Transcript Title of the presentation

AISB50 – Celebrating 50 years of the AISB
1st – 4th April 2014, Goldsmiths, University of London
Representation of Reality: Humans, Animals and Machines Symposium
Reality Construction
Through Info-Computation
Gordana Dodig Crnkovic
Professor of Computer Science
Mälardalen University, School of Innovation, Design and Engineering
[email protected]
Mälardalen University Sweden
REALITY
● What is reality for an agent?
● How does reality of a bacterium differ from a reality of a
human brain?
● Do we need representation in order to understand reality?
Representation of Reality: Humans, Animals and Machines Symposium
p. 3
REALITY
1 something that actually exists
Synonyms: actuality, case, materiality
Related Words: certainty, inevitability; circumstance, event, occurrence,
phenomenon; element, item, particular, thing
Near Antonyms: eventuality, possibility, potentiality, probability
Antonymsfantasy: (also phantasy), fiction, illusion
2 the fact of being or of being real
Synonyms: actuality, corporality, corporeality, reality, subsistence, thingness
Related Words: realness; presence, prevalence
Near Antonyms: absence, potentiality, virtuality
Antonyms: inexistence, nonbeing, nonexistence, nothingness, unreality
p. 4
REALITY
3 the quality of being actual
Synonyms: actuality, factuality, materiality
Related Words: authenticity, genuineness, truth, verity
Near Antonyms: fancy, fantasy, fiction, surreality
Antonyms: irreality, unreality
4 one that has a real and independent existence
Synonyms: being, substance, thing
Related Words: body, subject; material, matter, quantity, stuff
Near Antonyms: nonentity
p. 5
WHAT IS REALITY
(FOR AN AGENT)?
● When discussing cognition as a bioinformatic process of
special interest, we use the notion of agent, i.e. a system
able to act on its own behalf [1].
Agency in biological systems has been explored in [2][3].
The world (reality) as it appears to an agent depends on the
type of interaction through which the agent acquires
information and on agents own information-processing [1].
● Groups of agents communicate by exchanging messages
(information) that help them coordinate their actions based
on the (partial) information they possess and share as a
part of social cognition.
p. 6
COGNITION AS LIFE
● Agency and cognition is a property of all living organisms.
● Agents themselves can consist of networks of agents,
recursively. A single biological cell consists of network of
agents. Networks of cells form tissues that form organs that
form organisms that organize in ecologies.
● The question is how artifactual agents should be built in
order to possess different degrees of cognition and
eventually even consciousness.
Is it possible at all, for an artifactual agent to be cognitive
given that cognition in living organisms is a deeply
biologically rooted process connected to survival?
p. 7
LANGUAGE AS A TOOL
OF HIGH LEVEL COGNITION
● Increasing levels of cognition developed in living organisms
evolutionary, starting from basic automatic minimally
adaptive behaviours such as found in bacteria and even
insects (even though insects have nervous system and
brain, they lack the limbic system that controls emotional
response to physical stimuli, suggesting they don't process
physical stimuli emotionally) to increasingly complex
behaviour in higher organisms such as mammals.
p. 8
LANGUAGE AS A TOOL
OF HIGH LEVEL COGNITION
● Recent advances in natural language processing, such as
Watson computer that wins Geopardy, present examples of
developments towards machines capable of both
“understanding natural language” and “speaking” in a
human way.
● Along with reasoning, language is often considered a highlevel cognitive activity that only humans are capable of.
● Can AI “jump over” evolutionary steps in the development
of cognition and base language use on pure machine
learning from vast data sets?
p. 9
INFO-COMPUTATIONAL FRAMEWORK
FOR STUDY OF COGNITION
● The framework for the discussion here is the computing
nature in the form of info-computationalism.
● It takes reality to be information for an agent with a
dynamics of information understood as computation.
● Information is a structure and computation its dynamics.
● Information is observer relative and so is computation.
