1
CiE 2014: Language, Life, Limits
Life-Organizing Processes of Info-Computation
Gordana Dodig-Crnkovic
“There are fragments of knowledge which, put together, could support a picture of genetic
communication in multicellular organisms. However, in the absence of a proper theory, some were
discarded as meaningless and others were studied separately. They were never put together and
considered as parts of one picture” Eshel Ben-Jacob
Introduction
We are in a phase transition in the history of knowledge production and particularly in the
production of scientific knowledge, as a result of information-communication technology. Knowledge
production increases in both rate and interconnections between different fields and the growth of new
hybrid research fileds such as computational biology with computational-(biomodeling, evolutionary
biology, genomics, genetics, neuroscience, pharmacology), bioinformatics and similar. This
development was facilitated not only by fast and easy global communications between scientists of all
different proveniences, but also by communication between computer networks and automated
equipment that is forming a new layer of information-processing structures. This development hugely
enhances the extended cognition of humanity. As a consequence the knowledge gap between research
fields steadily decreases and cross-fertilization of ideas across sciences blossoms.
New concepts have become dominant and among them information (and its dual term
computation) have become central across knowledge fields. Scientific notions can be seen as the nodes
in vast networks of related ideas. Information has especially complex networks of associated meanings
as it is used ubiquitously and in variety of contexts. On a basic level where we are primarily interested
in mechanisms of information processes, information can be defined as a structure. This covers even
the classical dictionary idea of information as facts or “what is conveyed or represented by a particular
arrangement or sequence of things” (Oxford dictionary). In what follows information will be defined
by combining Bateson and Hewitt definitions. Computation on a fundamental level is the dynamics of
information. Being relational in nature, information presupposes an agent for whom it is “a difference
that makes a difference” (Bateson). An agent can be a simple physical entity such as molecule, or it
could be a complex biological system such as cell or a multicellular living being.
Information is central for all forms of life and its dynamics (computation) provides living organisms
with necessary means for self-generation and self-maintenance by self-organization (Kauffman) and
autopoiesis (Maturana and Varela). In this article I present a framework in which life is understood as
a process of cognition that is computation unfolding in the informational structure. Cognition1 [latin
co + gnoscere, to know] includes perception, awareness, intuition, reasoning and judgment –
processes that Maturana and Varela identify with life. Currently we lack a common understanding of
the process of cognition and its evolution from the first living organisms to humans and human
1
Interestingly, Kant’s notion of Erkenntniß is translated both as knowledge and as cognition.
2
CiE 2014: Language, Life, Limits
societies with social cognition. One of the steps towards better understanding of cognition is its
computational modelling with distributed computation between agents. Recent research on bacteria
and their swarms indicate that the beasic elements of cognition in the form of adaptivity to the
environment can be found even in such very simple organisms. Bacteria communicate, exchange
information, “compute” their own next state. They possess “chemical” language which helps them
identify the self, the kin and the others.
Christopher M.Waters and Bonnie L. Bassler (2005) Quorum Sensing: Cell-to-Cell
Communication in Bacteria. Annual Review of Cell and Developmental Biology. Vol.
21: 319-346
Nadell CD, Bucci V, Drescher K, Levin SA, Bassler BL, Xavier JB. (2013) Cutting through the complexity of
cell collectives. Proc R Soc B 280: 20122770. http://dx.doi.org/10.1098/rspb.2012.2770
Rutherford ST, Bassler BL. (2012) Bacterial quorum sensing: its role in virulence and possibilities
for its control. Cold Spring Harb Perspect Med. 2 pii: a012427.
Bassler BL. (2010) Small cells--big future. Mol Biol Cell. 21: 3786-3787.
Mehta P, Goyal S, Long T, Bassler BL, Wingreen NS. (2009) Information processing and signal
integration in bacterial quorum sensing. Mol Syst Biol. 5: 325.
Ng WL, Bassler BL. (2009) Bacterial quorum-sensing network architectures. Annu Rev Genet. 43:
197-222. PubMed
Bray, Dennis () Wetware: A Computer in Every Living Cell
Alexei Kurakin, “Self-Organization vs. Watchmaker: Stochastic Dynamics of Cellular
Organization,” Biological Chemistry, 2005, 386: 247–254; p. 250.
“Defying the ideas of design and clockwork determinism, a leitmotiv of the latest experimental research are the
ubiquitous observations of self-organization and stochasticity that appears to emerge as general principles
underlying the dynamics and organization of life at all scales. Stochastic molecular motors, stochastic enzymes,
stochastic self-organization of cytoskeleton structures, sub-cellular and sub-nuclear compartments, stochastic
self-organization of macromolecular complexes mediating transcription, DNA repair and chromatin
structure/function, stochastic gene expression and stochastic cellular responses are poorly compatible with the
familiar notions of design, programs, instructions and codes, and their systematic appearance is a call for active
efforts to loosen the grip of the conventional mechanistic models and concepts in a search for an alternative and
more adequate description of life systems.” (3) Kurakin, above
“In installment five, I began exploring an alternative view of life as an emergent phenomenon within an overall
framework of physical emergence that has been developed by investigators in condensed-matter physics over the
past half century.” Barham
”No one has discussed the implications of the just-in-time self-organization of cellular structures with greater
emphasis, eloquence, and profundity than Alexei Kurakin.”
