this abstract HCM, the second difference in Bateson’s definition is modelled as a holographic interference pattern – a personal interpretation hologram – created by the human observer by applying his/her personal “reference beam” of
past experiences (mental models) to the “object beam” of the present situation (Figure 11).
The key feature of this model is that different observers will interpret the same situation in different ways, because they bring different reference beams to the same object beam, thereby creating different interpretation holograms. Hence, the abstract HCM provides an intuitive way to model
“the making of the second difference” in Bateson’s definition of information. However, as most
analogies, the holographic analogy does not conserve the validity of all of its connotations when it
is applied to human cognition.
Figure 11: Interpreting an object through a tacit reference model
By making use of a holographic analogy for the cognitive process, the abstract HCM in fact
maps the cognitive process to the holographic process. Then some useful aspects or properties of
20
the holographic process can be “pulled back” and used to model the cognitive process .
19
[Comment by the present author]: In this context, the term ’or’ should be interpreted as an ”exclusive or” (a so-called
’xor’), since, from a mathematical point of view, fourier transforms and wavelet transforms (as the latter should be properly
called) represent two different methods of creating a holographic interference pattern by summing (or integrating) a very
large number of ’primitive’ waves - in fact, a continuous infinity of such waves. Fourier transforms use the trigonometric
functions sin( ) and cos( ) as primitive building blocks, while wavelet transforms use wavelets – also called ”brief oscillations”
- which are smoothly oscillating functions that ’live’ (= deviate from zero) only within a finite time interval. For more details on
wavelets, see http://en.wikipedia.org/wiki/Wavelet_series (Accessed on 27 June 2012).
20
This is an illustrative example of a general method of ”analogic mapping” from a lesser known into a better known
area. Such analogic mapping is carried out in the following two steps:
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For example, the interference property of holography is pulled back to the subjectivity property
of cognition: Just as different reference beams give different holograms when mixed with the same
object beam, different agents experience the same situation in different ways since they bring different reference beams to evaluate it.
Moreover, the reconstruction aspect of holography is pulled back to the ‘remembrance’ aspect
of cognition, i.e., the process of remembering “by reconstruction”. However, in this case not all of
the properties of holographic reconstruction are conserved. For example, an optical scene can be
uniquely reconstructed from a hologram of it, while an experienced situation cannot be uniquely
21
reconstructed from an impression or memory of it .
In section 4.4 we will demonstrate how this non-uniqueness of reconstruction can be turned into
a feature of the abstract HCM by introducing tacit cultural reference beams on top of which the
individual reference beams are superposed, and over which the individuals involved have little or
no conscious awareness or control. Such a tacit cultural reference beam can be thought of as a
form of cultural background, which tacitly frames and situates the cognition of each individual that
22
participates in the corresponding culture .
Figure 12 sums up the abstract HCM on the individual level. A cognitive agent, called the signa23
tor , assigns different relevance to the properties and behavior of different aspects of the situations
24
it encounters .
Figure 12: A signator evaluates a situation by assigning relevance to its different aspects
The assigned relevance depends on the perception filters (biases) of the agent/signator, which
are related to its past experiences, thoughts, assumptions, beliefs, and desires. This evaluation
process creates gaps between the observed present and the desired future, leading to an action
plan according to which the agent will apply different actions that attempt to modify the present
situation according to its wishes and expectations for the future.
4.4. Introducing Tacit Cultural Reference Beams into the HCM
We will now revisit the interaction between the individual and the shared mental models that is
indicated by the process loop marked “organizational double-loop learning” in Figure 10. Since our
Holographic Cognition Model is abstract, we are free to regard the individual reference beams as
being part of – or embedded within - various organizational or cultural reference beams - as depicted below in Figure 13 (which only shows one such beam). This “cultural embedding” of individuals
(1) Find a mapping that takes the less familiar domain into some better known area that can be considered to be analogous (or homomorphic as one says in mathematics) to the original domain. For example, when we say that human cognition is a bit like holography, we are in fact perfoming an analogic mapping – assuming that holography is a more familiar and
better known area than the domain of human cognition.
