Info-computational Constructivism
Gordana Dodig-Crnkovic
Mälardalen University, Sweden
gordana.dodig-crnkovic@mdh.se
Structured Abstract
Context: At present we lack both common understanding of the process of cognition in living
organisms and the details of construction of knowledge in embodied, embedded cognizing agents in
general, including future artifactual intelligent agents such as cognitive robots and softbots that are
being developed. The info-computational approach is focused on the mechanisms behind the observed
phenomena understood as computational processes on informational structures.
Purpose: This paper presents a comparative study of computational framework of infocomputationalism and constructivism through an analysis of the process of knowledge
production in cognizing agents. Two basic concepts of info-computationalism: information (structure)
and computation (information processing, information dynamics) are used to show how the process of
knowledge construction proceeds in cognizing agents from the interaction with the world as
“protoinformation” perceived by an agent. It provides support for the views of radical constructivism
that knowledge is actively constructed by an agent and shows how this process can be modelled.
Results: For a human it is impossible to grasp reality at once at all levels of organization, so we
analyze cognitive processes as they unfold in a layered structure of nested information network
hierarchies with corresponding computational dynamics (information processes) – from molecular, to
cellular, organismic and social levels and show how the process of knowledge generation proceeds.
Implications: Info-computational approach is especially suitable for modelling of phenomena where
network interactions are essential such as metabolism, immune system, individual and social
cognition.
Constructivist Content: The aim is to contribute to the constructivist project with new perspectives and
to indicate how computational approaches, dominant in knowledge production today and new
computational models under current development, together with new insights from research on theory
of information and bioinformatics are related to constructivism.
Key Words – Constructivism, Computing nature, Info-computationalism, Morphological computing,
Information physics, Evolution with Self-organization and Autopoiesis.
1
Introduction
The historical roots of info-computational constructivism can be traced back to cybernetics, which has
evolved through the three periods, according to (Umpleby, 2002). The period of engineering cybernetics,
from 1950s to 1960s, concerned the design of control systems and machines to emulate human reasoning
(Wiener, 1948) was the first order cybernetics. The next period, biological cybernetics, developed
during1970s and 1980s, was dominated by biology of cognition and second order constructivist
philosophy (Maturana, 1970; von Foerster, 1981; von Glasersfeld, 1987) while in the third, most recent
period of social cybernetics, modelling of social systems was developed (Luhmann )(Umpleby, 2001).
During the research in biology of cognition the focus shifted from what is observed to the observer, while
in the social cybernetics models of groups of observers developed within conceptual system of second
order cybernetics or constructivist cybernetics. (Umpleby, 2002) The achievements of the first period
have been largely assimilated into engineering, automation, robotics (especially autonomous robotics)
and related fields, the second period influenced cognitive science while the research into topics of the
third period is under current development labelled as social cognition/ social computing/ multi agent
systems. Via those three different directions of development, the approaches and results have been
integrated in the info - computational conceptual space, through its connections to engineering, cognitive
science, bioinformatics, social computing, robotics and related fields.
This article will concentrate on the connections of info-computationalism with the second period, with the
focus on biology of cognition and constructivist philosophy with Humberto Maturana, Heinz von
Foerster, and Ernst von Glasersfeld as main representatives. Based on arguments developed in my earlier
work it will examine how info-computationalism relates to different constructivist approaches. 1
Info-computationalism is a variety of natural computationalism which understands whole nature as a
computational process. As living organisms, we humans are cognizing agents who construct knowledge
and understanding of reality through interactions with the outside world and through the processing of
information within our cognitive apparatus. Processes of cognition as well as other processes in the infocomputational model of the universe are computational processes. This is a generalized type of
computation, natural computation, which is defined as information processing. Information is also a
generalized concept in this context, and it is always agent dependent. Information is a difference that
makes a difference for an agent. For different types of agents the same data input will result in different
information. Agent can be anything from a molecule to a plant, animal, human or robot. Thus information
processing (computation) in this approach is going on in the world, and information is the fabric of the
world that agents use and make sense of through interactions. What is reality depends on an agent or
rather on communicating networks of agents who form an inter-subjective common framework. It
happens for social animals as well as robots. That is how this info-computational framework provides a
broader view that shows the mechanisms of knowledge construction in different kinds of agents. It comes
to Maturana and Varela’s conclusion that life is synonymous with cognition and that to be alive means to
be a cognitive agent.
This brings the question about the distinction between living and non-living agents. When is a group or
“society of molecules”2 alive? The study of the origins of life made it clear (in agreement with Maturana
and Varela’s autopoetic view of life) that all living beings are open systems in the constant interaction
with the environment (Prigogine, Kauffman). Unlike our age-old attempts to build closed (axiomatic)
systems in sciences, we must learn to deal with the process of construction in an open system3 if we want
to be able to adequately model living systems. Within info-computationalism this process is natural
computation and what we can say about the objects of construction or objects in the world in general is
that they are potential informational structures that cognizing agents manipulate and made sense of.
1
The description of the conceptual framework of info-computationalism can be found in (Dodig-Crnkovic & Müller, 2011) (DodigCrnkovic, 2009) (Dodig-Crnkovic, 2006). The relationship between natural computing (such as biocomputing, DNA-computing,
social computing, quantum computing, etc) and the traditional Turing machine model of computation is elaborated in (DodigCrnkovic, 2012a)(Dodig-Crnkovic, 2011a) (Dodig-Crnkovic, 2011b) (Dodig-Crnkovic, 2010a). Constructing/generation of
knowledge within info-computational framework is discussed in (Dodig-Crnkovic, 2007) (Dodig-Crnkovic, 2010b)(DodigCrnkovic, 2010c)(Dodig-Crnkovic, 2008).
2
This is meant to point to Marvin Minsky’s ”society of mind”, or in general, when some interacting parts make some qualitatively
different whole, or: when an emergent phenomenon emerge?