3
CiE 2014: Language, Life, Limits
L.Landweber, L.Kari. The evolution of cellular computing: nature's solution to a computational
problem. Biosystems 52(1999)
L.Kari, L.F.Landweber. Computational power of gene rearrangement. Proc. DNA Computing 5,
DIMACS Series, 54(2000)
L.Kari, J.Kari, L.Landweber. Reversible molecular computation in ciliates. In Jewels are
Forever, Springer-Verlag (1999)
Mark J. Schnitzer Biological computation: Amazing algorithms Nature 416, 683-683
doi:10.1038/416683a
Ramiz Daniel, Jacob R. Rubens, Rahul Sarpeshkar & Timothy K. Lu (15 May 2013) Synthetic analog
computation in living cells Nature 497, 619-623 doi:10.1038/nature12148
Yaakov Benenson, Tamar Paz-Elizur, Rivka Adar, Ehud Keinan, Zvi Livneh & + et al.
(22 November 2001) Programmable and autonomous computing machine made of
biomolecules Nature 414, 430-434 doi:10.1038/35106533
Lila Kari, Grzegorz Rozenberg (October 2008). "The Many Facets of Natural
Computing". Communications of the ACM 51: pp.72–83.
Leandro Nunes de Castro (March 2007). "Fundamentals of Natural
Computing: An Overview". Physics of Life Reviews 4: pp.1–36.
based on template guided recombination – this is state transition table of
an automata. However, while classical automata are deterministic (or
indeterministic in a weak sense of choice between known states) biological
systems are indeterministic in a strong sense, that is their transition can be to
states which are not known in advance (new states for the system). As this
kind of transition does not happen very often, such indeterminism leads to
development. It is based on the fact that living organisms are open systems,
and environment is an unlimited source of inputs.
“Other approaches to cellular computing include developing an in vivo
programmable and autonomous finite-state automaton with E. coli,[45] and
designing and constructing in vivo cellular logic gates and genetic circuits that
harness the cell's existing biochemical processes (see for example [46][47]).”
Nakagawa, H., Sakamoto, K., Sakakibara, Y. Development of an in vivo computer
based on Escherichia Coli. In LNCS 3892, pages 203-212, Springer, 2006:
“Other approaches to cellular computing include developing an in vivo
programmable and autonomous finite-state automaton with E. coli,[45] and
4
CiE 2014: Language, Life, Limits
designing and constructing in vivo cellular logic gates and genetic circuits that
harness the cell's existing biochemical processes (see for example [46][47]).”
“Our fundamental idea to develop a programmable and autonomous
finite-state automata on E. coli is that we first encode an input string into
one plasmid, encode state-transition functions into the other plasmid, and
introduce those two plasmids into an E. coli cell by electroporation. Second,
we execute a protein-synthesis process in E. coli combined with four-base
codon techniques to simulate a computation (accepting) process of finite
automata, which has been proposed for in vitro translation-based
computations in [8]. This approach enables us to develop a programmable
in vivo computer by simply replacing a plasmid encoding a state-transition
function with others. Further, our in vivo finite automata are autonomous
because the protein-synthesis process is autonomously executed in the
living E. coli cell. We show some successful experiments to run an in vivo
finite-state automaton on E. coli.”
Zabet NR, Hone ANW, Chu DF Design principles of transcriptional logic circuits. In
Artificial Life XII Proceedings of the Twelfth International Conference on the
Synthesis and Simulation of Living Systems, pages 186-193. MIT Press,
August 2010.
Zabet NR Towards Modular, Scalable and Optimal Design of Transcriptional Logic
Systems.2010.
At a minimum, one must distinguish between “self-assembly” (or “self-ordering”), which
may be described in “downhill” (exergonic) terms, and “self-organization,” which cannot be
adequately so described. On this important topic, see:
Julianne D. Halley and David A. Winkler, “Consistent Concepts of Self-Organization and SelfAssembly,” Complexity, 2008, 14(2): 10–17.
Shapiro, J. A. (2011). Evolution: A View from the 21st Century. Upper Saddle River, New
Jersey, FT Press Science. ISBN 978-0132780933.
Living cells and organisms are cognitive (sentient) entities that act and interact
purposefully to ensure survival, growth, and proliferation. They possess corresponding
sensory, communication, information processing, and decision-making capabilities. (p.