(2) When such an analogic mapping has been established, we try to pull back different concepts and properties of the
better known area into the lesser known domain. Such pulled-back entities are then examined as potential candidates for
new concepts and properties that can be applied within the lesser known domain.
21
In fact, brain research has demonstrated that memories are dynamic, in the sense that whenever we access them we
are also liable to change them.
22
These tacit cultural backgrounds are closely related to the tacit support functions of a culture that are introduced in
section 5.3 below.
23
In accordance with Hofkirchner (2010, 70).
24
We introduce the notation “< Situation | Signator > = Situated Significance,” which is inspired by the Bra-Ket notation
introduced by Paul Dirac into quantum mechanics.
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captures the fact that individual tacit reference models do not arise in isolation, but are formed within a multitude of different organizational and cultural contexts. In the abstract HCM, each such context can be thought of as adding its own specific reference beam to the interpretation hologram that
is formed by an individual within this context.
The feedback loop between the individual and the shared mental models in Figure 10 corresponds to the feedback loop between the tacit individual and the tacit cultural reference models in
Figure 13. The latter figure also contains two other feedback loops that model (i) that the individual
reference models are updated by cognitive impressions, and (ii) that the cultural reference model is
updated by explicit expressions generated from these impressions. This is consistent with the
modelling of the “Press” process in Figure 17, which outputs im-pressions that are externalized into
25
ex-pressions .
Figure 13: Evolving a tacit cultural reference model
In summary, the main advantage of disregarding the neurophysics in Pribram's Holonomic Brain
Theory is that the metaphor/analogy of patch holograms provides a basis for modelling our biases
and limitations in noticing things - and then recalling and processing our memories of those things
– against an implicitly present cultural background of which we are largely unaware. This inclusion
of culturally embedded human biases increases the scope and the psychological plausibility of the
abstract HCM.
4.5. Emergent Stability of an Organizational or Cultural Reference Model
The cultural reference model introduced in the last section represents the world-view and the
behaviour of an organization or culture. The question then arises: How can we account for the stability of such organizational or cultural behaviour? According to Rocha (1998):
Heinz von Foerster [1965; 1969; 2003] equated the ability of an organization to classify its environment with the notion of eigenbehavior. He postulated the existence of some stable structures (eigenvalues) which are maintained in the operations of an organization’s dynamics. Following Piaget [von Foerster 2003], he observed that any specific instance of observation of
such an organization, will still be the result of an indefinite succession of cognitive/sensorymotor operations. This reiterated the constructivist position that observables do not refer directly to real world objects, but are instead the result of an infinite cascade of cognitive and
sensory-motor operations in some environment/subject coupling. Eigenvalues are selfdefining, or self-referent, through the imbedding dynamics – implying a complementary relationship (circularity, closure) between eigenvalues and cognitive/sensory-motor operators: one
25
See section 5.2 below.
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implies, or defines, the other. "Eigenvalues … represent the externally observable manifesta26
tions of the (introspectively accessible) cognitive [operations]" (von Foerster 2003, 265) .
Eigenbehavior can be thought of as a kind of “dynamic attractor” of the cognitive feedback loop
model that is presented in Naeve (2011, section 13.4). However, elaborating on this relationship is
outside the scope of the present paper.
5. Modelling Cultural Transmission and Evolution
5.1. Abstracting the Cultural Transformation Process
The abstraction that will be presented in this section starts by realizing that culture is strongly
connected with the past. Of course, this does not imply that cultures cannot be “future-oriented,” or
“celebrating the future of …” etc. What it does mean however is that the idea of culture is strongly
linked to the idea of preservation of certain artefacts and behaviours that are deemed by the “cultural curators” to be worth preserving. Moreover, the idea of culture is strongly linked to the idea of
27
working to pass this information on to future generations , both in the form of theories (= world
views) and practices (= organizational routines).