3
The problem of the relationship between closed and open systems, that is complementarity of constructive and axiomatic
approaches is addressed in (Burgin & Dodig-Crnkovic, 2013). This elucidates the problematic nature of absolute truth and shows
the need for replacement of the notion of truth by the notion of correctness within a formal system and relates to the controversies
about the relationship between knowledge and truth actualized by constructivists. Finally the idea of computing nature and the
relationships between two basic concepts of information and computation are explored in (Dodig-Crnkovic & Giovagnoli, 2013)
(Dodig-Crnkovic & Burgin, 2011).
2
Add preview of what the reader is to be expect
The Computing Nature
The universe is an idea deeply rooted in our human culture, different in different places and during
different epochs. At one time, it was a living organism (Tree of Life, World Turtle, Mother Earth), at yet
another time, mechanical machinery - the Cartesian-Newtonian clockwork. Today’s metaphor for the
universe is more and more explicitly becoming a computer or rather a network of networks of
computational processes.
Computer pioneer Zuse was the first to suggest (in 1967) that the physical behavior of the entire universe
is being computed on a basic level, possible to model on cellular automata, by the universe itself which
he referred to as “Rechnender Raum” or Computing Space/Cosmos. Consequently, Zuse was the first
pancomputationalist (naturalist computationalist), followed by many others like Ed Fredkin, Stephen
Wolfram and Seth Lloyd – to name but a few. According to the idea of computing nature (naturalist
computationalism or pancomputationalism) one can view the time development (dynamics) of physical
states in nature as information processing (natural computation). Such processes include self-assembly,
developmental processes, gene regulation networks, gene assembly in unicellular organisms, proteinprotein interaction networks, biological transport networks, and similar. (Dodig-Crnkovic & Giovagnoli,
2013)
Within info-computationalism, two basic concepts information and computation (the dynamics of
informational structure) are mutually interdependent (Dodig-Crnkovic, 2011a) (Chaitin, 2007) – so the
framework is a synthesis of informational structural realism and natural computationalism.
Informational structural realism (Floridi, 2003) takes information to be the fabric of the universe (for an
agent that will use the threads of this fabric to weave their own structures). As a consequence the process
of dynamical changes of the universe makes the universe a huge computational network where
computation is information processing. As it corresponds to the dynamic of processes that exist in the
universe, it is necessarily both discrete and continuous, on both symbolic and sub-symbolic4 level.
Information and computation are two fundamental and inseparable elements necessary for naturalizing
cognition and knowledge. (Dodig-Crnkovic, 2009) Information is structure, that exists either potentially,
outside of the agent (structures of the world) or inside an agent (agents own bodily structures that contain
memories of the previous experiences with the world). Messages are just a very special kind of
information that is exchanged between communicating agents. They can be chemical molecules, pictures,
sounds written symbols or similar. Agents can be as simple structures as molecules or simplest living
organisms. Agents are simply entities able to act on their own behalf.
Physicists Zeilinger (Zeilinger, 2005) and Vedral (Vedral, 2010) suggest the possibility of seeing
information and reality as one5. This is in accord with informational structural realism which says that
reality is made of informational structures (Floridi, 2009)(Floridi, 2008) (Sayre, 1976) as well as with
info-computational epistemology (Dodig-Crnkovic, 2009) based on informational structural realism in
conjunction with natural computationalism. Reality for an agent is informational and agent-dependent
(observer-dependent) and consists of structural objects, which are adjusted to the shared reality of agents
community of practice. This brings together metaphysical views of Wiener (“information is information,
not matter or energy”) and Wheeler (“it from bit”) with Zuse, Fredkin, Lloyd, Wolfram and others view
of computing nature.
In sum: for info-computationalism information is the structure, the fabric of reality. The world exists
(realist position of structural realism)6 in the form of proto-information, the potential form of existence
corresponding to Kant’s das Ding an sich. That proto-information becomes information (“a difference
4
Sub-symbolic computations go on in neural networks, as signal processing.
This of course does not imply that potential information from the world moves intact into an agent. This potential
(proto-) information is accessed by an agent through interactions and it is processed by agents cognitive apparatus. It
is dynamically integrated and linked to the rest of informational structures (in the memory). What is important and
new about this view of physicists is that they do not talk about matter and energy as the primary stuff of the universe
(which traditionally is objectivized within sciences). They talk about information, thus putting back an agent into the
picture of the world.
6
This is an ontological commitment in contrast to the agnostic position of constructivism, which argues that no such
metaphysics is necessary (for constructivist epistemology). In computing ontology represents all that exists for a
computational system.
5
3
that makes a difference” according to (Bateson, 1972)) for a cognizing agent in a process of interaction
through which specific aspects of the world get uncovered.
There is a more general definition of information that includes the fact that information is relational and
subsumes Bateson’s definition:
”Information expresses the fact that a system is in a certain configuration that is correlated to the
configuration of another system. Any physical system may contain information about another physical
system.” (italics added) (Hewitt, 2007)
This has profound consequences for epistemology and relates to the ideas of participatory universe,
(Wheeler, 1990) endophysics (Rössler, 1998) and observer-dependent knowledge production as
understood in second-order cybernetics. Combining Bateson and Hewitt insights, on the basic level,
information is the difference in one physical system that makes difference in another physical system.
Of special interest with respect to knowledge generation are agents - systems able to act on their own
behalf.
The world as it appears to an agent depends on the type of interaction through which the agent acquires
information7. Potential information in the world is obviously much richer than what we observe,
containing invisible worlds of molecules, atoms and sub-atomic phenomena, distant cosmological objects
and similar. Our knowledge about this potential information or proto-information which reveals with help
of scientific instruments continuously increase with the development of new devices and the new ways of
interaction with the world, both theoretical and material constructs (Dodig-Crnkovic & Mueller, 2009).
