Rethinking Knowledge. Modelling the World as Unfolding through Info-Computation for an Embodied...

8/13/2019 Rethinking Knowledge. Modelling the World as Unfolding through Info-Computation for an Embodied Situated Cog

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Rethinking Knowledge. Modelling the World as Unfolding through

Info-Computation for an Embodied Situated Cognitive Agent

Gordana Dodig-Crnkovic

Mlardalen University, Sweden

[email protected]

Abstract

Ever since the days of Plato and Aristotle, the concept of knowledge has been studied

within epistemology, the branch of philosophy concerned primarily with propositional knowledge and

establishing justification of belief, i.e. the truth of statements. Recently, with the rise of information and

communication technology, interest in knowledge management has been born from the necessity toidentify, represent and organise meaningful information. The focus shifted from true knowledge to useful

knowledge, from particular statements to knowledge systems. Knowledge is also being studied within the

educational sciences with the emphasis on learning aspects. The underlying assumption in all these

approaches is that knowledge is a distinctively human capacity.

The present paper describes a new approach to knowledge: knowledge as a natural phenomenon,

within an info-computational framework. Knowledge emerges from informational structures of cognitive

agents through processes of natural computation. A cognitive agent can be any living organism or an

artificial cognitive system. Adopting Maturana and Varelas approach, cognition is understood as being

synonymous with life, as a process of autopoiesis (self-production). Knowledge evolved from the

simplest forms of information, self-structuring in unicellular organisms to the most complex ones as

found in humans.

The aim is not to replace the existing approaches to knowledge but to complement them by new

insights into the phenomenon of knowledge. It may help to resolve old epistemological controversies

about the extent of knowledge (how much is possible to know), the sources of knowledge (empirical

experience vs. intellect), as well as about the nature of knowledge (traditionally it was the question of

how the concept of knowledge should be defined, which becomes transformed into the question: what in

the physical world corresponds to knowledge?). The info-computational approach to knowledge

generation can equally contribute to knowledge management and understanding of learning. Finally it can

contribute to rethinking cognition in humans and other living beings.

Keywords Knowledge generation, information, computation, cognition, info-computationalism,

computing nature, morphological computing, evolution with self-organisation and autopoiesis.


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Introduction

The process by which knowledge is acquired (or generated, produced) is called cognition1

[latin co + gnoscere, to know] and it includes perception, awareness, intuition, reasoning and

judgment. Currently we lack a common understanding of the process of cognition. In this paper

we start by adopting the view of the cognitive scientists Maturana and Varela, that cognition is

synonymous with life (Maturana & Varela, 1980). Even the simplest living organisms possess

some degree of cognition such as metabolism or locomotion. This means that not all cognition

is conscious but all of it is meaningful and purposeful for the cognitive agent. We are interested

in learning from living organisms among others in order to construct artificial cognitive agents

based on similar principles. These cognitive agents can be programs or robots capable of

assisting us in different tasks from intelligently cleaning e-mails or systematising data to

holding a conversation or executing space missions.

Knowledge is a result of cognition and as a natural phenomenon can be seen as emerging

from the biological structure of a cognitive agent. Knowledge provides evolutionary advantage

and ensures the agents ability to cope with the real world, thus improving its cognitive

capacities. In such a way a loop of interdependence between cognitive apparatus of an agent and

its knowledge is established.

This generalisation of cognition to include all living organisms (also plants and unicellular

organisms) and even cognitive computational artefacts is far from generally accepted. The

majority view is still that only humans possess cognition, even though some people would allow

that other primates do cognise, but not more than that. Our adoption of the general definition of

Maturana and Varela is motivated by the wish to provide a theory that would include all living

organisms and artificial cognitive agents within the same framework.

In order to address knowledge as a natural phenomenon, the info-computational approach

(Dodig-Crnkovic, 2006) is used for the study of mechanisms of knowledge generation, both in

an individual cognitive agent and in networks of agents (social cognition), both in real time and

in an evolutionary perspective, on a variety of levels of organisation.

The info-computational framework builds on two basic concepts: information (structure)

and computation (information dynamics). Cognitive processes unfold in a layered structure of

nested information network hierarchies with corresponding computational dynamics from

molecular, to cellular, organismic and social levels.

1Interestingly, Kants notion ofErkenntni is translated both asknowledgeand ascognition.