The ‘skeleton’ of the abstract evaluation-transformation model is given by the following fundamental observation:
The past is evaluating the present with respect to its plans for the future based on its wishes
and expectations (Figure 14), while, at the same time, the present is transforming the future into
28
the past (Figure 15) .
Figure 14: The Past is evaluating the Present with respect to its Plans for the Future (inner contour
process).
In accordance with the < Situation | Signator > notation of Figure 12, the notation < Present |
Past > denotes the evaluation part of this process. Note that we often refer to the present as a ‘sit29
uation’ (as in the expression “the present situation”) .
In this model, the Past plays the role of an abstraction of the biases/experiences of the Signator
of Figure 12. From the arrows pointing downwards into the Future process, we can infer that the
26
[Comment by the present author]: Organizational eigenvalues and eigenbehaviors are rooted in quantum mechanics,
where each observable (= measurable physical entity) is assumed to be associated with a so-called hermitian operator.
Such an operator always has a set of real eigenvalues, and in quantum mechanics these eigenvalues correspond to the set
of values that can result from measuring the physical entity in question. Moreover, when such a measurement is undertaken, the underlying wave function of the measured entity is ‘disturbed’ and forced into a new state, which is an eigenvector
(or eigenstate) of the operator of the measured entity, and this eigenvector corresponds to the eigenvalue that was measured for the entity. Such an eigenvector is the quantum-mechanical equivalent of an organizational eigenbehavior.
27
In Swedish this activity is called ‘tradera’. Unfortunately, the English term ‘trade’ has totally different semantics.
28
In fact, the present acts as a hyper-plane in “past-future space”. Just as you cannot pass from one side of a plane in
space to the other side of it without actually passing through the plane, no part of the future can turn into the past without
passing through the present.
29
In Figure 14 and Figure 15 the transformation of the future into the past by the present is implicitly modelled by the
fact that the “future process” feeds into the “present process,” which feeds into the “past process”.
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Past has both explicit and tacit wishes and expectations for the Future, which underpin its explicit
plans for the Future.
Figure 15: The Present is transforming the Future into the Past (outer contour process).
In terms of the contour notation introduced in Figure 1, the Past is cooperating with the Present
to evaluate the Future (inner contour), while the Future, Present, and Past are cooperating to transform the Future into the Past through the Present (outer contour). Moreover, the result of the
< Present | Past > evaluation process is modelled as tacit output in the form of Impressions, which
30
are then externalized to form Expressions, which are presented in the form of Inspectations .
Figure 16: Different combinations of wishes, expectations, and inspectations
The outcome of the < Present | Past > evaluation process involves all possible combinations
31
of wishes, expectations, and inspectations. Hence, it consists of seven different parts , which are
depicted in Figure 16. Of course, the relative significance of these different parts depends both on
the context and on the psychological disposition of the agent. For example, a notorious optimist
would often find that many of her/his wishes and positive expectations would not be inspected in
reality, whereas a ingrained pessimist would tend to find that many of her/his negative expectations
(= fears), would in fact not be inspected.
5.2. Storing Im-Pressions and Ex-Pressions of Encountered Situations
The holographic cognition model is part of the cultural evolution model depicted in Figure 17,
32
which has a cylindrical (= wrap-around) connectivity . A judgement is modelled as consisting of
30
31
32
Compare the learn-err model (section 3.1).
Combinatorially speaking, the eighth part is the ‘empty’ combination, which has no counterpart in this model.
Expressed by the fact that the concept Judgement appears both to the left and to the right in the model.
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ex-pression(s) and im-pression(s). They are produced in the im-press/ex-press process to the right
and stored in the im-store/ex-store process to the left, where it becomes history. Judgement also
consists of sign(s), which have explicit signifier(s) and tacit signified(s) in the semiotic sense of
33
Saussure.