Information and Computation in Cognizing Agents
Living organisms are adaptive systems and as a result of evolution, they have developed ability to
effectively deal with complex environments. Studies in biology, ethology and neuroscience, which have
increased our knowledge of biological cognitive functions, have led to the insight that the most important
feature of cognition is its ability to deal efficiently with complexity, see argument by Gell Mann8in . This
understanding that complexity management (thus efficient information processing) is central for survival,
together with the increase in power of electronic computing and robotics, especially cognitive robotics,
brings us closer to adequate modelling of intelligent behaviour.9 From the computationalist point of view
intelligence may be seen as capacity based on several levels of data processing in a cognizing agent, as
argued by Minsky. Data, information, perceptual images and knowledge are organized in a multiscale
model of the brain and nervous system, up to the emergent level of consciousness according to (Minsky,
1986)(Minsky, 2011). Multiresolutional models have proven to be a good way of studying complexity in
biological systems, and they are also being implemented in artificial intelligence, AI (Goertzel, 1993).
The advantage of computational approaches is their testability. Cognitive robotics research, e.g. presents
us with a sort of laboratory where our understanding of cognition can be tested in a rigorous manner.
From cognitive robotics it is becoming evident that cognition and intelligence are closely related to
agency. Anticipation, planning and control are essential features of intelligent agency. A similarity has
been found between the generation of behaviour in living organisms and the formation of control
sequences in artificial systems. (Pfeifer & Bongard, 2006)(Pfeifer, Lungarella, & Iida, 2007)
Information produced from sensory data processed by an agent is a result of the process of perception.
From the point of view of data processing, perception can be seen as an interface between the (potential)
data10 (the world) and an agent’s perception of the world. This interface is an information processing
device, which means that information input for an agent gets re-structured, and linked with the existing
information. This is illustrated by (Hoffman, 2009) criticizing the traditional view of perception as a true
picture of the world:
7
For example, results of observations of the same physical object (celestial body) in different wavelengths (radio,
microwave, infrared, visible, ultraviolet and X-ray) give profoundly different pictures, so agents sensitive to different
wave lengths would get different notions of the same phenomenon.
8
According to Gell-Mann, complex adaptive systems encode their environments into different schemata, and dealing
with complexity is of prime importance for information processing of a cognitive agent.
9 See for example first three chapters in Computing Nature (Cottam et al., Phillips and Lindley)
10 Data are atoms of information. Information is obtained when data result into structure (correlated), which happens
in the interaction with a cognizing agent.
4
“Instead, our perceptions constitute a species-specific user interface that guides behavior in a niche. Just as
the icons of a PC's interface hide the complexity of the computer, so our perceptions usefully hide the
complexity of the world, and guide adaptive behavior. This interface theory of perception offers a
framework, motivated by evolution, to guide research in object categorization. ”
Thus, perception produces chunks of interrelated informational structures that connect the inside
cognitive informational structures with the outside informational structures through a dynamic
information processing matching. Cognition cannot be cut off on one side of the interface, inside an agent
and its brain. Patterns of information are both in the world and in the functions and structures of the agent
which are connected through dynamical information processes of self-structuring (self-organization).
Information is the difference in the world that makes difference in an agent.
With perception as an interface (thus an executing program in a computationalist phrasing which
performs the first part of the construction process), sensorimotor activities play a central role in realizing
the function of connecting the inside with the outside worlds of an agent, endogenous with the
exogenous. Perception has co-evolved with sensorimotor skills of an organism. Enactive approach to
perception (Noë, 2004) emphasizes the role of sensorimotor abilities, that can be connected with the
changing informational interface between an agent and the world, and thus increasing information
exchange. Enactivism (Francisco Varela, Evan Thompson, and Eleanor Rosch) emphasizes that cognizing
agents self-organize through interaction with their environment. It is an approach closely related to
situated cognition and embodied cognition, and an alternative to Cartesian dualism, cognitivism (in its
representationalist and symbolic information-processing form) and computationalism (based on the
Turing Machine model of computation). However, recent developments in cognitive science and
computation have evolved to encompass physical computation and non-symbolic, embodied information
processing.
Both enactive approach and interface theory fit naturally into info-computational framework. As
mentioned before with reference to (Minsky, 1986) and (Goertzel, 1993), the step from perception to
higher cognitive processes is not trivial and detailed multiresolutional computational accounts are yet to
be developed. They can be expected along the lines similar to Brier’s transdisciplinary approach of
Cybersemiotics (Brier, 2013) with the difference that the connections between different branches of
scientific knowledge (in the sense of “Wissenschaft”11) are here construed computationally.
Traditionally, symbolic AI was an attempt to model cognition and intelligence as symbol manipulation,
which turned out insufficient. (Clark, 1989) In order to improve and complement symbolic approaches,
Smolensky proposed mechanism of an intuitive processor (which is not accessible to symbolic intuition),
with a conscious rule interpreter:
“What kinds of programs are responsible for behavior that is not conscious rule application? I will refer to
the virtual machine that runs these programs as the *intuitive processor*. It is presumably responsible for
all of animal behavior and a huge proportion of human behavior: Perception, practiced motor behavior,
fluent linguistic behavior, intuition in problem solving and game-playing--in short, practically all skilled
performance.” (Smolensky, 1988)
Sloman has developed interesting ideas about mind as virtual machine running on the brain in (Sloman,
2002) which also addresses the symbol grounding problem.
From the point of view of info-computationalism, a mechanism behind this virtual machine hierarchy is
computational self-organization of information, i.e. morphological computing, see (Dodig-Crnkovic,
2012b) and references therein. In his new research programme, Sloman goes a step further studying metamorphogenesis which is the morphogenesis of morphogenesis, (Sloman, 2013) – a way of thinking in the
spirit of second order cybernetics. The difference between morphological computing and our
conventional computers (artificial symbol manipulators implemented in physical systems and governed
by an executing program) is that that morphological computing takes place spontaneously in nature
through physical/chemical/biological processes. The process of growth as well as cognitive processes can
be modelled as morphological computing.