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The description of the conceptual framework of info-computationalism can be found in

(Dodig-Crnkovic & Mller, 2011) (Dodig-Crnkovic, 2009) (Dodig-Crnkovic, 2006). The

relationship between natural computing (such as biocomputing, DNA-computing, chemical

computing, quantum computing, social computing, etc) and the traditional Turing machine

model of computation is elaborated in (Dodig-Crnkovic, 2012a)(Dodig-Crnkovic, 2011a)

(Dodig-Crnkovic, 2011b) (Dodig-Crnkovic, 2010a). The constructing/generation/production ofknowledge within an info-computational framework is discussed in (Dodig-Crnkovic, 2007)

(Dodig-Crnkovic, 2010b) (Dodig-Crnkovic, 2010c) (Dodig-Crnkovic, 2008).

Cognition as a process of life is characterised by the interaction of a cognising agent with

its environment, which presupposes that living systems are necessarily open systems they

exchange mater-energy and information with the environment. The problem of the relationship

between closed and open systems is addressed in (Burgin & Dodig-Crnkovic, 2013) which

shows the need for replacement of the notion of truth by the notion of correctness within the

reasoning system and relates to the controversies about the relationship between knowledge and

truth as it appears in epistemology.

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) and morphological computing as the underlying mechanism of all

information self-structuring (self-organisation) is addressed in (Dodig-Crnkovic, 2012b)

(Dodig-Crnkovic, 2012c).

The Computing Nature

The universe has been conceptualised in various ways in different cultures 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

the universe is understood as a gigantic computer - a network of networks of computational

processes on many different levels of resolution from quantum mechanical to the molecular

(chemical), biological, sociological and eco-systemic levels.

The computer pioneer Zuse was the first to suggest (in 1967) that the physical behaviour of

the entire universe is being computed on a basic level, modelled by cellular automata, by the

universe itself that he referred to as Rechnender Raum or Computing Space/Cosmos.

Consequently, Zuse was the first pancomputationalist (naturalist computationalist), followed by

many others such as Fredkin, Wolfram, Chaitin and 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


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(natural computation). Such processes include self-assembly, developmental processes, gene

regulation networks, gene assembly in unicellular organisms, protein-protein interaction

networks, biological transport networks, and the like. (Dodig-Crnkovic & Giovagnoli, 2013)

What is the hardware that the computing universe relies on? We, as cognitive agents

interacting with the universe through information exchange, experience cognitively the universe

as information. The informational structural realism (Floridi, 2003) (Floridi, 2009) (Floridi,

2008) (Sayre, 1976) (Stonier, 1997) (Zins et al., 2007) is a framework that takes information as

the fabric of the universe (for an agent). The physicists Zeilinger (Zeilinger, 2005) and Vedral

(Vedral, 2010) suggest that information and reality are one.

For the informational universe, the dynamical changes of its informational structures make

it a huge computational network where computation is understood as information dynamics

(information processing).2

Info-computationalism is a synthesis of informational structural realism and natural

computationalism (pancomputationalism) - the view that the universe computes its own next

state from the previous one3. It builds on two basic complementary concepts: information

(structure) and computation (the dynamics of informational structure) as described in (Dodig-

Crnkovic, 2011a) (Chaitin, 2007). This is the basis of info-computational epistemology (Dodig-

Crnkovic, 2009).

In the computing nature, the generation of knowledge should be studied as a natural

process. That is the main idea of naturalised epistemology (Harms, 2006), in which the subject

matter is not our concept of knowledge, but the knowledge itself as it appears in the world 4

through specific informational structures of an agent. The origin of knowledge in the first living

agents is not well researched, since the idea still prevails that knowledge is possessed only by

humans.

However, there are different types of knowledge and we have good reasons to ascribe

knowledge how (procedural knowledge) and even simpler kinds of knowledge that

2Computations corresponding to dynamic processes in the universe are necessarily of both discrete and continuous

type, on both the symbolic and sub-symbolic level. Information and computation as two fundamental and inseparable

elements are used for naturalising cognition and knowledge in (Dodig-Crnkovic, 2009).3This amounts to computation being equivalent to causality. Note the difference between causality and determinism.

Computation is not always deterministicbut it is necessarily causal. See Collier J., Information, Causation And

Computation Chapter 4 in (Dodig-Crnkovic & Burgin, 2011)4Maturana was the first to suggest that knowledge is a biological phenomenon. He and Varela argued that

life should be understood as a process of cognition which enables an organism to adapt and survive in the

changing environment.