[1][4][5]
p. 10
INFO-COMPUTATIONAL FRAMEWORK
FOR STUDY OF COGNITION
● Cognition is studied as information processing in such
simple organisms as bacteria [6], [7] as well as cognitive
processes in other, more complex multicellular life forms.
● We discuss computational mind and consciousness that
have recently been widely debated in the work of Giulio
Tononi [8] and Christoph Koch. [9]
p. 11
INFO-COMPUTATIONAL FRAMEWORK
FOR STUDY OF COGNITION
● While the idea that cognition is a biological process in all
living organisms, as argued by Humberto Maturana and
Francisco Varela [10], [11], it is not at all clear that all
cognitive processes in different kinds of organisms are
accompanied by anything akin to (human) consciousness.
● The suggestion is made that cognitive agents with nervous
systems are the step in evolution that first enabled
consciousness of the kind that humans possess. Argument
is advanced that ascribing consciousness to the whole of
the universe is not justified.
p. 12
REALITY AS INFORMATION FOR AN
AGENT
● Defining reality as information leaves us with the question:
what is it in the world that corresponds to information
and its dynamics, computation?
How do we model information/ computation? Answers are
many and they are not unambiguous.
p. 13
INFORMATION, COMPUTATION,
COGNITION
● We can compare the present situation regarding
information, computation and cognition with the history
of the development of other basic scientific concepts. Ideas
about matter, energy, space and time in physics have their
history. The same is true of the idea of number in
mathematics or the idea of life in biology.
● So, we should not be surprised to notice the development
in the theory of computation that goes hand in hand with
the development of information science, cognitive science,
computability, robotics, new computational devices and
new domains of the real world that can be understood infocomputationally.
p. 14
LIFE AS INFO-COMPUTATIONAL
GENERATIVE PROCESS OF COGNITION
AT DIFFERENT LEVELS OF
ORGANIZATION
An agent is an entity capable of acting on its own behalf. It can be seen as
an "actor" in the Actor model of computation in which "actors" are the basic
elements of concurrent computation exchanging messages, capable of
making local decisions and creating new actors. Computation is thus
distributed in space where computational units communicate
asynchronously and the entire computation is not in any well-defined
state. (An actor can have information about other actors that it has received
in a message about what it was like when the message was sent.) (Hewitt,
2012)
p. 15
COGNITIVE INFORMATION PROCESSING
THEORY OF LEARNING ACCORDING TO
(GAGNÉ, 1985)
This is an old and simplistic idea of cognition as information
processing. Missing in this scheme are feedback loops that are
absolutely essential for cognition and learning. Also missing is
information integration from different sensors and couplings to
actuators. Memory is not a passive storage but active ingredient in
perception, that is both used for recognition and anticipation.
Cognitive / Information Processing Theory of Learning according to (Gagné, 1985)
p. 16
WHAT IS REALITY (FOR AN AGENT)?
“Whatever is a reality today, whatever you touch
and believe in and that seems real for you today, is
going to be, like the reality of yesterday, an illusion
tomorrow.”
Luigi Pirandello, Six Characters in Search of an
Author
The father, in Six Characters in Search of an Author,
act 3 (1921).
p. 17
WHAT IS REALITY (FOR AN AGENT)?
● Would we agree that reality resides in that which is now,
taking into account that our cognitive apparatus has a finite
resolution in time (it might be as much as 7 seconds delay
between decision and action*) – where now would be
measured, perhaps in minutes?
● What about phenomena that change more slowly? For such
phenomena, “now” could be days, or years depending on
the phenomenon. But if it is longer time than what we
immediately observe, then the reality must be based not
only on current perception/understanding but also on
memory.
● How about reality of future (anticipated) events? What is a
difference of a highly probable event (such that the Earth
revolves the Sun hundred years from now)?
Chun Siong Soon, Marcel Brass, Hans-Jochen Heinze & John-Dylan Haynes, “Unconscious Determinants of Free p. 18
Decisions in the Human Brain.” Nature Neuroscience, April 13th, 2008.
