Exploring the foundations of organizational knowledge

Abstract

If knowledge management is to be more than an art, it needs to be based on a sound epistemology and understanding of organizations. We present a paradigm and an ontology of organizational knowledge based on Karl Popper’s 1972 and later works on evolutionary epistemology, Maturana and Varela’s concept of living things as self-producing complex systems ('autopoiesis'), and theories of hierarchically complex systems. This approach to ontology development leads us to conclude that organizations can become living systems and thus have emergent properties of a higher order than the sum of the parts. We develop this theoretical argument by providing examples of how several different types of knowledge created by people within organizations emerge and change through time. We suggest the social processes of creating these different types of knowledge gives rise to meta-levels of organization that act to maintain the existence and coherence of organizations. We think that our ontology improves the basis for understanding the nature of knowledge that is important for proper organizational functioning. We draw out recommendations about the management of transformations between personal and organizational knowledge. We propose this biological understanding of knowledge in organizations because as practitioners, we think it provides a way of interpreting the dynamics of what actually happens in the realm of managing organizational knowledge. Thus, we lay a foundation for better understanding the considerable challenges associated with developing a practical approach to organizational knowledge management as a result.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1978316

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Working Paper No. 3


Exploring the Foundations of Organizational Knowledge

Richard Vines
Knowledge Management Specialist
Hon Fellow: eScholarship Research Centre, University of Melbourne

William P. Hall
President, Kororoit Institute Proponents and Supporters Association, Inc;
Engineering Learning Unit, University of Melbourne, Victoria, Australia


































© 2011 – Richard Vines & William P Hall
Kororit Institute Working Paper No. 3 - 12/30/2011
ISSN 1839-8855
http://kororoit.org

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Table of Contents
Introduction and Scope.....................................................................................................1
Biological Basis for ORGANIZATIONAL Epistemology ..............................................4
What is knowledge?................................................................................................................................ 4
Karl Popper’s three worlds and evolutionary theory of knowledge........................................................ 5
Emergence of life in complex adaptive systems..................................................................................... 8
Complexity......................................................................................................................................... 8
Adaptation.......................................................................................................................................... 8
Defining life as autopoiesis................................................................................................................ 9
Autopoiesis, life and knowledge...................................................................................................... 10
Living systems in a complex hierarchy............................................................................................ 11
Emergence of autopoiesis at levels of complexity above the cellular.............................................. 12
Knowledge and autopoiesis: Individual knowledge vs organizational knowledge............................... 15
Perspectives of knowledge in Living ORGANIZATIONS............................................15
Autopoietic organizations and organisational knowledge..............................................18
Organizational knowledge begins with “living knowledge”................................................................. 18
Organizational boundaries are semipermeable...................................................................................... 20
The dynamics of ORGANIZATIONAL knowledge......................................................20
Exchange and growth of personal knowledge in the organization........................................................ 21
Expressing personal knowledge as explicit knowledge. ....................................................................... 21
Turning explicit knowledge into common knowledge.......................................................................... 22
Critiquing of common knowledge to create formal knowledge............................................................ 22
Integrating formal knowledge transfer through the deployment of support systems............................ 23
Discussion and Conclusions...........................................................................................24
Acknowledgements.........................................................................................................27
References.......................................................................................................................28


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Exploring the Foundations of Organizational Knowledge1
Abstract
If knowledge management is to be more than an art, it needs to be based on a sound epistemology and
understanding of organizations. We present a paradigm and an ontology of organizational knowledge based on
Karl Popper’s 1972 and later works on evolutionary epistemology, Maturana and Varela’s concept of living
things as self-producing complex systems (“autopoiesis”), and theories of hierarchically complex systems. This
approach to ontology development leads us to conclude that organizations can become living systems and thus
have emergent properties of a higher order than the sum of the parts. We develop this theoretical argument by
providing examples of how several different types of knowledge created by people within organizations emerge
and change through time. We suggest the social processes of creating these different types of knowledge gives
rise to meta-levels of organization that act to maintain the existence and coherence of organizations. We think
that our ontology improves the basis for understanding the nature of knowledge that is important for proper
organizational functioning. We draw out recommendations about the management of transformations between
personal and organizational knowledge. We propose this biological understanding of knowledge in
organizations because as practitioners, we think it provides a way of interpreting the dynamics of what actually
happens in the realm of managing organizational knowledge. Thus, we lay a foundation for better understanding
the considerable challenges associated with developing a practical approach to organizational knowledge
management as a result.
Keywords: Knowledge Management, Evolutionary Epistemology, Knowledge Ontology, Organization Theory,
Autopoiesis, OODA Loop
The endless cycle of idea and action,
Endless invention, endless experiment,
Brings knowledge of motion, but not of stillness;
Knowledge of speech, but not of silence;
Knowledge of words, and ignorance of the Word.
All our knowledge brings us nearer to our ignorance,
All our ignorance brings us nearer to death,
….
Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?
[T.S. Eliot - Choruses from the Rock, Faber & Faber, London, 1934]
INTRODUCTION AND SCOPE
As recognized by Baskerville and Meyers (2002), Katerattanakul et al. (2006), Gregor
2006, and Furneaux et al. (2007) Information Systems (IS) is an applied discipline referencing
theories from a variety of other disciplines. But it also aspires to serve as a reference point in
relation to other disciplines concerned with understanding the roles and management of
information and knowledge in organizations. Gill and Bhattercherjee (2009) suggest that IS
has not yet achieved sufficient status as an “informing” discipline to give its practitioners
reasonable security in the academic hierarchy. Following Kuhn (1962, 1983), we think that
the IS discipline is still pre-paradigmatic, with little foundation theory of its own that draws
upon explanatory theories from a variety of “reference” disciplines. An apt historical
comparison for IS was the state of natural history in the 19th Century before Darwin’s Origin