11
Wissenschaft is a broader concept that the English word science, and includes besides science, also
learning, knowledge, and scholarship in general. The word science can be directly translated to
Naturwissenschaft.
5
Info-Computationalist Epistemological Constructivism
“Living systems are cognitive systems, and living as a process is a process of cognition. This statement is
valid for all organisms, with or without a nervous system.” (Maturana & Varela, 1980) p. 13
The above understanding of cognition is adopted by info-computationalism as it provides a notion of
cognition in degrees, which provides a bridge from human-level cognition to minimal cognition in
simplest biological forms and intelligent machines (under development). Within the framework of infocomputational naturalism (Dodig-Crnkovic, 2009) knowledge is seen as a result of successive structuring
of data, where data are simplest information units, signals acquired by a cognizing agent through the
senses/ sensors/ (Dodig-Crnkovic, 2007) (Skyrms, 2010). Information is meaningful data, which can be
turned into knowledge by an interactive computational process going on in the cognizing agent.
Information is always embedded in a physical substrate: signal, molecule, particle or event which will
induce change of a structure or a behaviour of an agent (Landauer, 1991). The world (reality) for an agent
presents potential information, both outside and within an agent. This is a detail of implementation which
classical constructivism does not study. But for a computationalist constructivism this is important as we
must know how to construct cognitive artificial agents that are able to function adequately in the real
world, so we must know how to treat information from the world and how to process it within an agent.
Unlike information, in its various potential and actual forms, which in the info-computationalist
framework constitutes reality for an agent and can be found potentially existing also outside an agent,
knowledge always resides in a cognitive agent. Semantics develops as data → information → knowledge
structuring process, in which complex structures are self-organized by the computational processing from
simpler ones. The meaning of information is thus defined for an agent and a group of agents in a network
and it is given by the use information has for them. Knowledge generation as information processing in
biological agents presupposes natural computation, defined by MacLennan (MacLennan, 2004) as
computation occurring in nature or inspired by that in nature, which is the most general current
computation paradigm.
Knowledge Generation as Morphological Computation
Traditional theoretical Turing machine model of computing is equivalent to algorithms/effective
procedures, recursive functions or formal languages. Turing machine is a logical device, a model for
execution of an algorithm. However, if we want adequately to model computing nature including
biological structures and processes understood as embodied physical information processing, highly
interactive and networked computing models beyond Turing machines are needed, as argued in (DodigCrnkovic & Giovagnoli, 2013). In order to develop general theory of the networked physical information
processing, we must also generalize the ideas of what computation is and what it might be. For new
computing paradigms, see for example (Rozenberg, Bäck, & Kok, 2012)(Burgin, 2005)(MacLennan,
2004) (Wegner, 1998)(Hewitt, 2012)(Abramsky, 2008). Turing machines form the proper subset of the
set of information processing devices.
In the computing nature, knowledge generation should be studied as a natural process. That is the main
idea of Naturalized epistemology (Harms, 2006), where the subject matter is not our concept of
knowledge, but the knowledge itself as it appears in the world 12 as specific informational structures of an
agent. The origin of knowledge in first living agents is not well researched, and the still dominant idea is
that knowledge is possessed only by humans. However, there are different types of knowledge and we
have good reasons to ascribe “knowledge how” and even simpler kinds of “knowledge that” to other
living beings. Plants can be said to possess memory (in their bodily structures) and ability to learn (adapt,
change their morphology) and can be argued to possess rudimentary forms of knowledge. On the topic of
plant cognition see Garzón in (Pombo, O., Torres J.M., Symons J., 2012) p. 121. In his Anticipatory
systems (Rosen, 1985) claim as well: “I cast about for possible biological instances of control of behavior
through the utilization of predictive models. To my astonishment I found them everywhere[…] the tree
possesses a model, which anticipates low temperature on the basis of shortening days” Popper (Popper,
1999) p. 61 ascribes the ability to know to all living: ”Obviously, in the biological and evolutionary sense
in which I speak of knowledge, not only animals and men have expectations and therefore (unconscious)
knowledge, but also plants; and, indeed, all organisms.”
12
Maturana was the first to suggest that knowledge is a biological phenomenon.
6
Computation as information processing should not be identified with classical cognitive science, with the
related notions of input–output and structural representations – but it is important to recognize that also
connectionist models are computational as they are also based on information processing (Scheutz,
2002)(Dodig-Crnkovic, 2009). The basis for the capacity to acquire knowledge is in the specific
morphology of organisms that enables perception, memory and adequate information processing that can
lead to production of new knowledge out of old one. Harms13 proved a theorem showing that under
certain conditions, the total amount of information in the system always increases by natural which will
always lead a population to accumulate information, and so to 'learn' about its environment. Writing on
Harm’s results (Okasha, 2005) points out that
“any evolving population 'learns' about its environment, in Harms' sense, even if the population is composed
of organisms that lack minds entirely, hence lack the ability to have representations of the external world at
all. ”
That may be seen not as a drawback of the theory but as its strength because of the generality of
naturalistic approach. It shows how cognitive capacities are a matter of degree and how they slowly and
successively develop with evolution. Recent empirical results suggest that even simple 'lifeless' prion
molecules are capable of evolutionary change and adaptation, (Li, Browning, Mahal, Oelschlegel, &
Weissmann, 2010).
However, this understanding of basic evolutionary mechanisms of accumulating information at the same
time increasing information processing capacities of organisms (such as memory, anticipation,
computational efficiency) is only the first step towards a full-fledged evolutionary epistemology, but the
most difficult and significant one.