Figure 17: Cultural evolution by storing im-pressions and ex-pressions of encountered situations
As is seen by comparison with Figure 8, the press- and store-processes are divided into explicit
and tacit parts. The past, i.e., the history (of a culture), is represented by its explicit part, the exhistory, and its tacit part, the im-history. They are the explicit respectively the tacit outcomes of the
store process. An encountered situation is influenced by the present context and the past history of
the culture. The situation is judged (evaluated) by a signator who first reflects on the different aspects of the situation and conjures up its appropriate pre-judgement(s), which are based on its
beliefs and assumptions. The signator then passes judgement(s) on the situation and creates a
‘cognitive hologram’ (in the form of an im-pression of the situation) by using its pre-judgement(s) as
a mental reference beam. The im-pression(s) are then externalized and expressed in the form of
ex-pression(s), and then both the im-pressions and the ex-pressions become ex-history respectively im-history, which together make up the history of the culture, which represents the cultural (collective) memorization process.
It is important to observe that ex-pressions can be stored in externally accessible ways, while
im-pressions can only be stored in the tacit memory of living persons. In fact, the tacit part, i.e., the
im-history, of the history of a culture consists of the collection of all the mental models of its members. Hence, the im-history of a culture is always ‘stored in’ (= resides in) the aggregated memories
of all the living members of this culture.
5.3. The Tacit Support Functions of a Culture
The Tacit Intersection Support Function (TISF) of a culture C consists of the shared mental
models of the people living within C. The TISF of C guides the attention of the people of C and
helps them to interpret the relevance of the im-pressions and ex-pressions that they encounter in
collectively relevant situations. The im-pressions originate only from the living, while the expressions can originate both from the living and from the dead.
The TISF represents the mainstream tacit part of the culture, which consists of the stored impressions that “make sense” for all members of the culture. If instead we look at the Tacit Union
Support Function (TUSF) we get a tacit support function each of whose concepts makes sense for
34
some member(s) of the culture .
5.4. Tacit and Explicit Knowledge Transfer
When the active life spans of two individuals overlap, they have the possibility of sharing both
ex-pressions and im-pressions (Figure 17) of a situation in terms of direct (explicit and tacit) inter35
action between them . In contrast, when their active life spans do not overlap, there is only the
possibility of knowledge transfer from the earlier (past) to the later (future) individual. In this case
there are two possibilities: (1) explicit knowledge transfer via recorded knowledge, and (2) indirect
33
See e.g., http://en.wikipedia.org/wiki/Sign_%28semiotics%29 (Accessed December 7, 2011).
See Naeve (2011, section 14) for a discussion on how these support functions influence the ‘inter-operations’ between different cultures.
35
This corresponds to socialization in the sense of the SECI model.
34
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tacit knowledge transfer via a tacit chain of interactions, i.e., a sequence of people whose active life
spans overlap consecutively and connect the two initial (non-overlapping) life-spans.
Figure 18 shows two examples of recorded knowledge transfer: (1) without tacit support (top),
and (2) with tacit support (bottom). The top example corresponds (for instance) to a company that
changes its management team in such a way that the old and the new teams never meet and interact, but instead the old team only leaves written instructions (recorded knowledge) for the new
team about how best to run the company and what issues that are the most important ones to address.
Figure 18: Recorded and tacit knowledge transfer
In contrast, the bottom example of Figure 18 shows a “tacit overlap” between the old and the
new management teams, where the recorded knowledge is supported by both tacit and explicit
interactions between the two teams.
5.5. Assembling the Pieces into a Cultural Transmission and Evolution Model
Figure 19 depicts a situation where different groups of agents are cooperating on different
levels. The agents A, B, C form the cooperating group (ABC), the agents D, F form another cooperating group (DF), and the agents G, H form a third cooperating group (GH). Moreover, the ‘inter36
nal’ groups (DF) and (GH) form a group ((DF)(GH)) that is cooperating with the group (ABC) .
36
Figure 19 should be thought of in 3D with the ‘pistol-shaped’ contours of the agents being located in parallel planes.