1 This paper is derived from and extends ideas first presented by Richard Vines, Luke Naismith and William P.
Hall in a conference paper, “Exploring the foundations of organisational knowledge: An emergent synthesis
grounded in thinking related to evolutionary biology”, presented in the actKM Conference, Australian
National University, Canberra, 23-24 October 2007.
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of Species and the early 20th Century rediscovery of Mendelian genetics that provided the
foundation theories for what became today’s biological sciences.
Significant indicators of IS’s pre-paradigmatic status are the ambiguities, confusions
and debates over such basic concepts of “information” and “knowledge” in relation to
management (Hildreth and Kimble 2002; Stenmark 2002; Wilson 2002; Miller 2002; Bates
2005; Land 2009). More specifically, a recent review focusing on knowledge management’s
“foundation” theories (Baskerville and Dulipovici 2006), clearly illustrates the ad hoc nature
and lack of coherence of the diversity of theories (many sourced from other disciplines)
claiming to explain aspects of the discipline. There are so many theories that a taxonomy is
needed to categorize them and a matrix (with many blank spaces) is required to indicate how
they cite and reference each other. Baskerville and Dulipovici do see some signs of
“cohesion” and development of “overarching theories” such as “knowledge strategy”,
“knowledge creation”, and “knowledge transfer/reuse”, but these still only explain aspects of
the dynamics of organizational knowledge. A proper “foundation” theory should provide a
single coherent framework for the whole discipline.
A major source of the uncertainty over what information or knowledge management
entails may be because these disciplines are largely based on paradigms sourced from the
social sciences (McKelvey 1997, 2002, 2002a; 2003). What is often not considered when
looking at organizations and knowledge from the purely sociological point of view is that the
increasing use of various tools and production technologies to extend human physical
capacities caused many changes in organizations and organizational governance, such that it
is useful to consider “socio-technical” organizations comprised of people plus their machines
and technologically mediated processes (Harvey 1968). Over the last 30 years, in addition to
the ways humans organize to produce physical products, use of tools such as personal
computers and the internet that act to extend human cognition have even more radically
revolutionized the way people interact in organizations (Hall 2006b; Yakhlef 2008). People in
today’s socio-technical organizations are cognitively knitted together with a wide variety of
technologies (e.g., Hall 2006b; Hall et al. 2008a; 2010; Hall and Kilpatrick 2011; Nousala et
al. 2011) that support distributed decision-making processes extending beyond the mental
bounds of human bodies. The result is what Pepperell (1995), Hayles (1999) and Yakhlef
(2008) consider to be a “post-human”2 condition where humans as organisms and their
technologies essentially become inseparable. Paradigms from the traditional social sciences
do not encompass or adequately illuminate this post-human complexity (Yakhlef 2008).
We suggest considering these matters from a new point of view. McKelvey (1997) calls
for the development of a “quasi-natural” organization science. Gregor (2009) suggests this
should be constructed building on Simon’s (1996) sciences of the artificial as informed by
traditions of the philosophy of science. Following Gregor our central claim is that
organizations need to be understood as complex socio-technical systems exhibiting emergent
properties that evolve spontaneously in unanticipated directions (Hayles 1999). Going beyond
Gregor, we believe a new “biological” paradigm of organization based on Karl Popper’s
(1972) evolutionary epistemology, Maturana and Varela’s (1980) concept of autopoiesis, and
the theory of hierarchically complex systems (Simon 1962, 1973, 2002; Pattee 1973, 2000;
2007a; Salthe 1985, 1993, 2004; Hall 2011) provides the conceptual framework for
understanding the roles and growth of knowledge in socio-technical organizations. These
ideas at the core of our approach have long histories on the fringes of their respective
disciplines. However, the synergistic synthesis we present here is new and provides a
coherent foundation theory for information systems and a variety of other disciplines