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(knowledge by acquaintance) to other living beings. Plants can be said to possess memory (in

their bodily structures that change as a result of past events) and the ability to learn (plasticity,

ability to adapt through morphodynamics) and can be argued to possess rudimentary forms of

knowledge. On the topic of plant cognition see Garzn in (Pombo, O., Torres J.M., Symons J.,

2012) p. 121. In hisAnticipatory systems(Rosen 1985) claims:

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.

Even 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.

Informational Structure of Reality

In sum, in the proposed framework, information is the structure, the fabric of reality for a

cognitive agent. The world exists independently from us (realist position of structural realism)

as potential information, corresponding to Kants das Ding an sich. This potential information

becomes actual information (a difference that makes a difference according to (Bateson,

1972)) for a cognising agent in a process of interaction through which specific aspects of the

world become uncovered.5

Even though Batesons definition of information is the widely cited one6, there is a more

general definition that includes the fact that information is relationaland subsumes Batesons

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. (Hewitt, 2007) (italics added)

This has profound consequences for epistemology and relates to the ideas of participatory

universe (Wheeler, 1990), endophysics (Rssler, 1998) and observer-dependent knowledge

5Compare this with Kant: To cognize, percipere, is to represent something in comparison with others and to have

insight into its identity or diversity from them." - the Vienna Logic at 24:846.6In the same vein, Schroeder in (Dodig-Crnkovic & Giovagnoli, 2013) distinguishes two aspects of information

selective and structural, while (Dodig-Crnkovic, 2006) defines processes of differentiation and integration of

information as basic for all our information processing.


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production as understood in second-order cybernetics. Combining Bateson and Hewitt insights,

on the basic level, information is a difference in one physical system that makes a difference in

another physical system.

Of special interest with respect to knowledge generation are agents, i.e.systems able to act

on their own behalf.7

The world as it appears to an agent depends on the type of interaction through which the

agent acquires information8. 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 the like. Our knowledge about this potential information which

is revealed with the aid of scientific instruments continuously increases with the development of

new devices and the new ways of interaction with the world, with new theoretical and material

constructs (Dodig-Crnkovic & Mueller, 2009).

As a consequence of the adoption of Hewitts definition of information as a relational

concept, the novelty in the info-computational approach compared to other types of

structuralism is that the reality consisting of structural informational objects for an agent is

agent-dependent (observer-dependent). These subjectively experienced individual agent

realities are adapted to the shared reality of community in a form of inter-subjective agreed

negotiated common world-view.

Cognition as Info-Computation

A cognitive system is a system whose organization defines a domain of interactions in

which it can act with relevance to the maintenance of itself, and the process of cognition

is the actual (inductive) acting or behaving in this domain.Living systems are cognitive

systems, and living as a process is a process of cognition.This statement is valid for all

organisms, with and without a nervous system. (Maturana, 1970) p.13

The central role of cognition for every cognitive agent, from bacteria to humans is its

efficiency in dealing with complexity of the world (Gell-Mann, 1994) helping an agent to

survive and thrive. With the development of electronic computing we are improving the ability

to adequately model living systems and their cognitive functions including intelligent

behaviour. From the computationalist point of view intelligence may be seen as capacity based

on several levels of data processing in a cognising agent (Minsky, 1986). Data, information,

7Agency has been explored in biological systems by Stuart Kauffman, see (Kauffman, 2000)(Kauffman,

1995)(Kauffman, 1993)8For 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.


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perceptual images and knowledge are organised in a multiscale model, up to the emergent level

of consciousness (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 (Goertzel, 1993).

The advantage of computational approaches to modelling compared to pure conceptual

models typical of traditional epistemology is their testability. Daniel Dennett declared in a talk

at the International Computers and Philosophy Conference, Laval, France in 2006: AI makes

philosophy honest. Paraphrasing Dennett we can say that info-computational models make

theories of knowledge and cognition more transparent and suitable for critical investigation and

experimentation. Cognitive robotics research, for example, 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

inseparable from agency. All cognitive systems are dynamical systems argues Giunti in (van

Gelder, T. and Port, 1995) p. 549. 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)

An agent perceives the world through information produced from sensory data. From the

point of view of data processing, perception can be seen as an interface between the data (the

world) and an agents perception of the world. (Hoffman, 2009) criticises the traditional view of

perception as a perfectly mirroring, true picture of the world:

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 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.Information is a difference in the world that makes a difference in an agent.