WHAT IS REALITY (FOR AN AGENT)?
● Undoubtedly, we base our decisions/actions on both
memory, current observations and anticipations.
● There is a difference between fiction or virtual reality and
anticipated event based on firm past evidence.
● Degree of reality varies between anticipated highly
probable event and fiction or virtual representation of any
state of similar event.
p. 19
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● In his new book, Explaining the Computational Mind [49]
Marcin Miłkowski portrays current state of the ideas about
computational mind. The author presents and
systematically dissects number of misconceptions about
what is computation, clearly placing both neural networks
and dynamical systems into the domain of computational.
This is something that some philosophers would deny,
while practitioners would agree with. [36]
p. 20
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● Miłkowski proposes his own view of computational models
in the following:
● “(O)n my mechanistic account, only one level of the
mechanism – the so-called isolated level – is explained in
computational terms. The rest of the mechanism is not
computational, and, indeed, according to the norms of this
kind of explanation, it cannot be computational through
and through.”
● In this article I argue that this one-level-approach is not
adequate for natural (intrinsic) computation which appear
in hierarchy of levels. The reason why Miłkowski tries to
avoid multiplicity of computational levels is a fear of
computationalism being trivial:
p. 21
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● In this article I argue that this one-level-approach is not
adequate for natural (intrinsic) computation which appear
in hierarchy of levels. The reason why Miłkowski tries to
avoid multiplicity of computational levels is a fear of
computationalism being trivial:
● “ the bottoming-out principle of mechanistic explanation
(…) says that a phenomenon has to be explained as
constituted by some other phenomenon than itself. For a
pancomputationalist, this means that there must be a
distinction between lower-level, or basic, computations and
the higher level ones. Should pancomputationalism be
unable to mark this distinction, it will be explanatorily
vacuous.” [50]
p. 22
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● From the above I infer that the model of computation,
which Miłkowski assumes in his book, is a top-down,
designed computation. Even though he rightly argues that
neural networks are computational models and even
dynamical systems can be understood as computational,
Miłkowski does not think of intrinsic computation as
grounded in physical process driven by causal mechanism,
characteristics of computing nature.
p. 23
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● The fundamental question that worries Miłkowski is the
grounding problem that can lead to the conclusion about
triviality. I will argue that this really is a non-problem.
● To start with, grounding is always anchored in an agent who
is the narrator of the explanation. The narrator choses the
granularity of the account. No picture has infinite
granularity and nothing hinders to imagine even lower
levels of existence (such as more and more elementary
particles). This means that grounding is done over and over
again in all sciences.
p. 24
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● When constructing computational models, Miłkowski’s
focus on only one layer is pragmatically justified, but not a
matter of principle. Even though one can reconstruct many
intrinsic computational layers in the human brain
(depending on the granularity of the account), for an
observer/narrator often one layer is in focus at a time. In
such simplified models the layers above and below, even
though computational, are sketchy and used to represent
constraints and not mechanisms. That is at least the case in
designed computation as found in conventional computers.
But e.g. looking at the experimental work of Subrata Ghosh
et al. building a functional model of brain, we find twelvelayer computational architecture applied. [51]
p. 25
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● What is at stake in a theory of implementation? The main
problem seems to me exactly the opposite.
● It is not so interesting to study how brain implements
computation top-down (how do we know 1+1=2) but how
intrinsic information processing, that is evidently going on
in the brain can be interpreted as computation. What are
the characteristics of that new kind of computation that
information processes in the brain constitute?
p. 26
COMPUTATION ALL THE WAY DOWN
TO QUANTUM
● In that sense of bottom-up intrinsic computation Chalmers
characterization holds, [54] p. 326:
● “A physical system implements a given computation when
the causal structure of the physical system mirrors the
formal structure of the computation.”
● This position is called the Standard Position (SP) by Sprevak.