2 In our use of the term “post-human” we do not imply any deep philosophical implications, but only the literal
fact that aspects of human cognition are extended, distributed, and may even be shared beyond the physical
limits of human bodies.
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concerned with the acquisition, development and management of knowledge in complex
organized systems.
Evolutionary epistemology was established in the cognitive sciences by Campbell
(1960, 1974, 1991) and in philosophy by Popper (1935, 1963, 1972, 1994).
The concept of autopoiesis (“self” + “production”) as a set of criteria for recognizing
when a complex adaptive system would be considered to be living was developed in the
1970s by the neurobiologists Maturana and Varela (Varela et al. 1974; Maturana 1980, 2002;
Maturana and Varela 1980; Varela 1980) and is being refined and restated by Maturana’s
student Hugo Urrestarazu (2004, 2011) and Hall (2006a, 2011).
The theory of hierarchically complex systems was developed progressively from several
threads, respectively from viewpoints in organization theory (Simon 1962, 1973, 2002),
philosophy (Koestler 1967, 1978), biophysics (Pattee 1973, 2000), and evolutionary biology
(Salthe 1985, 1993, 2004).
Each of these ideas independently has had some recognition but only minor take-up in
organization theory or in the information and knowledge management disciplines, e.g,
Popper’s work via Firestone and McElroy (2002, 2003, 2003a, 2005); autopoiesis via
Luhmann (1986, 1990, 1995), von Krogh and Roos (1995; von Krogh 1998) and Magalhäes
(1998; Magalhäes and Sanchez 2009); and hierarchy theory via Simon (1996). However,
despite their completely independent and very different paradigmatic lineages, these three sets
of theory are mutually supportive, and as we review here, the resulting synthesis (Hall 2003,
2005, 2006a, 2011; Hall et al. 2011) offers a unified theory that provides a foundational
framework for understanding organizational processes and systems for building and managing
information and knowledge.
Thus, the purpose of this paper is to outline this biological paradigm and to develop and
elucidate a theory-based ontology of organizational knowledge within this framework. We do
not defend the biological paradigm itself – this is done elsewhere (Hall 2003, 2005, 2006a,
2006a, 2011). At this point in its development the paradigm is still somewhat speculative, but
despite this Vines has agreed to collaborate with Hall for the purposes of this paper. (The
authors discuss their differences in Appendix 1.) We both think that this abductive type
speculation properly allows for a useful exploration of a foundation and framework for
understanding the emergence and growth of socio-technical organizations and the flows of
information and knowledge associated with them. We explore relationships and differences
between personal knowledge and knowledge pertaining to whole organizations and some of
the ways in which knowledge serves to bind humans together to form a higher order
organizational entity exiting in its own right. We think some of these higher order entities
exhibit many of the emergent properties that provide a basis for the claim that these
organizations could be considered to living “biological” entities in their own right.
Although the present work is largely theoretical, it derives from our practical
experiences as information and knowledge managers. Based on their consulting experiences,
Vines and Naismith (2002) adapted the concept of the Knowledge Life Cycle (Firestone
1999b; McElroy 1999, 2002; Firestone and McElroy 2002) and concluded that knowledge
management is an essential component in maintaining organizational viability and growth.
Working in parallel, Hall – who was an evolutionary biologist before moving into industry –
and associates from diverse backgrounds working in engineering knowledge management
environments, developed a “biological” concept of organizations (Hall 2003, 2003a, 2005,
2006a, 2011; Dalmaris 2006; Dalmaris et al. 2006; 2007; Hall et al. 2005, 2007, 2008, 2010,
2011; Hall and Nousala 2007, 2010; Martin et al. 2009; Nousala 2006; Nousala et al 2005;
2007, 2009, 2011; Nousala and Hall 2008). This multidisciplinary framework provides a
theoretical foundation for the Firestone & McElroy and Vines & Naismith (loc. cit.) concepts
about “knowledge life cycles” and “business knowledge support systems”.
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Unlike descriptive ontologies developed from practitioner and researcher interviews
(Earl 2001; Holsapple and Joshi 2004), the theory-derived ontology developed here derives
from three premises about knowledge, life and organizations: (1) All knowledge is
constructed by living entities. (2) For an autonomous organization to live, knowledge
necessary for the maintenance of its life must be embodied in the dynamic processes and
structures comprising the entity. (3) Entities with the necessary properties to be considered
living can be distinguished at different scalar levels of organization e.g., living cells,
multicellular organisms including individual humans, superorganisms (ant and bee colonies,
etc.), nation-states, and for our purposes here at least some economic and social organizations
(Hall 2006a, 2011). The basis for these premises is detailed in the works by Hall and his
associates cited above and are the subject of ongoing work outside the scope of the present
paper.
We accept that many organized systems that involve people in their dynamic structures
do not meet all of the necessary criteria to be considered living (i.e., autopoietic). Also, there
is a very fuzzy borderline between complicated organized systems of people on one hand, and
on the other, complex adaptive organizations that in some cases exhibit all the necessary
properties to be considered “living” entities. As we will show, knowledge, both at the
individual level and as embodied in the higher level organization is the glue that binds the
components of complex systems into living entities. To be able to analyze various
organizational forms, the formation and use of knowledge in them, and their relationships to
other organizations involving humans, we need a vocabulary that includes the possibility that
some organizations are living. Thus, our focus here is to develop a theoretical ontology based
on the above premises for discussing cognitive processes in individual humans and processes
of organizational cognition within which people interact to construct and maintain the
structures and processes that give life to that organization, both with one another and with
technological components forming the living organization. As we will highlight, we draw
upon the theory of autopoiesis (Maturana and Varela 1980, Maturana 2002, Varela et al 1974)
as a necessary definition for what it means for a socio-technical organization to be considered
living entity.
BIOLOGICAL BASIS FOR ORGANIZATIONAL EPISTEMOLOGY
What is knowledge?
In knowledge management there are almost as many definitions of knowledge as there
are practitioners, to say nothing of arguments about the meanings of and relationships
between data, information and knowledge (e.g., Stenmark 2002, Land 2009). In the pragmatic
world of organizational management, “knowledge” supporting organizational decisions needs
to represent the world as it exists, not as people might want to believe it is. Critical scientific
realism (Niiniluoto 2002) is a philosophical stance accepting that our knowledge of the world
is constructed internally but that there is also an external reality that we strive to understand
by criticizing the gaps between our claims about the world and what actually happens. In this
framework, knowledge is considered to be “true” if it corresponds to reality. Critical scientific
realism and several other different philosophical paradigms for determining truth in the
context of knowledge management are discussed by Mingers (2008). An important thread
deriving from critical scientific realism not mentioned by Mingers is evolutionary
epistemology, as espoused by Donald Campbell, Konrad Lorenz, and Karl Popper3. This is