143)
5
CiE 2014: Language, Life, Limits
One of the most profound lessons from the past six decades of molecular cell biology is that all
aspects of cell functioning and cellular biochemistry are subject to regulation. (p.69)
Without an elaborate sensory apparatus to pick up signals about chemicals in the environment
(nutrients, poisons, signals emitted by other cells) or to keep track of intracellular events (DNA
replication, organelle growth, oxidative damage), a cell’s opportunity to proliferate or contribute to
whole-organism development would be severely restricted. Life requires cognition at all levels [40,
41].” Shapiro p. 7
“The selected cases just described are examples where molecular biology has identified specific
components of cell sensing, information transfer, and decision-making processes. In other words, we
have numerous precise molecular descriptions of cell cognition, which range all the way from
bacterial nutrition to mammalian cell biology and development. The cognitive, informatic view of how
living cells operate and utilize their genomes is radically different from the genetic determinism
perspective articulated most succinctly, in the last century, by Francis Crick’s famous “Central Dogma
of Molecular Biology.” “ Shapiro p. 24
Living cells do not operate blindly. They continually acquire
information about the external environment and monitor their internal
operations. Then they use this information to guide the processes
essential to survival, growth, and reproduction. Cells constantly
adjust their metabolism to available nutrients, control their progress
through the cell cycle to make sure that all progeny are complete at
the time of division, repair damage as it occurs [34], and interact
appropriately with other cells. In a multicellular context, they even
undergo programmed cell death when suicide is beneficial to the
entire population or to the multicellular organism as a whole [35]
[36–39]. Without an elaborate sensory apparatus to pick up signals
about chemicals in the environment (nutrients, poisons, signals emitted
by other cells) or to keep track of intracellular events (DNA replication,
organelle growth, oxidative damage), a cell’s opportunity to
proliferate or contribute to whole-organism development would be
severely restricted. Life requires cognition at all levels [40, 41].
Bray, D. (2009). Wetware: A Computer in Every Living Cell New Haven, CT, Yale University
Press. ISBN 978-0300141733.
LUCA CARDELLI and GIANLUIGI ZAVATTARO Turing universality
of the Biochemical Ground Form, Mathematical Structures in Computer Science /
Volume 20 / Special Issue 01 / February 2010, pp 45-73
Zavattaro’s research was partially funded by ‘Progetto Strategico’ CompReNDe: Compositional and executable Representations
of Nano Devices.
BEN-JACOB & ROGLIC
Roglic, Darko in Information and Computation
6
CiE 2014: Language, Life, Limits
SUPER-RECURSIVE FEATURES OF EVOLUTIONARY PROCESSES AND THE MODELS FOR COMPUTATIONAL
EVOLUTION
Ben-Jacob, E. (1998) Bacterial wisdom, Godel’s theorem and creative genomic webs. Physica A 248, 57-76
Ben Jacob, E. (2003) Bacterial Self-Organization: Co-Enhancement of Complexification and Adaptability in a Dynamic
Environment Phil. Trans. R. Soc. Lond. A, 361:1283-1312
Ben-Jacob, E., Becker, I., Y. Shapira. Y. (2004) Bacteria Linguistic Communication and Social Intelligence. Trends in
Microbiology, Vol 12/8 pp 366-372
Ben Jacob, E. and Shapira, Y. (2005) Meaning-Based Natural Intelligence Vs. Information Based Artificial Intelligence.
Cradle of Creativity
Ben Jacob, E., Shapira, Y., Tauber, A.I. (2006) Seeking the Foundations of Cognition in Bacteria Physica A vol 359 ; 495524,
Ben Jacob, E. (2008). Social behaviour of bacteria: from physics to complex organization. The European Physical Journal B
Explanation here why simplest processes are 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. Apart from the
importance of understanding cognition in order to be able to help people with cognitive impairments,
we are interested in understanding cognition in living agents 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, controlling and optimizing social infrastructures 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 agent’s 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 to some degree, 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 (DodigCrnkovic, 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.
EXPLANATION OF (NATURAL) COMPUTING
Present account is a short presentation of a more extensive work. For further reading on the details
of the framework and its different aspects, interested reader is referred to the original articles as
follows.
The description of the conceptual framework of info-computationalism can be found in (DodigCrnkovic & Müller, 2011) (Dodig-Crnkovic, 2009) (Dodig-Crnkovic, 2006). The relationship between
natural computing (such as biocomputing, DNA-computing, chemical computing, quantum
7
CiE 2014: Language, Life, Limits
computing, social computing, etc) and the traditional Turing machine model of computation is
elaborated in (Dodig-Crnkovic, 2012a)(Dodig-Crnkovic, 2011a) (Dodig-Crnkovic, 2011b) (DodigCrnkovic, 2010a). The constructing/generation/production of knowledge within an infocomputational framework is discussed in (Dodig-Crnkovic, 2007) (Dodig-Crnkovic, 2010b) (DodigCrnkovic, 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 selfstructuring (self-organisation) is addressed in (Dodig-Crnkovic, 2012b) (Dodig-Crnkovic, 2012c).