[55] p. 112. It is applicable to intrinsic computation (bottom
up, natural/intrinsic), but not to designed conventional
computation (top-down) as this “mirroring” would be a
very complex process of interpretation, coding, decoding
and interpretation again.
● Thus, not only neurons and whole brains compute (in the
framework of computing nature) but also the rest of nature
computes at variety of levels of organization.
p. 27
COMPUTATIONAL MODELS OF MIND
EXCULPATED
● Sprevak’s [55] p. 108 concerns about computationalism:
● (R1) Clarity: “Ultimately, the foundations of our sciences
should be clear.” Computationalism is suspected to lack
clarity.
● (R2) Response to triviality arguments: “(O)ur conventional
understanding of the notion of computational
implementation is threatened by triviality arguments.”
Computationalism is accused of triviality.
● (R3) Naturalistic foundations: “The ultimate aim of
cognitive science is to offer, not just any explanation of
mental phenomena, but a naturalistic explanation of the
mind.” Computationalism is questioned for being formal
and unnatural.
p. 28
COMPUTATIONAL MODELS OF MIND
EXCULPATED
● Let me summarize the distinction between intrinsic
/natural/ spontaneous computation and designed
computation used in our technological devices.
● In the info-computationalism, that is a variety of
pancomputationalism, physical nature spontaneously
performs different kinds of computations (information
dynamics) at different levels of organization. This is
intrinsic, natural computation and is specific for a given
physical system. Intrinsic computation(s) of a physical
system can be used for designed computation, such as one
found in computational machinery, but it is far from all
computation that can be found in nature.
p. 29
COMPUTATIONAL MODELS OF MIND
EXCULPATED
● Why is natural computationalism not vacuous?
● For the same reason that physics is not vacuous which
makes the claim that the entire physical universe is
material. Now we will not enter the topic of ordinary
matter-energy vs. dark matter-energy. Those are all
considered to be the same kind of phenomena – natural
phenomena that must be studied with methods of physics.
● Why is natural computationalism not vacuous? For the
same reason that physics is not vacuous which makes the
claim that the entire physical universe is material. Now we
will not enter the topic of ordinary matter-energy vs. dark
matter-energy. Those are all considered to be the same
kind of phenomena – natural phenomena that must be
studied with methods of physics.
p. 30
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● Some computational models of consciousness [8], [58],
[59], [9] seem to lead to panpsychism - a phenomenon
defined as follows:
● “Panpsychism is the doctrine that mind is a fundamental
feature of the world which exists throughout the universe.”
[60]
p. 31
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● Pancomputationalism (natural computationalism,
computing nature) is the doctrine that whole of the
universe, every physical system, computes. In the words of
[61]:
● “Which physical systems perform computations? According
to pancomputationalism, they all do. Even rocks,
hurricanes, and planetary systems — contrary to
appearances — are computing systems.
Pancomputationalism is quite popular among some
philosophers and physicists.”
p. 32
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● Info-computationalism starts bottom-up, from natural
processes understood as computation. It means that
computation appears as quantum, chemical, biologicalcognitive, …etc.
● Only those transformations of informational structures that
correspond to intrinsic processes in natural systems qualify
as computation.
p. 33
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● Given the argument for info-computational modelling of
nature, and the argument that every living organism
possesses some extent of cognition one can ask: why
should we not do similar move and ascribe consciousness
to the whole of the universe (hypothesis called
panpsychism)? Searle describes consciousness as follows:
● “Consciousness consists of states of awareness or sentience
or feeling. These typically begin in the morning when you
wake up from a dreamless sleep and go on all day until you
go to sleep or otherwise become 'unconscious.' ” [62]
p. 34
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● The simple answer why panpsychism is not a good idea is:
in the case of panpsychism we have no good model.
Unlike computational models of physical and thus biological
and cognitive processes we have no good psychical
models.