Self-organization and Autopoiesis. System vs. Environment: Open vs. Closed
In order to understand knowledge as natural phenomenon, the process of re-construction of the origins,
development and present forms and existence of life, processes of evolution and development based on
self-organization are central. Work of (Maturana & Varela, 1980) on the constructivist understanding of
life processes is of fundamental importance. They define the process of autopoiesis:
“An autopoietic machine is a machine organized (defined as a unity) as a network of processes of
production (transformation and destruction) of components which:
(i) through their interactions and transformations continuously regenerate and realize the network
of processes (relations) that produced them; and
(ii) constitute it (the machine) as a concrete unity in space in which they (the components) exist
by specifying the topological domain of its realization as such a network.” (Maturana & Varela,
1980) p. 78
What does it mean that an autopoetic system is organizationally closed? It means that it conserves its
organization. That is a true of a momentaneous picture of the world in which organism lives (functions,
operates). Evolution shows that organisms change their structure and successively as they evolve even
organization through the interactions with the environment. In a sense organisms preserve their
organisation, but that organisation is dynamic on an evolutionary time scale. Living beings constantly
metabolize, communicate and exchange information with the world. We can say that there are different
processes going on in an organism – on a short time scales they retain their (dynamical) organization,
while exchanging information with the world. On the long time scale they evolve and thus change their
organization.
Maturana and Varela’s idea of autopoetic systems, and especially the idea of life as cognition is of vital
importance but it might need some reinterpretations when incorporated into the framework of infocomputationalism. Even Luhmann generalized Maturana and Varela’s concept of autopoiesis to include
also psychological and socio-communicative systems (Brier, 2013)
In short, the information processing picture of organisms incorporates basic ideas of autopoiesis and life,
from the sub-cellular to the multi-cellular level. Being cognition, life processes are different sorts of
morphological computing which on evolutionary time scales affect even the structures and organization
of living beings in a sense of meta-morphogenesis.
13
Harms W. F., (2004) Information and Meaning in Evolutionary Processes, Cambridge University Press, chapter 5.
7
Autopoetic processes with structural coupling (interactions with the environment) (Maturana & Varela,
1992) (Maturana & Varela, 1980) can be described within info-computational model as changes in
structures in biological system and thus the information processing patterns in a self-reflective, recursive
computation. Self-organization with natural selection of organisms, responsible for nearly all information
that living systems have built up in their genotypes and phenotypes, is a simple but costly method to
develop knowledge capacities. Higher organisms (which are “more expensive” to evolve 14) have grown
learning and reasoning capability as a more efficient way to accumulate knowledge. The step from
“genetic learning” (typical of more primitive life forms) to acquisition of cognitive skills on higher levels
of organization of the nervous system (such as found in vertebrata) will be the next step to explore in the
project of naturalized epistemology, which is an implementation of info-computational constructivism to
the question of knowledge generation in living agents.
Life is cognition according to Maturana (1970) and (Maturana & Varela 1980). In the info-computational
formulation, this corresponds to information processing in hierarchy of levels of organization, from
molecular networks, to cells and their organisations, to organisms and their networks/societies (DodigCrnkovic, 2008). In that way, fundamental level proto-information (structural information) corresponds to
the physical structure, while cognition is a process that appears as a product of evolution in complex
biological systems, as argued in (Dodig-Crnkovic & Hofkirchner, 2011).
“(I)f we see a living system behaving according to what we consider is adequate behavior in the
circumstances in which we observe it, we claim that it knows. What we see in such
circumstances, is: a) that the living system under our attention shows or exhibits a structural
dynamics that flows in congruence with the structural dynamics of the medium in which we see
it, and b) that it is through that dynamic structural congruence that the living system conserves its
living. I claim that the process which gives rise to the operational congruence between an
organism and its niche, the process that we distinguish in daily life either as learned or as
instinctive knowing, is structural coupling.”
What does it mean for an agent to know? According to (Maturana, 2002) knowledge is precondition for
adequate behaviour of an organism in its environment. For Maturana “the process which gives rise to the
operational congruence between an organism and its niche, the process that we distinguish in daily life
either as learned or as instinctive knowing, is structural coupling.”
Maturana’s structural determinism has its counterpart in the info-computational framework in a form of
structural realism with determinism replaced with causality which may take a form of statistical laws.
Of interest for understanding of life is a system (an agent) that presents a unity for and by itself, such that
can be described as constituting a whole - a property referred to as closure. (Bertschinger, Olbrich, Ay, &
Jost, 2006) mention the following different notions of closure:“ autopoiesis as organizational closure
(Maturana and Varela [1]), closure to efficient cause (Robert Rosen [2]), semantic closure (Howard
Pattee [3]), or operational closure (Niklas Luhmann [4]).“
In the info-computational constructive approach it would be instructive to study the properties and
behaviours of agents under different conditions of closure and how closure affects information processing
and knowledge generation in cognizing agents.
In terms of Foerster’s notion of eigenvalues (stable structures) and eigenbehaviours (stable behaviours
established in the interaction with the environment):
“Any system, cognitive or biological, which is able to relate internally, self-organized, stable structures
(eigenvalues) to constant aspects of its own interaction with an environment can be said to observe
eigenbehavior. Such systems are defined as organizationally closed because their stable internal states can
only be defined in terms of the overall dynamic structure that supports them.” (Rocha, 1998)
Even though organizationally closed living systems are informationally open 15, as they communicate and form
emergent representations of its environment. Rocha (1998) defines self-organization as the “spontaneous formation
of well organized structures, patterns, or behaviours, from random initial conditions.” (p.3). Learning, as a selforganizing process requires that the system (personal or organizational learning systems) “be informationally open,
14
More expensive in this context means that they take more time and other natural resources to develop.
A human compared to a bacteria is considerably more ”expensive” to develop.