The Computing Nature and Minimal Cognition
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 (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 – for cognitive agents.
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 (Chaitin, 2007). It builds on two basic complementary concepts: information
(structure) and computation (the dynamics of informational structure) as described in (DodigCrnkovic, 2011a) 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
Computations 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).
3 This amounts to computation being equivalent to causality. Note the difference between causality
and determinism. Computation is not always deterministic but it is necessarily causal. See Collier J.,
Information, Causation And Computation Chapter 4 in (Dodig-Crnkovic & Burgin, 2011)
2
8
CiE 2014: Language, Life, Limits
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” (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 Garzón in (Pombo, O., Torres J.M., Symons J., 2012) p. 121. In his Anticipatory
systems (Rosen 1985) claims:
I cast about for possible biological instances of control of behaviour 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.
It is important to notice that cognition in this model does not presuppose or imply consciousness.
There is no reason to ascribe consciousness to the elementary forms of cognition, because we have no
idea what that would be. On the contrary, primitive cognition as a process that helps organisms
survive and adequately use information from the world is a productive scientific concept.
Informational Structure of Reality for an Agent
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 Kant’s 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 Bateson’s definition of information as a difference is the widely cited one 6, there is a
more general definition 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. (Hewitt, 2007) (Italics added)
Maturana 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.
4
Compare 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.
6 In 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.
5
9
CiE 2014: Language, Life, Limits
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 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 (DodigCrnkovic & Mueller, 2009).
As a consequence of the adoption of Hewitt’s 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-subjectively agreed, negotiated common world-view.
Life as 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. The world is
practically inexhaustible and extremely complex and exceeds by all accounts what cognizing agent
can take in and cope with. Cognition is then the mechanism that enables cognizing agents to makes
sense of the world and uses it as a resource for survival at the same time avoiding its threats and
dangers for life.
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). HERE ADD DEEP LEARNING. Data, information,
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). COGNITION IS NOT CONNECTED WITH CONSCIOUSNES EVEN HERE.
Consciousness is a process that results from integration of multiple cognitive processes like vision,
auditory and other sensory-motor perception. “Time longer than one second is needed to synthesize
conscious experience” (Ballard, 2002)
The advantage of computational approaches to modelling of knowledge generation and learning
compared to pure conceptual analysis typical of traditional epistemology is their testability. Daniel
Dennett declared in a talk at the International Computers and Philosophy Conference, Laval, France
Agency has been explored in biological systems by Stuart Kauffman, see (Kauffman,
2000)(Kauffman, 1995)(Kauffman, 1993)
8 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.
7
10
CiE 2014: Language, Life, Limits
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
agent’s <representation> of the world. FOR EXAMPLE IN DEEP LEARNING (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 behaviour 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 behaviour.
This interface theory of perception offers a framework, motivated by evolution, to
guide research in object categorization.
Thus, perception cannot function 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. This is true in general even for
simplest physical non-cognitive agents.
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. Noë, in an enactive approach to perception, emphasises the
role of evolution of sensorimotor abilities in living systems that can be connected with the changing
informational interface between an agent and the world, and thus increasing information exchange
and the complexity of an organism’s information processing structures. (Noë, 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 Brier’s
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. Important connection goes via Terrence Deacons and Steven Pinkers ideas
about language.
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-Fashioned Artificial
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: (THIS SHOULD BE BETTER
EXPLAINED)
What kinds of programs are responsible for behaviour 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 behaviour and a
huge proportion of human behaviour: Perception, practiced motor behaviour, fluent
11
CiE 2014: Language, Life, Limits
linguistic behaviour, 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.
Smolensky’s ideas about virtual machines running intuitive information processes were developed
by Sloman, who characterises the human mind as a virtual machine running on the brain hardware,
(Sloman, 2002). He also addresses the symbol grounding problem, that is the question of how symbols
acquire meaning through sub-symbolic signal processing.
Relational Character of Information: Model-Dependence of Observations and Observer
(Species)-Dependence of (Cognitive) Models
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 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) (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 (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) 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 perturbation caused
by measurement sufficient to switch the system to a different attractor).
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
12
CiE 2014: Language, Life, Limits
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 but can work pretty well in practice.
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 Glasersfeld’s 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 (DodigCrnkovic, 2007) (Dodig-Crnkovic & Müller, 2011). “The subject” in the above can be any living
organism or indeed an artificial cognitive agent too.
Information Self-Structuring and Knowledge Generation through Morphological Computation
When talking about computational models of biological phenomena, it is important to emphasize 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 development of 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,
Bäck, & Kok, 2012) (Burgin, 2005) (MacLennan, 2004) (Wegner, 1998) (Hewitt, 2012) (Abramsky, 2008).