● In fact only naturalists accounts of consciousness provide
models, others prefer to see consciousness as totally
inexplicable in rational terms, a “mystery”.
● From the naturalist, knowledge generation point of view,
trying to understand everything as psyche got it backwards
– we do not know what to do after the very first move,
other than to say that it is “mysterious”.
p. 35
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● The simple answer why panpsychism is not a good idea is:
in the case of panpsychism we have no good model.
Unlike computational models of physical and thus biological
and cognitive processes we have no good psychical
models.
● In fact only naturalists accounts of consciousness provide
models, others prefer to see consciousness as totally
inexplicable in rational terms, a “mystery”.
● From the naturalist, knowledge generation point of view,
trying to understand everything as psyche got it backwards
– we do not know what to do after the very first move,
other than to say that it is “mysterious”.
p. 36
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● On the contrary, if we try to understand psyche or better to
say mind and consciousness as manifestations of physical
info-computational processes in the nervous system of a
cognizing agent, we immediately have an arsenal of
modelling tools to address the problem with and
successively and systematically learn more about it, even
construct artefacts (such as cognitive robots) and test it.
p. 37
WHY PANCOMPUTATIONALISM IS
USEFUL AND PANPSYCHISM IS NOT
● That is the main reason why panpsychism is not a good
scientific hypothesis. Instead of opening all doors for
investigation, it declares consciousness permeating the
entire universe and that's it. One can always generalize
concepts if they lead to better understanding and enable
further modelling. But generalizations of the idea of psyche
is akin to homeopathic procedure diluting it to
concentrations close to zero, and that will not give us
anything in terms of understanding of mechanisms of mind.
● Moreover, as a theory panpsychism belongs to medieval
tradition – that which is to be explained is postulated. I
wonder how would anyone ever get unconscious in a
conscious universe? What would be the difference between
human consciousness and the “consciousness” of a
bacterium or even a consciousness of vacuum?
p. 38
CONCLUSIONS AND FUTURE WORK
● Questions that we posed in the beginning of the article:
What is reality for an agent? How does reality of a
bacterium differ from a reality of a human brain? Do we
need representation in order to understand reality? led us
to the discussion of info-computational models of cognition
and consciousness.
● When talking about models of cognition, the very mention
of “computationalism” typically evokes reactions against
Turing machine model of the brain and perceived
determinism of computation. Neither of those two
problems affects natural computation or computing nature
where model of computation is broader than deterministic
symbol manipulation.
p. 39
CONCLUSIONS AND FUTURE WORK
● Computing nature consists of physical structures that form
levels of organization, on which computation processes
differ. It has been argued that on the lower levels of
organization finite automata or Turing machines might be
adequate, while on the level of the whole-brain non-Turing
computation is necessary, according to Andre Ehresmann
[63] and Subrata Ghosh et al. [51]
p. 40
CONCLUSIONS AND FUTURE WORK
● Finally, an argument is advanced that the idea of
panpsychism as a consequence of computational models by
no means should be understood as necessary. It rather
seems to be an artefact of the model and there is a variety
of ways to correct the model so that non-physical
properties do not follow.
p. 41
CONCLUSIONS AND FUTURE WORK
● For the future a lot of work remains to be done, especially
on the connections between the low level cognitive
processes and the high level ones. It is important to find
relations between cognition and consciousness and the
detailed picture of info-computational mechanisms behind
those phenomena.
p. 42
REFERENCES
http://www.idt.mdh.se/~gdc/work/AISB2014-02-20-1-GordanaDC.pdf
https://www.doc.gold.ac.uk/aisb50/#s23
E. Ben-Jacob, “Bacterial Complexity: More Is Different on All Levels,” in
Systems Biology- The Challenge of Complexity, S. Nakanishi, R. Kageyama, and
D. Watanabe, Eds. Tokyo Berlin Heidelberg New York: Springer, 2009, pp. 25–
35.