15
[Pask, 1992]
8
that is, for it to be able to classify its own interaction with an environment, it must be able to change its structure...”
(p.4).
Self-organization on a cognizing agent level is a micro-process of the larger self-organizing knowledge constructs
created on a social level.
“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 self-defining, or self-referent, through the
imbedding dynamics – implying a complementary relationship (circularity, closure) between
eigenvalues and cognitive/sensory-motor operators: one implies, or defines, the other.
"Eigenvalues represent the externally observable manifestations of the (introspectively
accessible) cognitive [operations]". italics added” (Foerster, 1977) p. 278
Reality as Simulation
We humans have an impression that we interact directly with the “real world as it is”. However that is far
from accurate characterization of what is going on, as already mentioned in connection to perception as
an interface.
“Of all information processing going on in our bodies, perception is only a tiny fraction. Our perception of
the world depends on the relative slowness of conscious perception. Time longer than one second is needed
to synthesize conscious experience. At time scales shorter than one second, the fragmentary nature of
perception reveals.” (Ballard, 2002)
Already Kant argued that “phenomena” or things as they appear to us and which constitute the world of
common experience are an illusion. Kaneko and Tsuda explain why:
“ (T)he brain does not directly map the external world. From this proposition follows the notion of
“interpreting brain”, i.e. the notion that the brain must interpret symbols generated by itself even at the
lowest level of information processing. It seems that many problems related to information processing and
meaning in the brain are rooted in the problems of the mechanisms of symbol generation and meaning.”
(Kaneko & Tsuda, 2001)
Consciousness provides only a rough sense of what is going on in and around us, in the first place what
we take to be essential for us. The world as it appears for our consciousness is a sketchy simulation which
is a computational construction. The Belief that we ever can experience the world 'directly as it is' is an
illusion (Nørretranders, 1999).
What would that mean anyway to experience the world 'directly as it is', without ourselves being part of
the process? Who would experience that world without us? It is important to understand that, as (Kaneko
& Tsuda, 2001) emphasize, the brain maps the information about the (part of the) world into itself, but the
mapped information is always formed by the activity of the brain itself. This is the view of (Maturana,
2007) as well.
The positivist optimism about observations independent of the observer proved problematic in many
fields of physics such as quantum mechanics (wave function collapse after interaction), relativity (speed
dependent length contraction and time dilatation) and chaos (a minor perturbation caused by
measurement sufficient to switch the system to a different attractor). In general, observer and the systems
observed are related (Foerster, 2003) and by understanding their relationship we can gain insights into
limitations and power of models and simulations as knowledge generators.
If what we perceive of the world is a simulation our brain plays for us in order to manage complexity and
enable us to act efficiently, then our knowledge of the world must also be mediated by this computational
modelling nature of cognition. Not even the most reliable knowledge about the physical world as it
appears in sciences is independent of the modelling frameworks which impact what can be known.16
Models are always simplifications made for a purpose and they ignore aspects of the system which are
irrelevant to that purpose. The properties of a system itself must be clearly distinguished from the
properties of its models. All our knowledge is mediated by models. We often get so familiar with a model
16
For example, within liquid-drop model of atomic nucleus, shell structure effects cannot be explained.
9
and its functions that we frequently act as if the model was the actual reality itself (Heylighen & Joslyn,
2001).
Awareness of the modelling character of knowledge and the active role of the cognizing agent in the
process of generation of knowledge is specifically addressed by second-order cybernetics. Cybernetic
epistemology is constructivist in recognizing that knowledge cannot be passively transferred from the
environment, but must be actively constructed by the cognizing agent based on the elements found in the
environment in combination with information stored in the agent. The interaction with the environment
eliminates inadequate models. Model construction thus proceeds through variation and selection. This
agrees with Glasersfeld’s two basic principles of constructivism (Glasersfeld, 1995):
“Knowledge is not passively received either through the senses or by way of communication, but is actively
built up by the cognizing subject.
The function of cognition is adaptive and serves the subject's organization of the experiential world, not the
discovery of an ‘objective ontological reality’ ”.
This understanding coincides with the info-computational view of knowledge generation (DodigCrnkovic, 2007)(Dodig-Crnkovic & Müller, 2011). In an info-computational interpretation, the process of
life is
Some Criticisms of Computationalism and Replies
“1. No information without humans?”
A typical criticism of the informational nature of reality originates from the belief that the world without
cognizing agents would lose its contents because there would be no one to observe it. The view that “If
all the humans in the world vanished tomorrow, all the information would vanish, too.”
Let me try to reply to the above criticism. First of all, not only is information physical (Landauer, 1996),
but even the opposite holds as well: “things physical are reducible to information”, quantum physics can
be formulated in terms of information, for details of the argument see Goyal (Goyal, 2012) and Chiribella
(Chiribella, G.; D’Ariano, G.M.; Perinotti, 2012).
Secondly, clearly, if there are no cognizing agents in the world, the world as potential information, das
Ding an sich, never turns into actual information for an agent. But given the fact that there are cognitive
agents besides humans, living beings – animals, plants, microorganisms and even machines capable of
cognitive computing i.e. processing of information and making sense of it, information for all those
agents continue to exist even if no human is present.
It is not necessary for an agent to be conscious in order to make use of the world as potential information.
Maturana and Varela view life in itself as a cognitive process (Maturana & Varela, 1992). Metabolism is
basic aspect of cognition along with sensorimotor functions and immune system processes. No free will is
needed for information processing that goes on in all living cells. Those processes can be understood as
computational in the sense of natural computation, even though those single-cellular agents cannot be
ascribed consciousness. Matsuno and Salthe go one step further (Matsuno & Salthe, 2011), attributing
material agency and information processing ability even to such simple systems as molecules.