With perception as an interface, sensorimotor activities play a central role in realising the

function of connecting the inside with the outside worlds of an agent. Perception has co-evolved

with sensorimotor skills of living organisms. No, in an enactive approach to perception,

emphasises the role of evolution of sensorimotor abilities in living systems that can be


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connected with the changing informational interface between an agent and the world, and thus

increasing information exchange and the complexity of an organisms information processing

structures. (No, 2004)

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 Briers Cybersemiotics (Brier, 2013) with the difference that within the info-

computational framework the connections between different types of scientific knowledge (in

the sense of Wissenschaft) are construed computationally.

Symbolic vs. Sub-symbolic Computation. Virtual Machines

Traditionally, analyses of knowledge, cognition and intelligence are done on the level of

(human) language, thus assumed to be symbolic. Not unexpectedly, the first attempts at AI were

modelling cognition and intelligence as symbol manipulation. However "Good Old-FashionedArtificial Intelligence" (GOFAI) turned out to be insufficient as a model of human intelligence

(Clark, 1989). We have experience of knowledge accessible without verbal intervention and

symbol manipulation, such as procedural knowledge (how to do something) that differs from

propositional knowledge (knowledge of facts, that is of prime interest for epistemology).

Moreover, symbols must be grounded in something more basic which from biology and

neuroscience turns out to be signal processing. Smolensky proposed the mechanism of an

intuitive processor (which is not accessible to the symbolic level of information processing)

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)

It follows from the above that ascribing degrees of knowledge to simple organisms implies

accepting non-symbolic knowledge as well. Symbols can be expected for organisms that at least

have nervous systems.

Smolenskys ideas about virtual machines running intuitive information processes were

developed by Sloman, who characterises thehuman mind as a virtual machine running on the

brainhardware,(Sloman, 2002). He also addresses the symbol grounding problem, that is the

question of how symbols acquire meaning through sub-symbolic signal processing.


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The Modelling Nature of Cognition and Observer Dependence

We humans have an impression that we interact directly with the real world as it is.

However, that is far from an accurate characterisation 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 is revealed. The brain creates a picture of

reality that we experience as (and mistake for) 'the actual thing' (Ballard, 2002) (italics added)

Kant, in the Critique of Pure Reason, had already argued that phenomena, or things as

they appear 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 the interpreting brain, i.e. the notion that the brain must interpret

symbols generated by itselfeven at the lowest level of information processing. It seems

that many problems related to information processing and meaning in the brain are

rooted in theproblems of the mechanisms of symbol generation and meaning. (Kaneko

& Tsuda, 2001) (italics added)

Consciousness provides only a rough sense of what is going on in and around us; in the

first place it relates to what we take to be essential. The world as it appears for our

consciousness is a sketchy simulation which is a computational construction. The belief that we

can ever experience the world 'directly as it is' is an illusion (Nrretranders, 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) emphasise, 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 seems to be the view of (Maturana, 2007) as well.

The positivist belief in observations independent of the observer proved problematic in

many fields of physics such as quantum mechanics (wave function collapse after interaction),

relativity (velocity-dependent length contraction and time dilatation) and chaos (a minor

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In general, the observer and the systems observed are related and by understanding their

relationship we can gain insights into the limitations and power of models and simulations as

knowledge generators, as argued in(Foerster, 2003).

If what we perceive of the world is a simulation that our brain plays for us in order to

manage complexity and enable us to act efficiently in the world, 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 indirectly impact what can be expressed and thus known. It does

not mean that scientific knowledge is arbitrary; it only means that it is reproducible under given

conditions within a given domain.

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 become so familiar with a model and its functions that we frequently act as if the model

was the actual reality itself (Heylighen & Joslyn, 2001), which of course is unjustified.

Awareness of the modelling character of knowledge and the active role of the cognising

agent in the process of generation of knowledge is specifically addressed by second order

cybernetics. Cybernetic epistemology is constructive in recognising that knowledge cannot be

passively transferred from the environment, but must be actively constructed by the cognising

agent based on the elements found in the environment in combination with information stored in

the agent (its morphology). The interaction with the environment eliminates inadequate models.

Model construction thus proceeds through variation, information self-organisation, and

selection. This agrees with Glasersfelds two basic principles (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

(Dodig-Crnkovic, 2007) (Dodig-Crnkovic & Mller, 2011). The subject in the above can be

any living organism or indeed an artificial cognitive agent too.