3 Although Mingers cites Popper’s early work on “critical rationalism” (Popper 1935, 1963), he makes no
reference to Popper’s later thinking that Firestone and McElroy (e.g., 2002, 2003, 2003a, 2005), Blackman et
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the epistemological basis for our work here. Donald T. Campbell (1974), first coined the term
“evolutionary epistemology” for application in the social and cognitive sciences. However,
Campbell credits Popper with originating evolutionary epistemology and with expressing its
fundamental perspective in Logik der Forschung (1935). Bartley (1976) discusses the role
Konrad Lorenz had in the genesis of evolutionary epistemology. Both Campbell (1960, 1991)
and Popper argued that knowledge is constructed in living things as they adapt to the world.
Popper argued that no objective truth could be proved - only that certain claims could be
shown to be in error through tests or criticisms of the claims as they impact reality (Popper
1935 [1959], 1963). From this, as will be detailed below, Popper argued that claims to know
are cognitively constructed and could be tested only through their successes and failures in
responding to external reality, i.e., as solutions to problems of life. In other words, the truth of
any claim’s correspondence with an external reality can never be known with certainty.
However, such constructed claims may stochastically approach a correspondence with reality
through repeated criticism, testing and the elimination of errors.
Here we adopt Popper’s (1972) broad concept deriving from evolutionary epistemology
that “knowledge is solutions to problems” – or at least claims towards solutions. More
technically, we use the term “knowledge” very generically for “control information” (Corning
2001) a cell, organism or organization uses to cybernetically control the maintenance of its
existence and responses to its environment (Hall 2006a). As we develop in later sections of
the present work, This broad definition of knowledge encompasses the more specific ideas of
“data”, “information”, “knowledge”, “intelligence”, and “wisdom” (Ackoff 1989; Hall 2003,
Rowley 2007), although the more specific terms are useful in particular contexts. For Popper
(1972), human knowledge grows through iterated interactions of three ontological domains or
“worlds”.
Karl Popper’s three worlds and evolutionary theory of knowledge
To suit our discussions of knowledge deriving from Popper (1972, 1978, 1994) across
different orders of complexity, our ontology development begins with defining three
ontological domains or “worlds”.
• “World 1” (W1)4 includes everything physical without interpretation5.
• “World 2” (W2) is the world of cybernetics, cognition and “living” knowledge in the
broad sense6. Popper includes “subjective knowledge” (i.e., the subject’s personal
knowledge), or “dispositional knowledge” (i.e., the subject’s structurally determined
propensity or disposition to act in a certain ways in particular circumstances) in W2,
which approximates Polanyi’s (1958, 1966) personal or ‘tacit’ knowledge.

al. (2004), Blackman and Henderson (2007), Capurro (2004) and Hall (loc. cit.) have applied to knowledge
management.
4 Our use of the abbreviated terms W1, W2, and W3 is a reminder that the concepts we use in this work are
defined somewhat differently from Popper’s worlds 1, 2, and 3.
5 Vines notes that in his Tanner Lecture, Popper (1978) stated: “we can subdivide the physical world 1 into the
world of non-living physical objects and into the world of living things, of biological objects; though the
distinction is not sharp”.
6 Vines also notes that in the same Tanner Lecture Popper (ibid) suggested that world 2 can also be subdivided
in various ways: “We can distinguish, if we wish, fully conscious experiences from dreams, or from
subconscious experiences. Or we can distinguish human consciousness from animal consciousness”.
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• “World 3” (W3) includes all kinds of persistently encoded7 knowledge (e.g., hereditary
information in DNA, written documents, electronically encoded information etc. –
Popper 1972: pp. 73-74). Codified knowledge is “objective” because its logical content
can exist and persist in W3 logically encoded in the physical structure of a W1 container
(e.g., as marks on paper, sequences of binary bits in a computer memory, etc.)
independent from the “knowing subject”, and can be decoded in W2 with similar
subjective meanings by different subjects.
It is arguable that our biological point of view shades these concepts in ways Popper did
not intend (Firestone, pers. comm.). However, the distinctions between the three worlds as
made here8 provide the basis for the coherent development of additional concepts.
A fundamental question is, How does knowledge emerge? We think evolutionary
epistemologies have something important to say about this question. Popper argued that no
objective truth could be proved - only that certain claims could be shown to be in error
through tests or criticisms of the claims as they impact reality (Popper 1935 [1959], 1963).
However, a theory referring to W1 can be constructed and shared verbally by entities in W2
and (optionally) be expressed or “codified” in the form of persistent W3 content. Through
iterated testing and intersubjective criticism to eliminate errors, what is asserted in W2 or W3
can approach correspondence with W1’s reality, as Popper (1972) explains in his “tetradic
schema” or “general theory of evolution” (Figure 1).
Pn is a “problem situation” the living entity faces in the world, TSm represent a range of
“tentative solutions” or “tentative theories” the entity may act on or propose. TSs may even
be randomly generated (cf. Campbell’s (1960) “blind variation”). EE (“error elimination”)
represents a process by which TSs are tested or criticized against the world to selectively
remove solutions or claims that don’t work in practice (this is the converse to Campbell’s
“selective retention”. Pn+1 represents the now changed problem situation remaining after a
solution has been incorporated. As the entity iterates and re-iterates the process (the arrow
indicating iteration is added by Hall), it will construct increasingly accurate representations of
and responses to external reality, even where there is no possibility for knowledge to directly
“reflect” external reality. Thus, the value of knowledge is determined in practice by the extent
to which workable solutions to pressing problems are constructed or identified and exploited
(Dalmaris et al 2006; Hall et al 2007, 2011).