When no agents are present to process protoinformation of the world, it remains potential. Epistemology
of info-computationalism is info-computational constructivism, and it describes the ways cognizing
agents process information and generate knowledge from the existing world17 which steadily changes and
evolves through processes of natural computation.
“2. Signals rather than information?”
(Brier, 2013) criticizes the idea of information as used in info-computationalist framework:
“This is the problem that pan-computational and pan-informational theories attempt to solve with a view of
the world as a grand computer and a new concept of natural computing (Dodig-Crnkovic, 2010; Dodig17
The realism of this approach consists in the assumption that world (reality) exists in the form of proto-information
(synonymous with potential information).
10
Crnkovic & Müller, 2011). Given these assumptions the view of natural computing can be expressed in this
way:
1. The physical world is a network of computational processes with many levels of organization.
2. Whatever changes there are in the states of the physical world, we understand them as computation.
3. Not all kinds of computations (changes in the physical world) are best represented by the Turing model.
In my view, it is not information that is transmitted through the channel in Shannon’s theory, but signals.”
As an answer this criticism I refer to the work of Skyrms (Skyrms, 2010) and Bateson (Bateson, 1972). It
is possible that we should see Bateson’s “differences that make a difference” as data or signals18:
“Kant argued long ago that this piece of chalk contains a million potential facts (Tatsachen) but that only a
very few of these become truly facts by affecting the behavior of entities capable of responding to facts. For
Kant’s Tatsachen, I would substitute differences and point out that the number of potential differences in
this chalk is infinite but that very few of them become effective differences (i.e., items of information) in the
mental process of any larger entity. Information consist of differences that make a difference.” (110a, italics
mine) (Bateson, 1979)
But those differences “items of information” become instantaneously information when they enter a
cognitive system and get integrated into its informational networks.
Brier continues his critical view of natural computationalism:
“We must further theorize how the processes of cognition and communication develop beyond their basis in
the perturbation of and between closed systems and into a theory of feeling, awareness, qualia and meaning.
(…) This ontological foundation does not solve the problem of how experience and meaningful cognition
and communication emerge or manifest themselves in the world.”
The above criticism may be applicable to some computational approaches but definitely not to infocomputationalism based on natural computing and the idea of the world as (proto) informational
structure. As Chalmers aptly remarks, information is fundamental and natural candidate for theory of
consciousness:
“Wheeler (1990) has suggested that information is fundamental to the physics of the universe. According to
this 'it from bit' doctrine, the laws of physics can be cast in terms of information, postulating different states
that give rise to different effects without actually saying what those states are. It is only their position in an
information space that counts. If so, then information is a natural candidate to also play a role in a
fundamental theory of consciousness.
We are led to a conception of the world on which information is truly fundamental, and on which it has two
basic aspects, corresponding to the physical and the phenomenal features of the world.” (italics mine)
(Chalmers, 1995)
The phenomenal aspect of information (vs. physical aspect) can be interpreted as a version of the
endogenous vs. exogenous aspect which get its natural formulation if we chose a relational definition of
information proposed in the introduction, obtained as a combination of Bateson and Hewitt definitions:
on the basic level, information is the difference in one physical system that makes difference in another
physical system. Phenomenal features show when this relation becomes reflexive.
Info-computationalism is agent-based (observer dependent) and takes into account context dependence of
open systems.
“3. Third-person approach?”
(Brier, 2013) discusses Dennett’s project to explain
subjective consciousness and the qualia in
his Consciousness Explained (Dennett 1991) by explaining ‘subjective’ phenomena in
‘objective’ terms “to be the objective, materialistic, third-person world of the physical
sciences.” Dennett (1987a, p. 5)
18
Data are atoms of information. Information is produced when data are interrelated (by an agent).
11
”As far as I can see, none of these endeavours are feasible, since the language of physics does not include
the notion of agent (agency) and meaning.”
The way of interpreting Dennett’s research programme guided by the principle of charity would be to
equal objective with inter-subjective and material to physical which makes it agree with modern cognitive
science approaches as for example presented in (Clark, 1989). Physics has no notion of meaning, but
meaning emerges from physical substrate. Music for a human is more than notes written down on paper
and more than physical phenomena of energy transfer between different media. Yet those are all different
facets/aspects/dimensions of music. Yet another is social – how music is learned, performed, organized in
practice. Subjective experience is still another such aspect of the phenomenon that we call music. It has
no special privileged position in relation to other aspects. Subjective experience has its own merits but it
is by no means more cognitively superior or functional than third-person understanding of that
experience. Without the third person we would not be able to share the knowledge about the existence of
other first person experiences. To base a research programme on a third person perspective, intersubjective knowledge and physical foundations is the most natural approach for a scientist.19
No philosophical approach or scientific field can exhaust all the aspects of one phenomenon – that is why
we need transdisciplinarity and collaboration as a constructive project. Constructive approaches are
important because elements of knowledge produced in specialist fields are used in the building of a
common knowledge network in which elements being connected gain new meaning from their new
common context.
Info-computational framework needs to fill many explanatory gaps and based on neuroscience, biology,
bioinformatics, biosemiotics, cognitive computing etc. provide computational models of phenomena of
mind that we still lack proper scientific models for. (Clark, 1989) and (Dodig-Crnkovic & Giovagnoli,
2013) give some hints how to fill those gaps within computational framework, but it also indicates that a
lot of work remains to be done. As every model serves certain purpose it is to be expected that some
models may be better than the others within different domains, on different levels of abstraction and for
different purposes.