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Knowledge Generation by Morphological Computation

When talking about computational models of biological phenomena, it is important to keep

in mind that within the info-computational framework computation is defined in a general way

as any information processing. This differs from the traditional theoretical model of

computation, the Turing machine model, which is a special case corresponding to

algorithms/effective procedures (equivalent to recursive functions or formal languages). The

Turing machine is a logical device, a model for the execution of an algorithm. However, if we

want to model computing nature adequately, including biological structures and embodied

physical information processing, a new understanding of computation is needed such as highly

interactive and networked concurrent computing models beyond Turing machines, as argued in

(Dodig-Crnkovic & Giovagnoli, 2013) and (Dodig-Crnkovic, 2011b) with reference to (Hewitt,

2012) and (Abramsky, 2008). In order to develop a general theory of networked physical

information processing, we must also generalise the ideas of what computation is and what it

might be developed into. For new computing paradigms, see for example (Rozenberg, Bck, &

Kok, 2012) (Burgin, 2005) (MacLennan, 2004) (Wegner, 1998) (Hewitt, 2012) (Abramsky,

2008).

Computation as information processing should not be identified with classical cognitive

science based on notions of inputoutput and representations in the sense of the Turing machine

model. It is important to recognise that connectionist models (e.g. neural networks) are

computationalas wellas 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 the old one.

As argued in (Dodig-Crnkovic, 2012b), morphology is the central idea in the

understanding of the connection between computation and information. It should be noted that

material also represents morphology, but on a more basic level of organisation the

arrangements of molecular and atomic structures. What appears as a form on a more

fundamental level of organisation (e.g. an arrangement of atoms), represents 'matter' as a

higher-order phenomenon (e.g. a molecule).

In morphological computing, the modelling of an agents behaviour (such as locomotion

and sensory-motor coordination) proceeds by abstracting the principles via information self-

structuring and sensory-motor coordination, (Matsushita et al. 2005), (Lungarella et al. 2005)

(Lungarella and Sporns 2005) (Pfeifer, Lungarella and Iida 2007). Brain control is decentralised

based on sensory-motor coordination through interaction with the environment. Through


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embodied interaction with the environment, in particular through sensory-motor coordination,

an information structure is induced in the sensory data, thus facilitating perception, learning and

categorisation. The same principles of morphological computing (physical computing) and data

self-organisation apply to biology and robotics.

From an evolutionary perspective it is crucial that the environment provides the physical

source of the biological body of an organism as well as a source of energy and matter to enable

its metabolism. The nervous system and brain of an organism evolve gradually through the

interaction of a living agent with its environment. This process of mutual shaping is a result of

information self-structuring. Here, both the physical environment and the physical body of an

agent can at all times be described by their informational structure 9. Physical laws govern

fundamental computational processes which express changes of informational structures.

(Dodig Crnkovic 2008)

The environment provides a variety of inputs in the form of both information and matter-

energy, where the difference between information and matter-energy is not in the kind, but in

the type of use the organism makes of it.As there is no information without representation10, all

information is carried by some physical carrier(light, sound, radio-waves, chemical molecules

able to trigger smell receptors, etc.). The same physical object can be used by an organism as a

source of information and as a source of nourishment/matter/energy. A single type of signal,

such as light, may be used by an organism both as information necessary for orientation in the

environment, and for the photosynthetic production of energy. Thus, the question of what will

be used 'only' as information and what will be used as a source of food/ energy depends on the

nature of the organism. In general, the simpler the organism, the simpler the information

structures of its body, the simpler the information carriers it relies on, and the simpler its

interactions with the environment.

The environment is a resource, but at the same time it also imposes constraints which limit

an agents possibilities. In an agent that can be described as a complex informational structure,

constraints imposed by the environment drive the time development (computation) of its

structures, and thus even its shape and behaviour, to specific trajectories.

9Here is the definition by John Daintith, A Dictionary of Computing (2004)

http://www.encyclopedia.com/doc/1O11-datastructure.html

Data structure (information structure) - an aspect of data type expressing the nature of values that are composite, i.e.

not atoms. The non-atomic values have constituent parts (which need not themselves be atoms), and the data structure

expresses how constituents may be combined to form a compound value or selected from a compound value.

10Landauer, R. 1991, Information is Physical', Physics Today 44, 23 - 29.