E. Ben-Jacob, “Learning from Bacteria about Natural Information Processing,”
Ann. N. Y. Acad. Sci., vol. 1178, pp. 78–90, 2009.
G. Tononi, “The Integrated Information Theory of Consciousness: An Updated
Account,” Arch. Ital. Biol., vol. 150, no. 2/3, pp. 290–326, 2012.
C. Koch, Consciousness - Confessions of a Romantic Reductionist. Cambridge
Mass.: MIT Press, 2012.
http://www.neuroinformatics2013.org Neuroinformatics conference 2013
p. 43
A COMPUTABLE UNIVERSE
p. 44
COMPUTING NATURE
Computation, Information, Cognition
Information and Computation
Computing Nature
Editor(s): Gordana Dodig Crnkovic and Susan
Editor(s): Gordana Dodig Crnkovic and
Editor(s): Gordana Dodig Crnkovic and
Stuart, Cambridge Scholars Publishing, 2007
Mark Burgin, World Scientific, 2011
Raffaela Giovagnoli, Springer, 2013
p. 45
Special Issue of the Journal Entropy
Selected Papers from Symposium on Natural/Unconventional
Computing and Its Philosophical Significance
Giulio Chiribella, Giacomo Mauro D’Ariano and Paolo Perinotti:
Quantum Theory, Namely the Pure and Reversible Theory of Information
Susan Stepney:
Programming Unconventional Computers: Dynamics, Development, SelfReference
Gordana Dodig Crnkovic and Mark Burgin:
Complementarity of Axiomatics and Construction
Hector Zenil, Carlos Gershenson, James A. R. Marshall and David A.
Rosenblueth:
Life as Thermodynamic Evidence of Algorithmic Structure in Natural
Environments
Andrée C. Ehresmann: MENS, an Info-Computational Model for (Neuro)cognitive Systems Capable of Creativity
Gordana Dodig Crnkovic and Raffaela Giovagnoli, Editorial:
Natural/Unconventional Computing and Its Philosophical Significance
46
Special issue of the journal Information
Information and Energy/Matter
Vlatko Vedral: Information and Physics
Philip Goyal: Information Physics—Towards a New Conception of Physical Reality
Chris Fields: If Physics Is an Information Science, What Is an Observer?
Gerhard Luhn: The Causal-Compositional Concept of Information Part I.
Elementary Theory: From Decompositional Physics to Compositional Information
Koichiro Matsuno and Stanley N. Salthe:
Chemical Affinity as Material Agency for Naturalizing Contextual Meaning
Joseph E. Brenner: On Representation in Information Theory
Makoto Yoshitake and Yasufumi Saruwatari: Extensional Information Articulation
from the Universe
Christopher D. Fiorillo: Beyond Bayes: On the Need for a Unified and Jaynesian
Definition of Probability and Information within Neuroscience
William A. Phillips: Self-Organized Complexity and Coherent Infomax from the
Viewpoint of Jaynes’s Probability Theory
Hector Zenil: Information Theory and Computational Thermodynamics: Lessons for
Biology from Physics
Joseph E. Brenner: On Representation in Information Theory
47
Gordana Dodig Crnkovic, Editorial: Information and Energy/Matter
Connections to the contemporary work
Informational structural realism (Luciano Floridi)
Unconventional computing – physical computing of natural systems
(Susan Stepney)
Agent-centred information self-structuring (Bill Phillips)
Informational reality for an agent (Vlatko Vedral)
Info-computational model for (neuro-)cognitive systems up to
creativity (Andrée C. Ehresmann)
Information integration and differentiation (Marcin Schröder)
Rao Mikkilineni Designing a New Class of Distributed Systems
(SpringerBriefs in Electrical and Computer Engineering)
Emergent Computation (Bruce MacLennan)
…
http://www.researchtoaction.org/live/wp-content/uploads/2011/05/networks1.jpg
ADDITIONAL MATERIAL
p. 49
ACTOR MODEL OF CONCURRENT
DISTRIBUTED COMPUTATION
“In the Actor Model [Hewitt, Bishop and
Steiger 1973; Hewitt 2010], computation
is conceived as distributed in space,
where computational devices
communicate asynchronously and the
entire computation is not in any welldefined state.