7 Popper (1978) said of world 3 objects: “Of most though not of all world 3 objects it can be said that they are
embodied, or physically realized, in one, or in many, world 1 physical objects”. In our explanation of World
3 we have chosen to use the term encoded rather than embodied. We do this because for the purposes of this
paper we want to emphasise the relationship between people and world three objects. We contend that
human intelligence is used in the processes of encoding (writing and authoring) and decoding (apprehending
via activities such as reading) W3 content. We contend this matter has significant implications for the design
of socio-technical organisations and the relationships between people and machines / computers in such
organisations. We also note that embodied knowledge is reflected in the dynamical structure of the living
entity, while encoded knowledge is impressed onto the structure of carrier structures in the form of energy
degenerate states that are physiologically comparatively inert (Pattee 2005, 2006, 2008).
8 Hall observes that although Popper in his later books appeared to have a biological approach to his
development of a theory of knowledge, Popper did not have a scientifically comprehensive understanding of
evolution or the cognitive sciences. Bartley (1976) notes that Popper and Konrad Lorenz, the Nobel laureate
ethologist, were boyhood friends and both students of the philosopher-psychologist Karl Bühler. Popper
described his worlds in philosophical terms, while Hall (2006a and 2011) has reformulated them in physical
and biological terms. Hall’s distinctions separate the intangible cybernetic phenomena of life (W2) from the
raw physical dynamics of moving particles (W1) – for which see the discussion beginning on the next page
of Pattee’s concept of the epistemic cut; and the dynamic structure of life in W2 from the inert but potentially
decodable imprint of knowledge in W3, that is also separated from W2 by an epistemic cut. Popper (1973)
considered that articulated language as thought or speech belonged in W3. For reasons discussed in footnote
11, Hall limits W3 to persistently encoded/imprinted knowledge.
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TS1
TS2
•
•
•
•
•
TSm
TSm
TSm
TSm
TSm
TSm
TSm
Pn
Pn
Pn
Pn
Pn
Pn
Pn
Pn+1
Pn+1
Pn+1
Pn+1
Pn+1
Pn+1
Pn+1
EE EEEEEEEE EEEE
TS1
TS2
•
•
•
•
••••••
TS1
TS2
•
•
•
•••••
TS1
TS2
•
•
••••
TS1
TS2
•
•••
TS1
TS2
••
TS1
TS2


Figure 1. Popper’s “general theory of evolution” (From Hall 2005, after Popper 1972: pp.
243).
Heritable knowledge may be constructed over evolutionary time via Darwinian natural
selection, i.e., by random variation in heritable “solutions”, the selective elimination of
variants that fail to solve their problems of life, and the hereditary transmission solutions that
have proved to be viable. Where solutions are represented in individual cognition, through
continuously iterated cycles of problem solving (i.e., through testing tentative solutions and
eliminating those that fail), the entity constructs increasingly accurate knowledge about the
world it is living in. These interconnected ideas formed the basis of Popper’s (1972) “general
theory of evolution” and “growth of knowledge” that takes place in living entities (Figure 1).
The concept that Howard Pattee (1995, 2000, 2001, 2006, 2007; 2008) called the
epistemic cut9 provides an account of the biophysical basis for Popper’s three worlds or
domains. This has also been called the “Heisenberg cut” (Graben & Atmanspacher 2009), that
relates to Wolfgang Pauli’s (1950, 1952) principle of complementarity. The epistemic cut
should not be confused with the “epistemic gap” separating “phenomenological knowledge”
from “physical knowledge” (Alter & Walter 2006; Chalmers 2006). Not only are the
paradigms surrounding the “cut” and the “gap” quite different, but “epistemic gap” relates to
forms of human consciousness, not fundamental aspects of living things. Pattee’s “cut” relates
to the ontological difference between uninterpreted physical reality on one side and
information about that reality on the other side, i.e., the cut is between physical reality and
knowledge of the physics.
Pattee argues that there is a strict ontological separation (in physical and philosophical
senses) between
…knowledge of reality from reality itself, e.g., description from construction, simulation
from realization, mind from brain [or cognition from physical system]. Selective
evolution began with a description-construction cut.... The highly evolved cognitive
epistemology of physics requires an epistemic cut between reversible dynamic laws and
the irreversible process of measuring initial conditions…. [Our italics, Pattee 1995: p.
23].
What “epistemic cut” means depends on Pattee’s definitions of information and
knowledge:
Knowledge is potentially useful information about something. Information is
commonly represented by symbols. Symbols stand for or are about what is represented.
Knowledge may be about what we call reality, or it may be about other knowledge. It is
the implementation of “standing for” and “about” - the process of executing the epistemic
cut - that [we need] to explore.