Conclusions
The fundamental second order cybernetics legacy consists of Maturana’s finding that knowledge is a
biological phenomenon, von Foerster’s insight that cognitive agents construct reality in the interplay with
the environment and von Glaserfeld’s argument that the most important characteristics of knowledge is
its viability – practicability in the world. (Umpleby, 1997)
The info-computational framework as a conceptual system builds on the results of Maturana, Foerster and
Glaserfeld and proposes morphological computation as information processing (computational)
mechanism for knowledge generation understood as information self-structuring in cognizing agents –
both biological and artificial. From current insights in the mechanisms of cognition (Smolensky &
Legendre, 2006)(Clark, 1989) (Rosen, 1991)(Wiener, 1948)(Foerster, 1960)(Kampis, 1991)(Kauffman,
1993) it is becoming increasingly visible how cognizing agents construct their knowledge from
information both reflexively within their own informational structures (memory, embodiment) and from
the interactions with the environment (embeddedness). Knowledge provides evolutionary advantage for
an agent and it is in the first place a tool of modelling of the world, thus as any model it is constructed for
a purpose and can by no means be agent-independent or absolute (Dodig-Crnkovic, 2008)(Bates, 2005).
Reality is a dynamical informational structure and for an agent it presents a simulation which is an
interface between an inner and an outer world, resulting from both past, anticipated and current
information used in producing relevant informational interface.
The present article addresses number of questions that have been posed on computational approaches to
constructivism. The goal is to contribute with info - computationalist realist version of constructivism.
The questions have been addressed as follows.
1. Computational Constructive Realism. Reality as Simulation
19
As an example I can mention psychology as a science which deals with people and their first-person experiences
by using third person approach. It is impossible for a physician to feel first person pain of a patient. It is more useful
if he/she can help patient by third-person socially shared knowledge about first person pain that people typically
share in similar situations.
12
Glasersfeld’s claim “[The world] is a black box with which we can deal remarkably well” with
computational interpretation can be unpacked into several claims.
The black box of the world is protoinformation, which presents potential information for an agent capable
of processing it for its own purposes. Sort of computation that “takes place in the cognizing subject that
gives rise to her reality construction based on her (its20) experiences” is natural computation, as defined
in the concept of Computing Nature (Dodig-Crnkovic & Giovagnoli, 2013).
The knowledge construction processes in different kinds of agents, biological and artificial are formulated
computationally in (Clark, 1989)(Pombo, O., Torres J.M., Symons J., 2012)(Scheutz, 2002)(DodigCrnkovic & Giovagnoli, 2013).
Even though existing computational models may provide useful heuristics for simulating an individual’s
construction of reality, we hope to learn more about how nature computes in order to get more detailed
computational models (Dodig-Crnkovic & Giovagnoli, 2013). However, reality construction in complex
cognizing agents like majority of biological systems is not possible to computationally predict, as infocomputational models, though causal, are not deterministic. Nevertheless, it might be possible to
reconstruct and computationally predict reality construction in simple agents such as robots or viruses in
controlled environments.
2. Information: Observer-dependence of knowledge production
In words of Foerster, observer-dependence (agent-dependence) is described as “the truism that a
description (of the universe) implies one who describes it (observes it)”, which implies that:
“(O)ne had to account for an “observer” (that is at least for one subject):
(i) Observations are not absolute but relative to an observer’s point of view (i.e., his coordinate system:
Einstein);
(ii) Observations affect the observed so as to obliterate the observer’s hope for prediction (i.e., his
uncertainty is absolute: Heisenberg). (…)
What we need now is the description of the “describer” or, in other words, we need a theory of the
observer.” p.258 (Foerster, 1981)
Even though there are attempts to define the observer, especially in the theory of measurement in
quantum mechanics, the common understanding of the central importance of agents (“observer
dependence”) in knowledge production is still missing.
3. Computation: Natural/morphological computing
Present article even offers answers to the following questions: “How to define ´computational´?” (As
information processing) and “Can computational models ever create something new?” (Yes, e.g.
evolution shows how new species evolve in case of natural computing)
Info-computational framework enables unified understanding of knowledge generation in cognizing
agents, from the simplest living forms to the most complex ones, building on two basic concepts:
information (structure) and computation (process).
4. Self-organization and Autopoiesis. System vs. Environment: Open vs. Closed
This article argues that computational autopoiesis is not only possible, but that autopoiesis is
fundamentally an info-computational process based on morphological computing. Information is defined
as the difference in one physical system that makes the difference in another physical system.
20
in case of simpler organisms, machines or software agents, my addition.
13
As natural computing is physical computing, enactivism (enactive approach to cognition)21 comes as a
direct consequence of info-computationalism.
As in a short article like this all the arguments cannot be presented in detail, I refer interested reader to
original articles and books (Dodig-Crnkovic & Burgin, 2011) and (Dodig-Crnkovic & Giovagnoli, 2013)
and references therein all of which support the claim that info-computationalism is a way to “to set
´computational constructivism’ in motion”.
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The author
Gordana Dodig-Crnkovic is professor of Computer Science at Mälardalen University, Sweden. Her
research interests include: computing paradigms, natural computing, social computing and social
cognition, info-computational models, foundations of information, computational knowledge generation,
computational aspects of intelligence and cognition, theory of science/philosophy of science, computing
and philosophy and ethics (ethics of computing, information ethics, roboethics and engineering ethics).
She is a co-organizer, PC member and invited speaker at numerous conferences. The most recent events
she co- organized are symposia Natural/Unconventional Computing and its Philosophical Significance
(co-organized with Raffaela Giovagnoli) and Social Computing - Social Cognition - Social Networks and
Multiagent Systems (co-organized with Judith Simon) at AISB/IACAP World Congress 2012. She is the
author of more than eighty international journal and conference publications. Her teaching includes
Research Methods, Theory of Computation, Philosophy of Computing and Professional Ethics.
List of at least five potential independent reviewers
1.
2.
3.
4.
5.
Sören Brier, sb.ibc@cbs.dk
Bob Logan, logan@physics.utoronto.ca
Ranulph Glanville, ranulph@glanville.co.uk
Lorenzo Magnani, lmagnani@unipv.it
Stuart Umpleby, umpleby@gwu.edu
17