(An Actor can have information about
other Actors that it has received in a
message about what it was like when the
message was sent.) Turing's Model is a
special case of the Actor Model.”
(Hewitt, 2012)
Hewitt’s “computational devices” are conceived as computational
agents – informational structures capable of acting on their own
behalf.
p. 50
ACTOR MODEL OF CONCURRENT
DISTRIBUTED COMPUTATION
Actors are the universal primitives of concurrent distributed
digital computation. In response to a message that it receives,
an Actor can make local decisions, create more Actors, send
more messages, and designate how to respond to the next
message received.
For Hewitt Actors rise to the level of “Agenthood ” when they
competently process expressions
for commitments including the following:
Contracts, Announcements, Beliefs, Goals, Intentions, Plans,
Policies, Procedures, Requests, Queries.
In other words, his agents are human-like.
p. 51
LIFE AS INFO-COMPUTATIONAL
GENERATIVE PROCESS OF COGNITION
AT DIFFERENT LEVELS OF
ORGANIZATION
This paper presents a study within info-computational constructive
framework of the life process as <knowledge> generation in living agents
from the simplest living organisms to the most complex ones. Here
<knowledge> of a primitive life form is very basic indeed – it is
<knowledge> how to act in the world. An amoeba <knows> how to search
for food and how to avoid dangers.
p. 52
LIVING AGENTS
A living agent is a special kind of actor that can reproduce
and that is capable of undergoing at least one
thermodynamic work cycle. (Kauffman, 2000)
This definition differs from the common belief that (living)
agency requires beliefs and desires, unless we ascribe some
primitive form of <belief> and <desire> even to a very simple
living agents such as bacteria. The fact is that they act on
some kind of <anticipation> and according to some
<preferences> which might be automatic in a sense that they
directly derive from the organisms morphology. Even the
simplest living beings act on their own behalf.
53
LIVING AGENTS
Although a detailed physical account of the agents capacity to
perform work cycles and so persist in the world is central for
understanding of life/cognition, as (Kauffman, 2000) (Deacon,
2007) have argued in detail, this work is primarily interested
of the info-computational aspects of life.
Info-computational approach takes information an
computation to be the two basic building block concepts,
corresponding to structure and process, being and becoming.
Given that there is no information without physical
implementation (Landauer, 1991), computation as the
dynamics of information is the execution of physical laws.
54
LIVING AGENTS
Kauffman’s concept of agency (also adopted by Deacon)
suggests the possibility that life can be derived from physics.
That is not the same as to claim that life can be reduced to
physics that is obviously false.
However, in deriving life from physics one may expect that
both our understanding of life as well as physics will change.
We witness the emergence of information physics (Goyal,
2012) (Chiribella, G.; D’Ariano, G.M.; Perinotti, 2012) as a
possible reformulation of physics that may bring physics and
life/cognition closer to each other. This development
smoothly connects to info-computational understanding of
nature (Dodig-Crnkovic & Giovagnoli, 2013).
55
THE COMPUTING NATURE
Life can be analyzed as cognitive processes unfolding in a
layered structure of nested information network hierarchies
with corresponding computational dynamics (information
processes) – from molecular, to cellular, organismic and social
levels.
In order to construct life as cognitive process we will introduce
two fundamental theories about the nature of the universe
and propose their synthesis:
The first one with focus on processes is the idea of
computing universe (naturalist computationalism/
pancomputationalism) in which one sees the dynamics of
physical states in nature as information processing (natural
computation).
56
THE COMPUTING NATURE
The parallel fundamental theory with focus on structures is
Informational structural realism (Floridi, 2003) that takes information
to be the fabric of the universe (for an agent).