Computation as information processing should not be identified with the notion of computation
in classical cognitive science based on notions of input–output and representations in the sense of the
Turing machine model. It is important to recognise that connectionist models (e.g. neural networks) as
well as dynamic systems models are equally computational as they are also based on information processing
(Scheutz, 2002) (Dodig-Crnkovic, 2009).
The basis for the capacity to acquire knowledge (I SHOULD EXPLAIN IN MORE DETAIL IN
WHAT WAY SOME STRUCTURE PRESENTS KNOWLEDGE PROGRAMME) 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 higherorder phenomenon (e.g. a molecule).
13
CiE 2014: Language, Life, Limits
In morphological computing, the modelling of an agent’s 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 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. HERE DEEP LEARNING SHOULD BE ADDED AND NEURONS THAT GROW
CONNECTIONS WITH LEARNING WHILE CONNECTIONS GET PRUNED IF NOT USED.
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 evolutionary shaping between an organism and its
environment 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. Fundamental
computational processes, which express changes of informational structures, are implementation of
physical laws. (Dodig Crnkovic 2008)
The environment provides an organism with 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, 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
agent’s 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.
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.
Here 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.
10 Landauer, R. 1991, “Information is Physical'”, Physics Today 44, 23 - 29.
Landauer, R. 1996, “The Physical Nature of Information” Physics Letter (A 217), 188
9
14
CiE 2014: Language, Life, Limits
In anticipation of some possible criticisms of the approach, which requires generalization of the
concept of cognition to all living beings, I would like to say the following. 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 its naturalistic approach. It shows how cognitive capacities are a matter of degree and how they
slowly and successively develop with evolution. The proposed approach suggests mechanisms that
can be tested by simulations and they agree with our best current scientific knowledge about
evoliution, learning and cognition (LESLIE VALIANT PAC) 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. It should be combined with the insights on the nature of language such
as presented by Terry Deacon and behaviors of social cognition.
From the point of view of info-computationalism, a mechanism behind the aforementioned
Sloman’s 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.
HERE IT IS NOT COMPLETELY CLEAR WHAT IS THE CONNECTION BETWEEN
DIFFERENT KINDS OF KNOWLEDGE AND MORPHOLOGICAL COMPUTATION.
Life by Self-organisation and Autopoiesis of Open Systems in the Environment
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 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 (emphasis added)
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 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
15
CiE 2014: Language, Life, Limits
the whole, that is as an instrument, or organ... The part must be an organ producing
the other parts—each, consequently, reciprocally producing the others... Only under
these conditions and upon these terms can such a product be an organized and selforganized being, and, as such, be called a physical end.
http://oll.libertyfund.org/index.php?option=com_staticxt&staticfile=show.php%3Fti
tle=1217&layout=html Immanuel Kant, The Critique of Judgement [1892]
Today we may 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. 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. SELF-ORGANIZATION IS MORPHOLOGICAL COMPUTING. 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 Varela’s 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 selfreflective, 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 in terms of resources) 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 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).
Open Questions and Future Research
Promises of info-computational research program 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)
16
CiE 2014: Language, Life, Limits
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 huge European 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: (JEFF HAWKINS, LEARNING FROM NEUROSCIENCE)
Biology – mechanisms and origins of life: What computational problems can our understanding
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 of information 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 constitute only 4% of what we see in the universe – the remaining 96%
contains 21% dark matter and 75% dark energy. Can informational 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 what sort 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 selforganisation of informational structures. Complex behaviour can emerge from simple generators! Selfstar properties in organic systems: self-organisation, self-configuration (auto-configuration), selfoptimisation (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 studying linear 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 for simulating 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 phenomena within 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-andmany. (Schroeder, 2013a) (Schroeder, 2013b)
17
CiE 2014: Language, Life, Limits
Computation as (natural) information processing – a Computing Nature project such as defined 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 infocomputational approach. The idea is to provide stable methodological and practical 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. reasoning or other local information
post-processing within an agent), which are informational structures with computational
dynamics, both in the world understood as potential information, and in the agent itself
(embodiment, embeddedness), 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? Knowledge is seen as a result of
learning that produces in informational structure on which it is possible to act.
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 & Müller, 2011)
To conclude, let me quote Feynman from The 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)
References
Abramsky, S. (2008). Information, Processes and Games. In J. Benthem van & P. Adriaans (Eds.),
Philosophy of Information (pp. 483–549). Amsterdam, The Netherlands: North Holland.
Ballard, D. (2002). Our perception of the world has to be an illusion. Journal of Consciousness Studies, 9,
54–71.
Bateson, G. (1972). Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and
Epistemology (pp. 448–466). University Of Chicago Press.
Brier, S. (2013). Cybersemiotics: A New Foundation for Transdisciplinary Theory of Information,
Cognition, Meaningful Communication and the Interaction Between Nature and Culture. Integral
Review, 9(2), 220–262.