Landauer, R. 1996, The Physical Nature of Information Physics Letter (A 217), 188


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This relationship between an agent and its environment is called structural coupling by

(Maturana & Varela 1980) and is described by (Quick and Dautenhahn 1999) as non-

destructive perturbations between a system and its environment, each having an effect on the

dynamical trajectory of the other, and this in turn affecting the generation of and responses to

subsequent perturbations.

Harms proved a theorem showing that natural selection will always lead a population to

accumulate information, and so to 'learn' about its environment (Harms, 2006). Okasha

(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.

Ascribing some rudimentary cognition and thus capacity for knowledge to all living

organisms, no matter how primitive, should be seen not as a drawback of the theory but as its

strength because of the generality of a naturalistic approach. It shows how cognitive capacities

are a matter of degree and how they slowly and successively develop with evolution. From bio-

computing we learn that in living organisms the biological structure (hardware) is at the same

time a program (software) which controls the behaviour of that hardware. (Kampis, 1991)

However, this understanding of the basic evolutionary mechanisms of accumulating

information, at the same time increasing the information-processing capacities of organisms

(such as memory, anticipation, computational efficiency), is only the first step towards a fully-

fledged evolutionary epistemology, but the most difficult and significant one, as it requires a

radical change in our understanding of fundamental concepts of knowledge, cognition,

intelligence, computation and information, among others.

From the point of view of info-computationalism, a mechanism behind the aforementioned

Slomans virtual machine hierarchy (Sloman, 2002) is the computational self-organisation of

information, i.e. morphological computing, see (Dodig-Crnkovic, 2012b) and references

therein. In his new research programme, Sloman goes a step further studying meta-

morphogenesis, which is the morphogenesis of morphogenesis, (Sloman, 2013) a way of

thinking in the spirit of second order cybernetics.

System and Environment. Self-organisation and Autopoiesis

In order to understand knowledge as a natural phenomenon, the process of re-construction

of the origins, development and present forms and existence of life, the processes of evolution


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and development based on self-organisation are central. The work of Maturana and Varela on

the constructivist understanding of life is fundamental. They define the process of autopoiesis of

a living system as follows:

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 organisationally closed? It means that it

conserves its organisation. That is true of a momentaneous picture of the world in which an

organism lives (functions, operates). Obviously evolution shows that organisms change their

organisation through interactions with the environment. In a sense organisms preserve their

organisation, but that organisation is dynamical and evolving. Living beings constantly

metabolise, communicate and exchange information with the world. We can say that there are

different processes going on in an organism on a short time scale they retain their (dynamical)

organisation, while exchanging information with the world. On the longer time scale they

evolve and thus slowly change their organisation.

Immanuel Kant, in his Critique of Judgment, was the first to use the attribute "self-

organising" arguing that teleology (goal-directed behaviour) is possible only for entities that

exist through self-organisation. Such a system is capable of acting on its own behalf (agency)

and governing itself.

In such a natural product as this, every part is thought as owing its presence to the

agency of all the remaining parts, and also as existing for the sake of the others and of

the whole, that is as an instrument, or organ... The part must be an organ producing the

other partseach, consequently, reciprocally producing the others... Only under these

conditions and upon these terms can such a product be an organized and self-organized

being, and, as such, be called a physical end.

http://oll.libertyfund.org/index.php?option=com_staticxt&staticfile=show.php%3Ftitle=1217&layout=html

Immanuel Kant, The Critique of Judgement [1892]

Today we ascribe purposeful (autonomous, goal-directed) behaviour to robots but even

though they appear to act autonomously, they are essentially dependent on humans for

production, maintenance and energy supply.


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After Kant, cyberneticians (Ashby, von Foerster, Pask, and Wiener) returned to the ability

of self-organisation in different systems, both natural and artificial.

The idea of self-organisation was introduced in general systems theory in the 1960s, and

later during the 1970s and 1980s in complex systems. Prigogine (Prigogine & Stengers, 1984)

contributed by insights in the self-organisation in thermodynamic systems far from equilibrium,

which showed an ability of non-living matter to self-organise on the condition that energy is

provided from the environment that is used for self-organisation. This ability of inanimate

matter (chemicals) to self organise has been studied in detail by Kauffman (Kauffman, 1993,

1995). It has inspired research into the origins of life connecting the self-organisation of

chemical molecules with the self-organisation and autopoiesis of living beings.