9 To our knowledge Pattee never addressed Popper’s ideas or evolutionary epistemology directly.
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Heritable, communicable, or objective knowledge requires an epistemic cut to
distinguish the knowledge from what the knowledge is about. By useful information or
knowledge I mean information in the evolutionary sense of information for construction
and control, measured or selected information, or information ultimately necessary for
survival. [my emphasis, his italics]. …
The requirement for heritable or objective knowledge is the [epistemic] separation of
the subject from the object, the description from the construction, the knower from the
known. Hereditary information originated with life with the separation of description and
construction.... [Pattee’s italics, our bold - Pattee 1995: p. 26]
Following Pattee, there is a clear separation from the dynamics of living things in W1
and the codified knowledge of life in W3, but what he does not see clearly and admits he
cannot understand is how the genetic code (W3) can emerge from the world of physical
dynamics (W1). What is missing from Pattee’s ontology of two domains and one cut is
knowledge embodied in the structural dynamics of autopoietic systems.
As discussed extensively by Hall (2011), natural selection favors the autopoietic
stabilization of dissipative structures. The first kind of knowledge to emerge within an
energetically dissipative system that may be evolving towards autopoiesis is structure that is
organized in such a way that dynamic feedback loops work to maintain that structure (i.e., by
solving “problems” caused by perturbations that might otherwise cause the system to
disintegrate). Corning (2001: p. 1277) called this structural “knowledge” control information:
“the capacity (know how) to control the acquisition, disposition and utilization of
matter/energy in purposive (teleonomic) processes.” To us this is the system’s structurally
determined propensity or disposition to act in a certain way in particular circumstances,
falling into W2. The epistemic gap in this definition between the physical world and
knowledge of that world is clear. Hoffmeyer and Emmeche (1991; Emmeche and Hoffmeyer
1991; Hoffmeyer 2000, 2002), citing Pattee’s epistemic cut, recognize two kinds of
knowledge: “analog” embodied in dynamic structure that we consider to be “dispositional” in
W2; and “digital” or symbolic knowledge that clearly fits into W3.
Thus, as argued much more extensively in Hall (2011), we think there is a clearly
rational physical basis for Popper’s three worlds ontology that we will show can be applied to
all kinds of complex knowledge-based organized systems.
Emergence of life in complex adaptive systems
Complexity
To us both people and organizations are “complex adaptive systems” at different scales
or levels of organization, where each of the terms in this phrase implies aspects that may be
difficult for some to grasp. A “system” is a set of causally connected components as
determined either intrinsically by their degree of connectedness or as discriminated by an
external observer. Following Simon (1962: p. 468), a “complex system” is “one made up of a
large number of parts that interact in a non-simple way. In such systems, the whole is more
than the sum of the parts, not in an ultimate, metaphysical sense, but in the important
pragmatic sense that, given the properties of the parts and the laws of their interaction, it is
not a trivial matter to infer the properties of the whole.” By contrast to complicated systems,
which simply have large numbers of parts that interact in predictable ways, complexity
implies the interactions of the parts include aspects of nonlinearity and/or indeterminacy that
make it impossible to make long-term predictions about the behaviour of the system.
Adaptation
We also follow Simon’s (1962: p. 479) definition of “adaptation”:
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The distinction between the world as sensed and the world as acted upon defines the basic
condition for the survival of adaptive organisms. The organism must develop correlations
between goals in the sensed world and actions in the world of process. … Given a desired
state of affairs and an existing state of affairs, the task of an adaptive organism is to find
the difference between these two states, and then to find the correlating process that will
erase the difference.
As reviewed above, Popper’s (1972) general theory of evolution explains how
adaptation can evolve in non-teleonomic ways.
Defining life as autopoiesis
The concept of “autopoiesis” (i.e., “self + production”) was developed in the 1970s as a
necessary and sufficient definition for what it took for a complex adaptive molecular system
to be considered a living organism (Maturana and Varela 1980, Maturana 2002). Varela et al
(1974) listed six criteria that we abbreviate here (original definitions given in quotes):
1.
Bounded (“the unity [entity] has identifiable boundaries”). In this Varela et al. were
primarily concerned that the entity could be discriminated by an external observer. To
us this criterion should read, “the entity has self-identifiable boundaries”. Note: in living
cells the boundary is a semi-permeable membrane, protected by a cell wall in plants.
Complex (“there are constitutive elements of the unity, that is, components”).
Mechanistic (“the component properties are capable of satisfying certain relations that
determine in the unity the interactions and transformations of these components”). In
other words, the complex entity is dynamical, such that components show causal
interactions driven by energy dissipation.
Self-referential or self-differentiated (“the components that constitute the boundaries of
the unity constitute these boundaries through preferential neighborhood relations and
interactions between themselves, as determined by their properties in the space of their
interactions”). That is, the boundaries of the system are determined by the structural
relationships between the entity’s components.
Self-producing (“the boundaries of the unity are produced by the interactions of the
components of the unity, either by transformations of previously produced components,
or by transformations and/or coupling of non-component elements that enter the unity
through its boundaries”). Note that there is no implication here that the entity is
physically closed against exchanges of matter and energy.
Autonomous (“all the other components of the unity are also produced by interactions of
its components as in [the statement above], and … those which are not produced by the
interactions of other components participate as necessary permanent constitutive
components in the production of other components”).
2.
3.
4.
5.
6.
It is arguable that, as was the case for our treatment of Popper’s three worlds, our
paraphrase for the purposes of developing an ontology here shades Maturana and Varela’s
original concepts in ways they did not intend (Urrestarazu, pers. comm.)10. However, implicit
in the definition of autopoiesis is the concept of “self-sustainability”, i.e., that the autopoietic
entity contains within its cybernetic structure sufficient capacity for cybernetic self-regulation