Burgin, M. (2005). Super-Recursive Algorithms (pp. 1–320). New York: Springer-Verlag New York Inc.
Burgin, M., & Dodig-Crnkovic, G. (2013). From the Closed Classical Algorithmic Universe to an Open
World of Algorithmic Constellations. In G. and Dodig-Crnkovic & Giovagnoli R. (Eds.), Computing
Nature (pp. 241–253). Berlin Heidelberg: Springer.
Chaitin, G. (2007). Epistemology as Information Theory: From Leibniz to Ω. In G. Dodig Crnkovic
(Ed.), Computation, Information, Cognition – The Nexus and The Liminal (pp. 2–17). Newcastle UK:
Cambridge Scholars Pub.
Clark, A. (1989). Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing.
Cambridge, MA: MIT Press.
Dodig-Crnkovic, G. (2006). Investigations into Information Semantics and Ethics of Computing (pp. 1–133).
Västerås, Sweden: Mälardalen University Press. Retrieved from http://mdh.divaportal.org/smash/get/diva2:120541/FULLTEXT01
18
CiE 2014: Language, Life, Limits
Dodig-Crnkovic, G. (2007). Knowledge Generation as Natural Computation. In N. Callaos (Ed.),
Proceedings
of International Conference on
Knowledge Generation.
Retrieved from
http://www.mrtc.mdh.se/index.php?choice=publications&id=1400
Dodig-Crnkovic, G. (2008). Knowledge Generation as Natural Computation. Journal of Systemics,
Cybernetics and Informatics, 6(2), 12–16.
Dodig-Crnkovic, G. (2009). Information and Computation Nets. Investigations into Info-computational
World. Information and Computation (pp. 1–96). Saarbrucken: Vdm Verlag.
Dodig-Crnkovic, G. (2010a). Biological Information and Natural Computation. In J. Vallverdú (Ed.), .
Hershey PA: Information Science Reference.
Dodig-Crnkovic, G. (2010b). Constructivist Research and Info-Computational Knowledge Generation.
In L. Magnani, W. Carnielli, & C. Pizzi (Eds.), MODEL-BASED REASONING IN SCIENCE AND
TECHNOLOGY Abduction, Logic, and Computational Discovery (pp. 359–380). Heidelberg/Berlin:
Springer.
Dodig-Crnkovic, G. (2010c). The Cybersemiotics and Info-Computationalist Research Programmes as
Platforms for Knowledge Production in Organisms and Machines. Entropy, 12(4), 878–901.
Dodig-Crnkovic, G. (2011a). Dynamics of Information as Natural Computation. Information, 2(3), 460–
477.
Dodig-Crnkovic, G. (2011b). Significance of Models of Computation from Turing Model to Natural
Computation. Minds and Machines,, 21(2), 301–322.
Dodig-Crnkovic, G. (2012a). Physical Computation as Dynamics of Form that Glues Everything
Together. Information, 3(2), 204–218.
Dodig-Crnkovic, G. (2012b). The Info-computational Nature of Morphological Computing. In V. C.
Müller (Ed.), Theory and Philosophy of Artificial Intelligence (SAPERE., pp. 59–68). Berlin: Springer.
Dodig-Crnkovic, G. (2012c). Info-computationalism and Morphological Computing of Informational
Structure. In P. L. Simeonov, L. S. Smith, & A. C. Ehresmann (Eds.), Integral Biomathics. Tracing the
Road to Reality. Berlin, Heidelberg.
Dodig-Crnkovic, G., & Burgin, M. (2011). Information and Computation (pp. 1–350). Singapore: World
Scientific Pub Co Inc.
Dodig-Crnkovic, G., & Giovagnoli, R. (2013). Computing Nature. Berlin Heidelberg: Springer.
Dodig-Crnkovic, G., & Hofkirchner, W. (2011). Floridi’s Open Problems in Philosophy of Information,
Ten Years After. Information, 2(2), 327–359. doi:10.3390/inf10x000x
Dodig-Crnkovic, G., & Mueller, V. (2009). A Dialogue Concerning Two World Systems: InfoComputational vs. Mechanistic. (G. Dodig Crnkovic & M. Burgin, Eds.)Information and Computation.
Singapore: World Scientific Pub Co Inc. Retrieved from http://arxiv.org/abs/0910.5001
Dodig-Crnkovic, G., & Müller, V. (2011). A Dialogue Concerning Two World Systems: InfoComputational vs. Mechanistic. In G. and Dodig Crnkovic & M. Burgin (Eds.), Information and
Computation (pp. 149–184). World Scientific.
Feynman, R. P. (1965). The Character of Physical Law, November 1964 Cornell Lectures (pp. 57–8). BBC.
Floridi, L. (2003). Informational realism. In Selected papers from conference on Computers and philosophy Volume 37 (pp. 7–12). Darlinghurst, Australia, Australia: Australian Computer Society, Inc.