The three blue lines are located in a plane, called The Now Plane, which is perpendicular to these planes. The Now Plane
represents the present moment of time, and as time passes, this plane is translated towards the right.
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Figure 19: Cooperation between the groups (ABC) and ((DF)(GH)).
Combing this model with the recursive SECI-based model of section 2.3 (Figure 7), and introducing coherent colouring with Figure 19, the interactions within the group ((DF)(GH)) are modelled
recursively in Figure 20.
Figure 20: Interactions within the group ((DF)(GH)).
The result of combining the models of Figure 19 and Figure 20 is shown in Figure 21. If we think
of the recursive (SECI-based) “conversations plane” as representing all conversations that are
going on in a culture at a certain moment of time, then the model depicted in Figure 21 represents
37
the evolution of these conversations over time .
Figure 21: Cultural intra-, inter-, and supra-conversations over time
In principle – but, of course, not in practice – we are now in a position to model the (tacit and
explicit) transmission and evolution of a culture, since the model in Figure 21 has ‘hooks,’ i.e., conceptual placeholders, that can capture both the tacit and explicit interactions that take place within
the culture – as well as the resulting records of these interactions. The model of Figure 21 is sup37
The results (= records) of these conversations (the black dots of Figure 18) are not shown in Figure 21.
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ported by the models of: Figure 11 and Figure 13 (the abstract Holographic Cognition Model),
which describe the (cognitive) intra-actions of each agent; Figure 17 (storing im-pressions and expressions of encountered situations), which describes the collaborative version of the HCM; and
Figure 20, which describes the (communicative) inter-actions and (cooperative) supra-actions of
the agents taking part in a “cultural discourse” that is translating and transforming the corresponding culture.
Moreover, the dynamics of the model of Figure 21, i.e., when the conversational activities of the
culture are activated, is controlled by the learn-err model of Figure 9. This model should be thought
of as “sitting on top” of the cultural conversation model of Figure 21 and initiating new cultural con38
versations whenever important expectational errors/surprizes are encountered . Two examples of
such surprises are the present financial and ecological crises, which are currently sparking off a lot
of confused conversational activities. Hopefully, the modelling techniques presented in this article
can contribute towards reducing this conceptual confusion and improve the conversational clarity
and relevance of the inter-cultural discourses and supra-cultural actions and activities on which our
future critically depends.
6. Conclusions and Future Work
It has been the overall aim of this article to provide a set of modelling techniques that can help
to create overviews of “the big picture” - overviews that are vital for addressing the problems of
specialization and fragmentation that threaten the future of civilization as we know it. This aim has
been addressed by introducing a set of semantically rich communicative modelling techniques notably the modified SECI model for non-sequential and recursive knowledge creation (Figure 6)
and the learn-err model for learning “by surprize” (Figure 9). Moreover, in order to model the subjectivity of human perception, an abstract Holographic Cognition Model was introduced (Figure 11)
and elevated to the collective level (Figure 13). Finally, it was demonstrated how these models can
be integrated into a model of cultural evolution (Figure 17) by cultural conversations (Figure 21).
The resulting overall model provides conceptual placeholders for capturing any part of a cultural
evolution process that is deemed to be important enough to be captured - within a specific context,
39
for a specific purpose, and with respect to a specific target group .
The models presented in this article represent an initial attempt to capture and structure the elusive concepts involved in cultural evolution by conversations, and they need to be elaborated and
refined in various ways. For example, they would benefit from being connected with the theory of
learning conversations initiated by Pask (1976; 1996) and elaborated by Laurillard (2002), as well
as from deepening their connection with the theory of the learning organization (Senge 2006).
The abstract Holographic Cognition Model should be augmented by a model of the cognitive
feedback loop - in order to better describe the formation of mental models, as well as the connec40
tions between will, skill, action and reflection . It would also be desirable to expand the HCM to
include von Foerster’s ideas of organizational eigenbehavior (section 4.5), as well as to include the
idea of alter-ego-tuning (Hofkirchner 2010, 122-123).
