http://groups.uni-paderborn.de/hagengruber/pris08/

1. 1
Info-Computationalism and Philosophical
p p
Aspects of Research in Information Sciences
Gordana Dodig Crnkovic
School of Innovation, Design and Engineering,
Mälardalen University, Sweden
Philosophy's Relevance in Information Science Paderborn University
Paderborn, Germany, 2008 10 04
http://groups.uni-paderborn.de/hagengruber/pris08/

2. 2
What is Science?
How did Sciences Develop?
Eye
2
Maurits Cornelis Escher

3. 3
The Mytho-Poetic Universe
y
Mytho-Poetic Universe of Egypt Hindu Mytho-Poetic Universe
In ancient Egypt the dome of the sky In Hindu myth, the tortoise supports
elephants that hold up the world, and
was represented by the goddess Nut,
everything is encircled by the world
the night sky, and the sun, the god
serpent
Ra, was born from her every morning. 3

4. 4
The M h i l U i
Th Mechanical Universe
The Medieval Geocentric Universe The Clockwork Universe
Newton Philosophiae Naturalis
The universe depicted in The
p
Principia Matematica, 1687
Nuremberg Chronicle (1493)
4

5. 5
The Computational Universe
We are all living inside a gigantic
computer. No, not The Matrix: the
Universe.
Every process, every change that takes
E h h k
place in the Universe, may be
considered as a kind of computation.
E Fredkin S Wolfram G Chaitin
Fredkin, Wolfram,
The universe is on a fundamental level
an info-computational phenomenon
info computational phenomenon.
GDC
http://www nature com/nsu/020527/020527-
http://www.nature.com/nsu/020527/020527-
16.html
5

6. 6
The Computational Universe
Konrad Zuse was the first to suggest (in 1967)
that the physical behavior of the entire
universe is being computed on a basic level,
possibly on cellular automata by the universe
automata,
itself which he referred to as quot;Rechnender
Raumquot; or Computing Space/Cosmos.
Computationalists: Zuse, Wiener, Fredkin,
Wolfram, Chaitin, Lloyd, Seife, 't Hooft,
Deutsch, Tegmark, Schmidhuber, Weizsäcker,
Wheeler..
http://en.wikipedia.org/wiki/Pancomputationalism
http://www.nature.com/nature/journal/v
435/n7042/full/435572a.html 6

7. 7
The Major Paradigm Shifts in our View
of the Universe
(Classical) Info-
Mechanic Computational
Mytho-poetic,
Universe Universe
God Centric
God-Centric Universe

8. 8
The Classical Model of Science
The Classical Model of Science is a system S of propositions and
concepts satisfying the following conditions:
All propositions and all concepts (or terms) of S concern a specific
•
set of objects or are about a certain domain of being(s).
There are in S a number of so-called fundamental concepts (or
•
terms).
All other concepts (or terms) occurring in S are composed of (or
•
are definable from) these fundamental concepts (or terms).
8

9. 9
The Classical Model of Science
There are in S a number of so-called fundamental propositions
so called propositions.
•
All other propositions of S follow from or are grounded in (or are
•
provable or demonstrable from) these fundamental propositions
propositions.
All propositions of S are true.
•
All propositions of S are universal and necessary in some sense or
•
another.
9

10. 10
The Classical Model of Science
All concepts or terms of S are adequately known A non-
known. non
•
fundamental concept is adequately known through its composition
(or definition).
The Classical Model of Science is a reconstruction a posteriori and
•
sums up the historical philosopher’s ideal of scientific explanation.
The fundamental is that “All propositions and all concepts (or
•
terms) of S concern a specific set of objects or are about a certain
domain of being(s).”
Betti A & De Jong W R., Guest Editors, The Classical Model of Science I: A Millennia Old Model of Scientific
W. R Editors Millennia-Old
Rationality, Forthcoming in Synthese, Special Issue
10