Floridi, L. (2008). A defense of informational structural realism. Synthese, 161(2), 219–253.
Floridi, L. (2009). Against digital ontology. Synthese, 168(1), 151–178.
Foerster, H. von. (2003). Understanding Understanding: Essays on Cybernetics and Cognition. Springer
Berlin Heidelberg.
Gell-Mann, M. (1994). The Quark and the Jaguar: Adventures in the Simple and the Complex. New York:
Freeman/ Owl Book.
Glasersfeld, E. von. (1995). Radical constructivism: A way of knowing and learning. London: Falmer Press.
Goertzel, B. (1993). The Evolving Mind. New York: Gordon and Breach.
Harms, W. F. (2006). Naturalizing Epistemology: Prospectus 2006. Biological Theory, 1, 23–24.
Hewitt, C. (2007). What Is Commitment? Physical, Organizational, and Social. In P. Noriega, J.
Vazquez-Salceda, G. Boella, O. Boissier, & V. Dign (Eds.), Coordination, Organizations, Institutions,
and Norms in Agent Systems II (pp. 293–307). Berlin, Heidelberg: Springer-Verlag.
Hewitt, C. (2012). What is computation? Actor Model versus Turing’s Model. In H. Zenil (Ed.), A
Computable Universe, Understanding Computation & Exploring Na-ture As Computation. World
Scientific Publishing Company/Imperial College Press.
19
CiE 2014: Language, Life, Limits
Heylighen, F., & Joslyn, C. (2001). Encyclopedia of Physical Science & Technology. 4 (3rd ed.). In R. A.
Meyers (Ed.), (pp. 155–170). Academic Press. New York.
Hoffman, D. (2009). The interface theory of perception: Natural selection drives true perception to
swift extinction. In M. J. Dickinson, S., Leonardis, A., Schiele, B., & Tarr (Ed.), Object categorization:
Computer and human vision perspectives (pp. 148–166). Cambridge, UK: Cambridge University Press.
Kampis, G. (1991). Self-modifying systems in biology and cognitive science: a new framework for dynamics,
information, and complexity (pp. 1–564). Amsterdam: Pergamon Press.
Kaneko, K., & Tsuda, I. (2001). Complex systems: chaos and beyond. A constructive approach with
applications in life sciences. Berlin, Heidelberg: Springer.
Kauffman, S. (1993). Origins of Order: Self-Organization and Selection in Evolution. Oxford University
Press.
Kauffman, S. (1995). At Home in the Universe: The Search for Laws of Self-Organization and Complexity.
Oxford University Press.
Kauffman, S. (2000). Investigations. New York. London: Oxford University Press.
MacLennan, B. J. (2004). Natural computation and non-Turing models of computation. Theoretical
computer science., 317(1). Retrieved from http://www.worldcat.org/title/natural-computation-andnon-turing-models-of-computation/oclc/97708146&referer=brief_results
Maturana, H. (1970). Biology of Cognition. Research Report BCL 9. 1970, Biological Computer Laboratory:
Urbana, IL. (p. BCL Report 9.0.). Illinois: Defense Technical Information Center. Retrieved from
http://www.worldcat.org/title/biology-of-cognition/oclc/227616284&referer=brief_results
Maturana, H. (2002). Autopoiesis, Structural Coupling and Cognition: A history of these and other
notions in the biology of cognition. Cybernetics & Human Knowing, 9(3-4), 5–34.
Maturana, H. (2007). Systemic versus genetic determination. Constructivist Foundations, 3, 21–26.
Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: the realization of the living. Dordrecht
Holland: D. Reidel Pub. Co.
Maturana, H., & Varela, F. (1992). The Tree of Knowledge. Shambala.
Minsky, M. (1986). The Society of Mind. New York: Simon and Schuster.
Minsky, M. (2011). Interior Grounding, Reflection, and Self-Consciousness. In G. Dodig-Crnkovic &
M. Burgin (Eds.), Information and Computation (pp. 287–305). Singapore: World Scientific Pub Co.
Noë, A. (2004). Action in Perception. Cambridge, MA: MIT Press.
Nørretranders, T. (1999). The User Illusion: Cutting Consciousness Down to Size. New York: Viking.
Okasha, S. (2005). Review of William F. Harms, Information and Meaning in Evolutionary Processes.
Notre Dame Philosophical Reviews, 12.
Pfeifer, R., & Bongard, J. (2006). How the body shapes the way we think – A new view of intelligence. MIT
Press.
Pfeifer, R., Lungarella, M., & Iida, F. (2007). Self-organization, embodiment, and biologically inspired
robotics. Science, 318, 1088–1093.
Pombo, O., Torres J.M., Symons J., R. S. (Ed.). (2012). Special Sciences and the Unity of Science (Logic,
Epi.). Springer.