10 What we are doing here is unifying the paradigms of Popper’s later epistemology and Maturana and Varela’s
autopoiesis, such that an understanding of evolutionary epistemology informs the understanding of
autopoiesis, and vice versa. Following the insights of Kuhn (1962, 1983), some shifts in concepts and
vocabulary are required to make the contents of the two paradigms commensurable. This in no way denies
the importance or value of the two schools to the present project.
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to be able to compensate against potentially disruptive perturbations that might otherwise
cause its disintegration (Maturana and Varela 1980).
Luhmann (1986, 1990, 1995), Von Krogh and Roos (1995), Magalhaes (1998) amongst
others extended the concept of autopoiesis to suggest that social organisations or systems
might be autopoietic entities at a higher order of complexity. Luhmann extended the idea of
autopoiesis to establish a theory of social systems, where intangible human social systems
were formed by recursive networks of communications. Hall and Nousala (2010) claim that
Luhmann fundamentally misunderstood Maturana and Varela’s autopoiesis by thinking that
the self-observation necessary for self-maintenance formed a paradoxically vicious circle.
Luhmann tried to resolve this apparent paradox by placing the communication networks on an
imaginary plane orthogonal to the networked people. However, Karl Popper’s evolutionary
epistemology and the theory of hierarchically complex systems (see below) shows that what
Luhmann thought was a vicious circle is a virtuous spiral of organizational learning and
knowledge. There is no closed circle that needs to be explained via Luhmann’s extraordinarily
paradoxical linguistic contortions.
Stafford Beer (1981, 1984), in his “viable systems model”, and in his Preface to
Maturana and Varela (1980), also argued that self-directing systems with properties of life
could exist at different orders of complexity. Others (Zolo 1990; Mingers 1992, 1995, 2002,
2004; Biggiero 2001; Kay 2001; Brocklesby 2004; etc.) have argued that higher order entities
cannot be considered to be autopoietic from a variety of philosophical and sociological points
of view too diffuse and too numerous to review here. Also, Maturana (2002) and Varela
(1980) both made it clear that they only ever intended the concept of autopoiesis to be applied
to macromolecular systems at the cellular level of organization.
However, the approach followed here, unifying autopoiesis and evolutionary
epistemology with the theory of hierarchically complex systems, is very different from
Maturana and Varela, Luhmann, and Beer’s approaches to autopoiesis. We will show that
autopoietic systems may also emerge at levels of hierarchical complexity above the cellular
level.
Autopoiesis, life and knowledge
As described by Hall (2005, 2006a, 2011; Hall et al. 2005) and substantially simplified
here, autopoiesis is an emergent phenomenon growing out of entropically dissipative eddies
(i.e., emergent systems) transporting fluxes of energy from source to sinks. These may be
shaped by natural selection that favors the survival of those systems that are so organized that
the cyclical dynamics of their structure applies some degree of self-regulatory feedback to
maintain the dynamics of the structure. It should be noted that such systems must reach some
degree of complexity before feedback is possible, and that systems with feedback are
inherently non-linear.
Where chance arrangements of the structural organization of complex systems provide
tendencies for even the slightest self-regulatory capacity to maintain itself, such tendencies
represent an embodiment of at least some control information (W2 knowledge) that can be
amplified and extended by further natural selection favoring survival of better regulated
systems. That is, randomly emerging systems lacking feedback regulation soon disintegrate in
the face of perturbations. Any knowledge embodied in the structure of the system prior to its
disintegration is lost in that disintegration. In Popper’s terms, systems that disintegrate have
been eliminated as “errors” that failed to solve “problems of life”. Progress towards any of the
six criteria of Varela et al. (1974) listed above will add the capacity to survive an increasing
range and magnitude of perturbations (i.e., robustness) to surviving systems, and will thus be
favored by natural selection.
It follows from this kind of argument that (1) to survive, autopoietic systems depend on
the control information/survival knowledge embodied in the organizations of their dynamic
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structures, and (2) the evolutionary consequence of natural selection working on autopoietic
systems is the generation of knowledge. In other words, (1) life cannot exist without
knowledge, and (2) knowledge is a product of living.
Living systems in a complex hierarchy
What does it mean that the Universe, organizations, people and cells are all
“hierarchically complex systems”? Simon (1973, 2002) defines the key concepts (see also
Simon 1996; Pattee 1973, 2000; Salthe 1985, 1993, 2004; Chaisson 2001). “Hierarchy”, as
used here (Salthe’s “scalar” hierarchy) refers to nesting relationships where a large object,
e.g., a “Chinese box”, may exist as one of a set of similar objects within a still larger object,
and contains a set of smaller objects (boxes), where each of the smaller objects may itself
contain a set of still smaller objects, and so on up and down along a dimensional scale. When
applied to dynamic systems, a given system observed at one scalar level may be seen to be
composed of a number of subsystems when examined at a smaller scale, or be seen to be one
of several or many components of a supersystem that can be discriminated at a larger scale.
Although hierarchies may be arbitrarily established by an observer, there are completely
natural criteria by which observers can discriminate hierarchically organized systems in the
real world by their dynamic properties. If the dynamics of interactions in the system are
observed at a particular level of focus for a length of time, τ, and we cannot discriminate
changes that take place in less time than T, there are three classes of change: (1) those at
higher levels that are too slow to be seen during our observation such that they appear to us to
be constant; (2) those at our level of focus that we can see happening; and (3) those at lower
levels of focus that are so fast that the systems in which they occur seem to be in equilibrium
or steady-state, such that they interact as “rigid” objects. Simon (1973) calls systems with
these properties “nearly decomposable”. Thus, even though the several components of a
complex system at a given level of complexity may each have a high frequency of internal
dynamics, the internal dynamics of one component is only very loosely coupled by much
slower dynamics with the fast internal dynamics of other components. Even though each
component is internally complex and dynamic, the loose coupling between the internal
dynamics of one component and another allows them to be clearly discriminated as discrete
objects or entities. Maturana recognized near decomposability as follows (Maturana 1980: p.
30): “…the physical boundaries of a living system… are realized by [their] components
through their preferential interactions within the autopoietic network, [which then] become
apparent as surfaces of thermodynamic cleavage” [our italics], where rates of energy
dissipation outside the boundary are substantially less than that within the boundary.
Observers can recognize such self-defined systems at a given level of organization by
selecting an appropriate “level of focus”.
It is easy for humans to recognize and see complex system entities at our own focal
level or lower levels of organization in a complex systems hierarchy (e.g., where we would
use a magnifying glass or microscope). On the other hand it is much more difficult for us to
discriminate and focus on complex entities at larger scales and higher levels of focus than our
own, e.g. ‘evolutionary entities’ that contain us such as Homo sapiens, or the planet Earth, or
the Milky Way Galaxy, (Gould 2002; Chaisson 2001). It takes the equivalent of looking
through the wrong end of a telescope and considerable mental effort and practice for us to
recognize and “see” the boundaries of such higher level systems that include us in their
structures. However, many human organizations are at least self-identifiably bounded by the
equivalent of an individual cell’s semi-permeable boundary or a multi-cellular organism’s
skin, e.g., by physical walls and boundary fences, access security policies, ID tags, property
deeds and ownership records, etc. that we can also learn to see as boundaries.
The phenomena comprising autopoiesis can emerge at different scales or levels of
complexity in a hierarchically complex world. The theory of hierarchically complex living
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systems derives from concepts of complexity, control and causation, scalar levels of
organization, and emergence (Hall 2006a). Hierarchically complex systems are those where
individual parts that interact to form a system at one “level of focus” can be seen to be
composed of several to many interacting components at a more detailed, “lower”, level of
focus (Figure 2). Every complex system can be seen to have a triadic existence: as a
component in a higher level “supersystem”, the focal system itself at the focal level, which in
turn is comprised by lower level “subsystems” serving as its components (Koestler 1967,
1978; Salthe 1985, 1993). Spontaneous dynamics in a system is entropically driven by the
dissipation of free energy in a flux from a high potential source to a low potential sink
(Prigogine 1955, 1981). The dynamic structure of a focal system (i.e., the specific states,
interactions and trajectories of the components comprising the system) at a point in time
establish conditions that provide a downward control over the dynamic possibilities available
to the subsystems comprising the focal system (Pattee 1973, 2000). The dynamic structures
providing that control can be considered to embody part of the control information governing
the cybernetics of the system (Pattee 2000, Corning 2001).
HIGHER LEVEL SYSTEM / ENVIRONMENTHIGHER LEVEL SYSTEM / ENVIRONMENT
SYSTEM SYSTEM
"HOLON""HOLON"
SYSTEMSYSTEM
SUBSYSTEMSSUBSYSTEMS
boundary
conditions,conditions,
constraints,constraints,
regulations,regulations,
actualitiesactualities
FOCAL LEVELFOCAL LEVEL
PossibilitiesPossibilities
initiating
conditionsconditions
universal
laws
"material -
causes" causes"
boundary
initiating
universal
laws
"material -