Moreover, as outlined in Naeve (1997; 2011), the cognitive, communicative and cooperative activities discussed in this article have profound connections to an area of abstract algebra called
Category Theory. For example, as discussed in Naeve (2011, section 14), the limit concepts of CT
can be used to model the interoperability aspects of different cultures. However, in order to make
these ideas more widely applicable, they need to be carefully ‘metaphorized’ and elaborated in
ways that are accessible outside the highly specialized community of abstract mathematics.
In order to overcome the problems of specialization and fragmentation and deal effectively and
efficiently with the social, economic and environmental problems that humanity is facing, we have
at our disposal the fundamentally important “intellectual forces” of transparency and accountability.
It is my ambition to make use of these forces by combining the communicative modelling techniques presented in this article with the opportunistic collaboration and negotiation techniques presented in Naeve (2005; 2010) and apply this combination of modelling techniques to global problems where increased transparency and accountability are necessary in order to provide sustainable solutions.
38
However, the complexity of drawing the interaction between these models has prevented me from trying to integrate
them in the same diagram.
39
As discussed in Naeve (2011, 4), context (= ‘where’), purpose (= ‘why’), and target group (= ‘for whom’) are fundamentally important in order to determine the appropriate scope and structure of a communicative model.
40
As mentioned at the end of section 4.5 (p.17), such a model is presented in Naeve (2011), section 14.4. Its connections to the models presented in this article will be the subject of a future article.
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References
Argyris, Chris, and Donald Schön. 1978. Organizational Learning: A Theory of Action Perspective. Reading, Massachusetts:
Addison-Wesley.
Bateson, Gregory. 1972. Steps to an Ecology of Mind: Collected Essays in Anthropology and Psychiatry. San Francisco:
Chandler.
Bateson, Gregory.1978. The Pattern Which Connects: Introduction to Mind and Nature: A Necessary Unity. New York:
Bantam Books. Accessed June 27, 2012. http://www.oikos.org/mind&nature.htm
Bohm, David. 1980. Wholeness and the Implicate Order. London: Routledge & Kegan Paul.
Goleman, Daniel. 2007. Pribram – the Magellan of Brain Science, Interview with Karl Pribram. Psychology Today. Accessed
June 27, 2012. http://www.sybervision.com/Golf/hologram.htm#memory.
Edersheim, Elisabeth Hass. 2007. The Definitive Drucker. New York: McGraw-Hill.
Hofkirchner, Wolfgang. 2009. A Unified Theory of Information – an Outline. Accessed June 27, 2012.
http://bitrumagora.files.wordpress.com/2010/02/uti-hofkirchner.pdf
Hofkirchner, Wolfgang. 2010. Twenty Questions About a Unified Theory of Information – A Short Exploration into Information from a Complex Systems View. Litchfield Park: Emergent Publications.
Kim, Daniel. 2004. The Link between Individual and Organizational Learning. In How Organizations Learn – Managing the
Search for Knowledge, 2nd edition, edited by Ken Starkey, Sue Tempest, and Alan McKinlay, 29-50. London: Thomson
Learning.
Laurillard, Diana. 2002. Rethinking Teaching for the Knowledge Society. EDUCAUSE Review 37(1): 16-25.
Lefrère, Paul. 2011. Using Information (and Exformation) to Inform Action. In Perspectives on Information, edited by Magnus Ramage and David Chapman, 77-90. New York: Routledge.
Lonner, Walter. 1994. Culture and Human Diversity. In Human Diversity: Perspectives on People in Context, edited by
Edison Trickett, Roderick Watts and Dina Birman, 230-243. San Francisco: Jossey-Bass.