Popper, K. (1999). All Life is Problem Solving. Routledge.
Prigogine, I., & Stengers, I. (1984). Order out of Chaos: Man’s new dialogue with nature. Flamingo.
Rosen, R. (1985). Anticipatory Systems. Pergamon Press.
Rozenberg, G., Bäck, T., & Kok, J. N. (Eds.). (2012). Handbook of Natural Computing. Berlin Heidelberg:
Springer.
Rössler, O. (1998). Endophysics: the world as an interface. Singapore-New Jersey-London-Hong Kong:
World Scientific.
Sayre, K. M. (1976). Cybernetics and the Philosophy of Mind. London: Routledge & Kegan Paul.
Scheutz, M. (2002). Computationalism new directions (pp. 1–223). Cambridge Mass.: MIT Press. Retrieved
from
http://www.worldcat.org/title/computationalism-newdirections/oclc/51969469&referer=brief_results
Schroeder, M. (2013a). Dualism of Selective and Structural Manifestations of Information in Modelling
of Information Dynamics. In G. Dodig-Crnkovic & R. Giovagnoli (Eds.), Computing Nature
(SAPERE 7., pp. 125–137). Berlin, Heidelberg: Springer.
20
CiE 2014: Language, Life, Limits
Schroeder, M. (2013b). Complexity of Complexity. In E. Cohen, Eli and Boyd (Ed.), Proceedings of the
Informing Science and Information Technology Education Conference In SITE 2013 University Fernando
Pessoa Porto, Portugal. Santa Rosa, CA, USA: Informing Science Institute.
Simeonov, P. L. (2013). On some recent insights in Integral Biomathics. J. Prog. Biophys. Mol. Biol.,
Forthcomin.
Sloman, A. (2002). The Irrelevance of Turing machines to AI. In Computationalism – New Directions (M.
Scheutz, Ed.) (pp. 87–127). Cambridge, Mass: MIT Press.
Sloman,
A.
(2013).
Meta-morphogenesis.
Retrieved
from
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html.
Smolensky, P. (1988). On the proper treatment of connectionism. Behavioural and Brain Sciences, 11, 1–
74.
Stepney, S. (2008). The neglected pillar of material computation. Physica D: Nonlinear Phenomena,
237(9), 1157–1164.
Stepney, S., Braunstein, S. L., Clark, J. A., Tyrrell, A. M., Adamatzky, A., Smith, R. E., … Partridge, D.
(2005). Journeys in Non-Classical Computation I: A Grand Challenge for Computing Research. Int.
J. Parallel Emerg. Distr. Syst., 20, 5–19.
Stepney, S., Braunstein, S. L., Clark, J. A., Tyrrell, A. M., Adamatzky, A., Smith, R. E., … Partridge, D.
(2006). Journeys in Non-Classical Computation II: Initial Journeys and Waypoints. Int. J. Parallel
Emerg. Distr. Syst., 21, 97–125.
Stonier, T. (1997). Information and meaning: an evolutionary perspective. New York: Springer. Retrieved
from
http://www.worldcat.org/title/information-and-meaning-an-evolutionaryperspective/oclc/36696698&referer=brief_results
Van Gelder, T. and Port, R. (Ed.). (1995). Mind as motion: Explorations in the dynamics of cognition.
Cambridge Mass.: the MIT press.
Vedral, V. (2010). Decoding reality: the universe as quantum information (pp. 1–240). Oxford: Oxford
University Press.
Wegner, P. (1998). Interactive foundations of computing. Theoretical computer science., 192(2). Retrieved
from
http://www.worldcat.org/title/interactive-foundations-ofcomputing/oclc/90683418&referer=brief_results
Wheeler, J. A. (1990). Information, physics, quantum: The search for links. In W. Zurek (Ed.),
Complexity, Entropy, and the Physics of Information. Redwood City: Addison-Wesley.
Wolff, G. J. (2003). Information Compression by Multiple Alignment, Unification and Search as a
Unifying Principle in Computing and Cognition. Artificial Intelligence Review, 19, 193–230.
Wolff, G. J. (2006). Unifying Computing and Cognition: The SP Theory and its Applications.
CognitionResearch.org.uk, England.
Zeilinger, A. (2005). The message of the quantum. Nature, 438(7069), 743. doi:10.1038/438743a
Zenil, H. (2012). A Computable Universe. Understanding Computation & Exploring Nature As Computation.
(H. Zenil, Ed.). Singapore: World Scientific Publishing Company/Imperial College Press.
Zins, C., Debons, A., Beghtol, C., Buckland, M., Davis, C. H., Dodig-Crnkovic, G., … Smiraglia, R. P.
(2007). Knowledge map of information science: Implications for the future of the field. Brazilian
Journal
of
Information
Science,
(1).
Retrieved
from
http://www.mrtc.mdh.se/index.php?choice=publications&id=1398