11. 11
The Scientific Method
EXISTING KNOWLEDGE
THEORIES
PREDICTIONS
HYPOTHESIS
AND OBSERVATIONS
Hypothesis must
Hypothesis must be adjusted
be redefined
SELECTION AMONG TESTS AND NEW
COMPETING THEORIES OBSERVATIONS
Consistency
achieved
The hypotetico-deductive cycle
EXISTING THEORY CONFIRMED
(within a new context) or
NEW THEORY PUBLISHED
The Scientific-community cycle
11

12. 12
Natural Philosophy
Natural philosophy or the philosophy of nature (Latin philosophia
naturalis),
naturalis) is a study of nature and the physical universe that was
dominant before the development of modern science in the 19th
century. Newton was natural philosopher.
At older universities, l
ld i iti long-established Ch i of N t l Phil
t bli h d Chairs f Natural Philosophy are
h
nowadays occupied mainly by physics professors.
http://en.wikipedia.org/wiki/Natural_philosophy
http://en.wikipedia.org/wiki/Natural philosophy
At present, interesting complexity phenomena are studied on the
intersection of several research fields such as computing, biology,
neuroscience, cognitive science, philosophy, physics, and similar
i iti i hil h hi d i il
information/computation intensive fields which might again form a
core of a new life-centric natural philosophy.

13. 13
Info-Computationalism
p
Information and computation are two interrelated and mutually defining
p
phenomena – there is no computation without information
p
(computation understood as information processing), and vice
versa, there is no information without computation (all information is
a result of computational processes).
Being interconnected, information is studied as a structure, while
computation presents a process on an informational structure. In
order to learn about foundations of information, we must also study
computation.

14. 14
Information
A special i
i l issue of th
f the
Journal of Logic, Language and Information
(Volume 12 No 4 2003) dedicated to the
different facets of information
information.
A Handbook on the Philosophy of Information
(Van Benthem Adriaans) is in preparation as
Benthem,
one volume Handbook of the philosophy of
science. http://www.illc.uva.nl/HPI/
The Internet
http://www.sdsc.edu/News%20Items/PR022008_moma.html
http://www.sdsc.edu/News%20Items/PR022008 moma.html

15. 15
“IT IS TEMPTING TO SUPPOSE THAT SOME CONCEPT OF
INFORMATION COULD SERVE EVENTUALLY TO UNIFY MIND,
MATTER, AND MEANING IN A SINGLE THEORY.” (Emphasis in the original)
Daniel C. Dennett And John Haugeland. Intentionality.
in Richard L. Gregory, Editor. The Oxford Companion To The Mind. Oxford University
Press, Oxford, 1987.

16. 16
Computation
The Computing Universe: Pancomp tationalism
Comp ting Uni erse Pancomputationalism
Computation i generally d fi d as i f
C t ti is ll defined information processing.
ti i
(See Burgin, M., Super-Recursive Algorithms, Springer Monographs in
Computer Science, 2005)
p )
For different views see e.g.
http://people.pwf.cam.ac.uk/mds26/cogsci/program.html Computation and
Cognitive Science 7–8 July 2008, King's College Cambridge
The definition of computation is widely debated, and an entire issue of the
journal Minds and Machines (1994, 4, 4) was devoted to the question
“What i C
“Wh t is Computation?” E
t ti ?” Even: Th
Theoretical C
ti l Computer S i
t Science 317 (2004)

17. 17
Computing Nature and
Nature Inspired Computation
Natural computation includes
computation that occurs in nature or
is inspired by nature Computing
nature.
Inspired by nature:
•Evolutionary computation
•Neural networks
•Artificial immune systems
•Swarm intelligence
In 1623, Galileo in his book The Assayer - Simulation and emulation of nature:
Il Saggiatore, claimed that the language of •Fractal geometry
nature's book is mathematics and that the
•Artificial life
Artificial
way to understand nature is through
mathematics. Generalizing ”mathematics”
Computing with natural materials:
to ”computation” we may agree with
Galileo – the great book of nature is an e- •DNA computing
book! •Quantum computing
http://www.youtube.com/watch?v=JA5QoTMvsiE&feature=related
Journals: Natural Computing and IEEE Transactions on Evolutionary Computation.