Combining definitions of Bateson:
“ information is a difference that makes a difference” (Bateson, 1972)
and Hewitt:
”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), we get:
information is defined as the difference in one physical system that
makes the difference in another physical system.
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THE COMPUTING NATURE
information is defined as the difference in one physical system that
makes the difference in another physical system.
This implies relational character of information and thus agentdependency in agent-based or actor model.
As a synthesis of informational structural realism and natural
computationalism, info-computational structuralism adopts two basic
concepts: information (as a structure) and computation (as a dynamics
of an informational structure) (Dodig-Crnkovic, 2011) (Chaitin, 2007).
In consequence the process of dynamical changes of the universe
makes the universe a huge computational network where
computation is information processing. (Dodig-Crnkovic & Giovagnoli,
2013) Information and computation are two basic and inseparable
elements necessary for naturalizing cognition and <knowledge>. (DodigCrnkovic, 2009)
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THE COMPUTING NATURE
Agents - systems able to act on their own behalf and make
sense (use) of information are of special interest with respect
to <knowledge> generation.
This relates to the ideas of participatory universe, (Wheeler,
1990) endophysics (Rössler, 1998) and observer-dependent
<knowledge> production.
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MORPHOLOGICAL COMPUTATION –
FROM SIMPLEST TO THE MOST
COMPLEX ORGANISMS
In the computing nature, <knowledge> generation should be
studied as a natural process. That is the main idea of
Naturalized epistemology (Harms, 2006), where the subject
matter is not our concept of <knowledge>, but the knowledge
itself as it appears in the world as specific informational
structures of an agent.
Maturana and Varela were the first to suggest that knowledge
is a biological phenomenon. They argued that life should be
understood as a process of cognition, which enables an
organism to adapt and survive in the changing environment.
(Maturana & Varela, 1980)
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NETWORK AGENT/ACTOR MODELLS
Protein network in yeast cells
Human connectome
Human protein interaction network
Social network
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Human brain is biological information
processor - network of neurons processing information
http://neuralethes-en.blogspot.se/2012/04/human-connectome-project.html
Human Connectome Project
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Info-computational framework: connecting
informational structures and processes from
quantum physics
to living organisms and societies
Nature is described as a complex informational structure for a
cognizing agent.
Computation is information dynamics (information
processing) constrained and governed by the laws of physics
on the fundamental level.
Information is the difference in one information structure that
makes a difference in another information structure.
p. 63
COMPUTING NATURE
The basic idea of computing nature is that all processes taking place
in physical world can be described as computational processes – from
the world of quantum mechanics to living organisms, their societies
and ecologies. Emphasis is on regularities and typical behaviors.
Even though we all have our subjective reasons why we move and
how we do that, from the bird-eye-view movements of inhabitants in
a city show big regularities.
In order to understand big picture and behavior of societies, we take
computational approach based on data and information.
See the work of Albert-László Barabási who studies networks on
different scales:
http://www.barabasilab.com/pubs-talks.php
COMPUTATION AS INFORMATION
PROCESSING
Info-computational approach takes information as the primary stuff of
the universe, and computation is as time-dependent behavior
(dynamics) of information.
This results in a Dual-aspect Universe: informational structure with
computational dynamics. (Info-Computationalism, Dodig Crnkovic)
Information and computation are closely related – no computation
without information, and no information without dynamics
(computation).
COGNITION AS COMPUTATION
http://www.worldhealth.net/news/
hormone-therapy-helps-improve-cognition
Information/computation mechanisms are
fundamental for evolution of intelligent agents. Their
role is to adapt the physical structure and behavior
that will increase organisms chances of survival, or
otherwise induce some other behavior that might be
a preference of an agent.
In this pragmatic framework, meaning in general is
use, which is also the case with meaning of
information.
http://www.ritholtz.com/blog/wp-content/uploads/
2012/04/my-brain-hurts.png