Figure 2. The systems triad in hierarchy of complex dynamic systems (Hall et al. 2005 after
Salthe 1985).
Emergence of autopoiesis at levels of complexity above the cellular
Where the potential gradient in the energy flux between the source and sink across a
given focal system is particularly large (e.g., because the higher level supersystem is
“inefficient”) processes may emerge to form an additional dissipative system between two
existing levels of organization in the complex systems hierarchy (Salthe 2004; see additional
references in Hall 2011). Where conditions are suitable, life may emerge at any level of
complexity where natural selection favors the emergence of autopoietic properties (Hall
2006a; Hall 2011).
Maturana and Varela (1980: p. 135), define an autopoietic machine as one that is
organized (defined as a unity) as a network of processes of production, transformation
and destruction of components that produces the components which: (i) through their
interactions and transformations regenerate and realize the network of processes
(relations) that produced them; and (ii) constitute it as a concrete unity in the space in
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which they exist by specifying the topological domain of its realization as such a
network.
An allopoietic machine is one that has as a product of its functioning something
different from itself, such as a car produced by a production line. Using these definitions,
Maturana and Varela explain how an assembly of machines that are autopoietic in their own
right can combine to form a higher order autopoietic entity (Maturana and Varela 1980: pp.
110-111):
If the autopoiesis of the component unities of a composite autopoietic system conforms to
allopoietic roles that through the production of relations of constitution, specification and
order define an autopoietic space, the new system becomes in its own right an autopoietic
unity of second order. This has actually happened on earth with the evolution of the
multicellular pattern of organization. When this occurs, the component (living)
autopoietic systems become necessarily subordinated, in the way they realize their
autopoiesis, to the maintenance of the autopoiesis of the higher order autopoietic unity
which, through their coupling, they define topologically in the physical space. If the
higher order autopoietic system undergoes self-reproduction (through the self-
reproduction of one of its component autopoietic unities or otherwise), an evolutionary
process begins in which the evolution of the manner of realization of the component
autopoietic systems is necessarily subordinated to the evolution of the manner of
realization of the composite unity. Furthermore, it is to be expected that if the proper
contingencies are given, higher order autopoietic unities will be formed through
selection. In fact, if coupling arises as a form of satisfying autopoiesis, a second order
unity formed from previous autopoietic systems will be more stable, the more stable the
coupling is [our emphasis].
In a national economy, money represents control over energy fluxes and books of
account document the cash flows that economic organizations dissipate in order to maintain
their survival (Hall 2005, 2006a). Autopoietic economic organizations can emerge at a level
of organization between the dissipative requirements of individual humans for food,
protection from the elements, etc. and the larger national economy that inefficiently caters for
the needs of particular individuals.
Hall (2005: pp. 180-181) listed properties of economic organizations for each of Varela
et al’s (1974) six criteria for recognizing when a system should be considered to be
autopoietic (see their words above, p. 9).
1.
Bounded
Varela et al. were primarily concerned that the boundary be identifiable by an
external observer. More importantly to us, to be considered autopoietic the entity itself
must be able to discriminate its own components from those in the external
environment. Where economic organizations are concerned, their components are often
identified, physically aggregated and separated from the external environment by
fences, walls, access security procedures, corporate branding and logos, etc. Most
importantly, although individual people are members of an organization for only parts
of days for parts of their lives, they are “tagged” in a variety of ways as members of the
organization for those fractions of their life that are important to the organization using
membership and business cards, employment agreements, wages and salaries, oaths of
allegiance, acceptance of creeds, wearing of uniforms, etc.
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