Naeve, Ambjörn. 1997. The Garden of Knowledge as a Knowledge Manifold – A Conceptual Framework for Computer
Supported Subjective Education. Internal report CID-17, TRITA-NA-D9708, Department of Numerical Analysis and
Computer Science, KTH, Stockholm. Accessed June 27, 2012.
http://kmr.nada.kth.se/papers/KnowledgeManifolds/cid_17.pdf
Naeve, Ambjörn. 2005. The Human Semantic Web – Shifting from Knowledge Push to Knowledge Pull. International Journal of Semantic Web and Information Systems (IJSWIS) 1(3): 1-30. Accessed June 27, 2012.
http://kmr.nada.kth.se/papers/SemanticWeb/HSW.pdf
Naeve, Ambjörn. 2009. Disagreement Management as a Way to Increase the Organizational Performance of Humanity Inc.
Invited talk at the TEN-Competence Winter School in Innsbruck, February 2, 2009. Accessed June 27, 2012.
http://www.slideshare.net/EagleBear/ambjrn-on-disagreement-managment-988233
Naeve, Ambjörn. 2010. Opportunistic (L)earning in the Mobile Knowledge Society. International Journal of Mobile and
Blended Learning (IJMBL) 2(4): 29-46.
Naeve, Ambjörn. 2011. A Modeling Primary on Communicative Modeling and Disagreement Management. TEL-Map Deliverable D1.1, January 2011. Accessed June 27, 2012. http://telmap.confolio.org/scam/4/resource/839
Naeve, Ambjörn, Pertti Yli-Luoma, Milos Kravcik, Miltiadis Lytras, Bernd Simon, Mia Lindegren, Mikael Nilsson, Matthias
Palmér, Nikos Korfiatis, Fridolin Wild, Richard Wessblad, Vana Kamtsiou, Dimitra Pappa, and Barbara Kieslinger. 2005.
A Conceptual Modelling Approach to Studying the Learning Process with a Special Focus on Knowledge Creation. Deliverable 5.3 of the PROLEARN EU/FP6 Network of Excellence, IST 507310, June 2005. Accessed June 27, 2012.
http://kmr.nada.kth.se/papers/SemanticWeb/ProlearnD5.3.pdf
Naeve, Ambjörn, Andreas Kaibel, Volker Zimmermann, Daniel Burgos, Miltiadis Lytras, Miguel-Angel Sicilia, Paul Lefrère,
Milos Kravcik, Muhamed Amine Chatti, Pertti Yli-Luoma, Fridolin Wild, Matthias Palmér, Mikael Nilsson, Hannes Ebner,
and Fredrik Enoksson. 2007. A SECI-based Framework for Professional Learning Processes. PROLEARN Deliverable
D1.10, July 2007.
Naeve, Ambjörn, Pertti Yli-Luoma, Milos Kravcik, and Miltiadis Lytras. 2008a. A Modeling Approach to Studying the Learning Process with a Special Focus on Knowledge Creation. International Journal of Technology Enhanced Learning
(IJTEL), 1(1/2): 1-34.
Naeve, Ambjörn, Miguel-Angel Sicilia, and Miltiadis Lytras. 2008b. Learning Processes and Processing Learning: From
Organizational Needs to Learning Designs. Journal of Knowledge Management 12(6): 5-14.
Nonaka, Ikujiro. 1994. A Dynamic Theory of Organizational Knowledge Creation. Organization Science 5(1): 14-37.
Nonaka, Ikujiro and Hirotaka Takeuchi. 1995. The Knowledge-Creating Company: How Japanese Companies Create the
Dynamics of Innovation. New York: Oxford University Press.
Nonaka, Ikujiro., Ryoko Toyama, and Noboru Konno. 2000. SECI, Ba and Leadership: A Unified Model of Dynamic
Knowledge Creation. Long Range Planning 33(1): 5-34.
Nørretranders, Tor. 1991. Märk Världen. Stockholm: Bonnier Alba.
Pask, Gordon. 1976. Conversation Theory: Applications in Education and Epistemology. Amsterdam: Elsevier.