The importance of Maturana and Varelas idea of autopoietic systems can hardly be

overestimated, and especially the idea of life as cognition is of vital importance. However, it

might need some reinterpretations when incorporated into the framework of info-

computationalism. Similarly, when Luhmann applied the ideas of Maturana and Varela to social

autopoetic systems, he developed an adapted triple autopoietic model of the biological, psychic

and socio-communicative systems. (Brier, 2013)

In short, the information processing model of organisms incorporates basic ideas of

autopoiesis and life, from the sub-cellular to the multi-cellular, organismic and societal levels.

Being cognition, life processes are different sorts of morphological computing which on

evolutionary time scales affect the organisation (structures) of living beings even in a sense of

meta-morphogenesis (i.e. morphogenesis of morphogenesis), (Sloman, 2013).

Through autopoietic processes with structural coupling (interactions with the environment)

a biological system changes its structures and thereby the information processing patterns in a

self-reflective, recursive manner (Maturana & Varela, 1992) (Maturana & Varela, 1980). Self-

organisation 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) have

developed a capability of learning and reasoning as a more efficient way to accumulate

knowledge. The step from genetic learning (typical of more primitive forms of life) to the

acquisition of cognitive skills on higher levels of organisation of the nervous system (such as

found in vertebrata) will be the next step to explore in the project of naturalised epistemology.

In the info-computational formulation, the life as cognition process (Maturana & Varela,

1980, 1992; Maturana, 1970, 2002) corresponds to information processing in the hierarchy of


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levels of organisation, from molecular networks, to cells and their organisations, to organisms

and their networks/societies (Dodig-Crnkovic, 2008). Thus the fundamental level proto-

information (structural information) corresponds to the physical structure, the fabric of reality

for an agent, while cognition is a process that both unfolds in real time as information self-

structuring through interactions (morphological computing), and develops on a long-time scale

(meta-morphogenesis) as a product of evolution in complex biological systems, as argued in(Dodig-Crnkovic & Hofkirchner, 2011).

Examples of Existing Applications

Concurrently with the development of methodological and philosophical and information-

and computation- theoretical arguments for the development of a new scientific paradigm

motivated by the need of better understanding of biological systems, such as the info-

computational approach, the number of new practical applications steadily increases. For a

review of the contemporary work on Biomathics11, see the forthcoming article (Simeonov,

2013).

Two volumes covering topics of information and computation (Dodig-Crnkovic & Burgin,

2011) and the idea of computing nature (Dodig-Crnkovic & Giovagnoli, 2013) cover a range of

topics in which the basic ideas presented in this article have been developed and applied.

The work of Wolff is another interesting practical application (Wolff, 2003, 2006). In the

book Unifying Computing and Cognition, Wolff presents his SP theory with its applications. In

the words of author:

The "SP theory of intelligence" aims to simplify and integrate concepts across artificial

intelligence, mainstream computing and human perception and cognition, with

information compression as a unifying theme. It is conceived as a brain-like system that

receives 'New' information and stores some or all of it in compressed form as 'Old'

information. It is realised in the form of a computer model -- a first version of the SP

machine. The concept of "multiple alignment" is a powerful central idea. Using

heuristic techniques, the system builds multiple alignments that are 'good' in terms of

information compression.

Even though SP theory may find many interesting applications and provides new conceptual

tools to unpack the complex issue of cognition, it is only the beginning of a research in this

direction that has many open questions to address. For example Kauffman argues that living

11A new phase of scientific development in which mathematicians turn to biological processesfor inspiration in

creating novel formalisms in mathematics appropriate to describe biological phenomena.


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organisms exhibit high resilience exactly because of redundant information they embody. Thus

in living systems information compression should not be expected to be maximal but the trade-

offs with redundancy is necessary (Kauffman, 1995).

Open Questions and Future Research

Promises of info-computational programmes rely on learning from nature using

definability, simulability and (where applicable) predictability of its physical processes and

structures as a means to improve our understanding of complex phenomena such as life

(cognition) based on (constantly improved) concepts of computation and information. (Dodig-

Crnkovic, 2011b)

Based on the info-computational framework, the following topics are of particular interest

for future research.

- Structures and functioning of the human brain, at present the subject of the hugeEuropean FET Flagship Human brain project http://www.humanbrainproject.eu. What can be

learned about cognition, intelligence, and our epistemological and ontological premises within

the framework of info-computational naturalism? Given that our brains and nervous systems are

info-computational networks, what can we say about the mind? How do we develop

artifactually intelligent autonomous systems based on insights from natural (organic)

computing? Embodiedness of all natural phenomena including the mind:

- Biology mechanisms and origins of life: What computational problems can ourunderstanding of natural self-organisation and management of complexity help to solve? The

origins of life and connectedness between the living and the non-living world.

- Physics information physics as a project of re-formulating physics in terms ofinformation and its dynamics (computation). We lack understanding of physics at very small

and very large dimensions, and do not understand the incompatibility between quantum

mechanics and general relativity. Matter and energy as we know constitute only 4% of what we

see in the universe the remaining 96% contains 21% dark matter and 75% dark energy. Caninformational reconceptualisation of physics help to explain this discrepancy? Do we need to

take into account observer dependence of information generation, including scientific

knowledge? Theories of emergent phenomena on different scales defined informationally:

- Complexity. In a complex system, what we see is dependent on where we are and whatsort of interaction is used to study the system. Generative Models how does the complexity

arise? Evolution is the most well-known generative mechanism, with complexity arising from

simplicity by the self-organisation of informational structures. Complex behaviour can emerge


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from simple generators! Self-star properties in organic systems: self-organisation, self-

configuration(auto-configuration),self-optimisation(automated optimisation),self-repair(self-

healing), self-protection (automated computer security), self-explaining, and self-awareness

(context-awareness) all are part of autopoiesis. Complex adaptive artificial systems are

studied inspired by biological systems.

- Modelling and simulation understood as info-computation. We are used to studyinglinear systems which possess decomposibility - Modelled by Analysis Top-down Global

(Reductionism) However, non-linear systems behave as a whole and are appropriately modelled

by synthesis (integration) - bottom-up, distributed, networked). Here, instead of analytical

methods, Holism and System approaches apply.

- Agent-based Models. An agent-based model (ABM) is a computational model forsimulating the actions and interactions of autonomous individuals in a network, with a view to

assessing their effects on the system as a whole. It combines elements of game theory, complex

systems, emergence, computational sociology, multi agent systems, and evolutionary

programming. Semiotics distinguishes between first person second person third person

accounts, and agent-based models correspond to first-person accounts (Simeonov, 2013)

- Computing nature. Along with the study of biological and other complex phenomenawithin the info-computationalist framework, a lot of work remains on the modelling of natural

phenomena based on understanding of the universe as a network of info-computational

processes. Continuous and discrete, analogue and digital computing are all parts of the

computing universe and should be studied, understood and modelled. Understanding of

evolution as an info-computational, morphodynamic process based on self-structuring of

information through morphological computation:

- Information (for an agent) From the difference that makes the difference for an agent -unification as synthesis (integration of information) and search as differentiation (Dodig-

Crnkovic, 2006). The meaning of the concept information is the resolution of categorical

opposition of one-and-many.(Schroeder, 2013a) (Schroeder, 2013b)

- Computation as (natural) information processing aComputing Nature project such asdefined in (Zenil, 2012) (Dodig-Crnkovic & Giovagnoli, 2013) (Dodig-Crnkovic, 2011b)

(Stepney, 2008; Stepney et al., 2005, 2006) and (MacLennan, 2004).

Conclusion

This article presents a new understanding of knowledge as a natural phenomenon, based on

an info-computational approach. The idea is to provide stable methodological and practical


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grounds for the existing approaches to knowledge and to complement them by new insights into

the phenomenon of knowledge. It may help to resolve old epistemological problems such as:

The extent of knowledge(how much is possible to know) by pointing to info-computational

and evolutionary origins of (agent-dependent) knowledge.

The sources of knowledge (empirical experience vs. intellect), which are informational

structures with computational dynamics, both in the agent itself (embodiment, embeddedness),

and in the world understood as potential information, which for an agent is actualised through

interactions.

The nature of knowledge,traditionally the question about how the concept of knowledge

should be defined, in the info-computational framework becomes transformed into the question:

what in the physical world is knowledge?

As we have seen from its applications, the info-computational approach to knowledge

generation can contribute both to epistemology and to knowledge management and the

understanding of learning.

Finally, the info-computational approach can contribute to rethinking cognition as a self-

organising bio-chemical life process in humans and other living beings. Thus we can start to

learn how to adequately model living systems which have traditionally been impossible to

effectively frame theoretically, simulate and study in their full complexity. (Dodig-Crnkovic &Mller, 2011)

To conclude, let me quote Feynman fromThe Character of Physical Law:

Our imagination is stretched to the utmost, not, as in fiction, to imagine things which

are not really there, but just to comprehend those things which are there.

(Feynman, 1965)

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