Pask, Gordon. 1996. Heinz von Foerster's Self Organization, the Progenitor of Conversation and Interaction Theories.
Systems Research 13(3): 349-362.
Polanyi, Michael. 1967. The Tacit Dimension. New York: Anchor Books.
Praslova, Ludmila. 2006. Culture as Unfolding Process: Integrating Perspectives in Building a Theory. Published online.
Accessed June 27, 2012. http://tinyurl.com/bodygjs
Pribram, Karl. 2007 (1987). Holonomic Brain Theory. Scholarpedia 2(5):2735. Accessed June 27, 2012.
http://www.scholarpedia.org/article/Holonomic_Brain_Theory
Rocha, Luis.1998. Selected Self-Organization and the Semiotics of Evolutionary Systems. In Evolutionary Systems: Biological and Epistemological Perspectives on Selection and Self-Organization, edited by Gertrudis Van de Vijver, Stanley
Salthe, and Manuela Delpos, 341-358. Dordrect, The Netherlands: Kluwer Academic Publishers.
Rumbaugh, James, Ivar Jacobsson, and Grady Booch. 1999. The Unified Modeling Language Reference Manual. Reading,
Mass.: Addison Wesley.
Schrödinger, Erwin. 1951. Science and Humanism. Cambridge: Cambridge University Press.
CC: Creative Commons License, 2013.
tripleC 11(1): 46-66, 2013
66
Senge, Peter. 2006. The Fifth Discipline – The Art & Practice of the Learning Organization. 2nd edition. New York: Doubleday.
Shannon, Claude. 1948. A Mathematical Theory of Communication. Bell System Technical Journal 27(3): 379-423, 623656.
von Foerster, Heinz. 1965. Memory Without Record. In Anatomy of Memory, edited by D.P. Kimble, 388-433. Palo Alto:
Science and Behavior Books.
von Foerster, Heinz. 1969. What is Memory That It May Have Hindsight and Foresight as Well? In The Future of The Brain
Sciences, edited by Samuel Bogoch, 19-65 and 89-95. New York: Plenum Press.
von Foerster, Heinz. [1977] 2003. Objects: Tokens for (Eigen-)Behaviors. In Understanding Understanding, Heinz von
Foerster, 261-272. New York: Springer.
Wilber, Ken, ed. 1982. The Holographic Paradigm and Other Paradoxes: Exploring the Leading Edge of Science. London:
Shambala Publications.
Yli-Luoma, Pertti, and Ambjörn Naeve. 2006. Towards a Semantic E-learning Theory by Using a Modeling Approach. British
Journal of Educational Technology 37(3): 445-459.
About the Author
Ambjörn Naeve
has a background in mathematics and computer science, and in 1993 he received his doctorate from KTH within the field of
computer vision. Today he mainly works as a researcher within the field of networked and personalizable learning environments. In this capacity he heads the Knowledge Management Research group, which he founded in 2001, and which is
based at the school of Computer Science and Communication at KTH and at Uppsala Learning Lab at Uppsala University.
Ambjörn Naeve has contributed widely within the fields of Knowledge Management, Technology Enhanced Learning and
Semantic Web. His main contributions have taken the form of articles for international research conferences and journals,
book-chapters, co-edited books, and thematic (special) issues of international research journals. Moreover, Ambjörn Naeve
is the editor-in-chief of the International Journal of Technology Enhanced Learning (IJTEL). During later years, the KMRgroup has specialized in methods and tools for communicative modelling, disagreement management and unplanned (adhoc) collaboration. This work has mainly been financed by various EU projects. The most recent among these projects are
H-net, which is building a professional knowledge network for lifelong and personalizable learning within the European
hematological community of practice, ROLE, which is concerned with configuring and supporting personalizable and responsive learning environments, and TEL-Map, which is creating a dynamic roadmapping platform to support collaborative
future-gazing and awareness-building for Technology Enhanced Learning in Europe and beyond.
CC: Creative Commons License, 2013.
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