18. 18
Turing Machines Limitations –
Self-Generating
Self Generating Living Systems
Complex biological systems must be modeled as self-
referential, self-organizing quot;
f il lf i i quot;component-systemsquot;
quot;
(George Kampis) which are self-generating and
whose behavior, though computational in a general
sense, goes f beyond T i machine model.
far b d Turing hi dl
“a component system is a computer which, when executing its operations
(software) builds a new hardware.... [W]e have a computer that re-wires itself in
a hardware software interplay: the hardware defines the software and the
hardware-software
software defines new hardware. Then the circle starts again.”
(Kampis, p. 223 Self-Modifying Systems in Biology and Cognitive Science)

19. 19
Beyond Turing Machines
y g
Ever since Turing proposed his machine model which identifies
computation with the execution of an algorithm there have been
algorithm,
questions about how widely the Turing Machine (TM) model is
applicable.
With the advent of computer networks, which are the main paradigm
of computing today, the model of a computer in isolation,
represented by a Universal Turing Machine, has become
insufficient.
i ffi i t
The basic difference between an isolated computing box and a
network of computational processes (nature itself understood as a
t kf t ti l (t it lf d t d
computational mechanism) is the interactivity of computation. The
most general computational paradigm today is interactive
computing (Wegner Goldin).
(Wegner, Goldin)

20. 20
Beyond Turing Machines
y g
The challenge to deal with computability in the real world (such as
computing on continuous data, biological computing/organic
computing, quantum computing, or generally natural computing)
has brought new understanding of computation.
Natural computing has different criteria for success of a computation,
halting problem is not a central issue, but instead the adequacy of
the comp tational response in a net ork of interacting
computational network
computational processes/devices. In many areas, we have to
computationally model emergence not being clearly algorithmic.
(Barry Cooper)

21. 21
Correspondence Principle
picture after Stuart A Umpleby
A.
http://www.gwu.edu/~umpleby/recent_papers/2004_what_i_learned_from_heinz_von_foerster_fig
ures_by_umpleby.htm
TM
Natural Computation

22. 22
Computability Theory
Barry Cooper
http://www.amsta.leeds.ac.uk/~pmt6sbc/

23. 23
Info-Computationalism Applied:
Epistemology Naturalized
p gy
Naturalized epistemology (Feldman, Kornblith, Stich) is, in general, an
idea that knowledge may be studied as a natural phenomenon --
that the subject matter of epistemology is not our concept of
knowledge, but the knowledge itself.
“The stimulation of his sensory receptors is all the evidence anybody
has had to go on, ultimately, in arriving at his picture of the world. Why
g , y, g p y
not just see how this construction really proceeds? Why not settle
for psychology? “(quot;Epistemology Naturalizedquot;, Quine 1969; emphasis
mine)
I will re-phrase the q
p question to be: Why not settle for computing?
y p g
Epistemology is the branch of philosophy that studies the nature, methods, limitations, and
validity of knowledge and belief.

24. 24
Naturalist Understanding of Cognition
According t M t
A di to Maturana and V l (1980) even the simplest organisms
d Varela th i l t i
possess cognition and their meaning-production apparatus is contained
in their metabolism. Of course, there are also non-metabolic interactions
with the environment, such as locomotion, that also generates meaning
for an organism by changing its environment and providing new input
data.
Maturana’s and Varelas’ understanding that all living organisms posess
some cognition, i some d
iti in degree. i most suitable as th b i f a
is t it bl the basis for
computationalist account of the naturalized evolutionary epistemology.
Info-Computationalism and Philosophical Aspects of Scientific Research

25. 25
Info-Computational Account of
Knowledge Generation
Natural computing as a new paradigm of
computing goes b
ti beyond th T i M hi model
d the Turing Machine dl
and applies to all physical processes including
those going on in our brains.
The next great change in computer science and
information technology will come from mimicking
gy g
the techniques by which biological organisms
process information.
To do this computer scientists must draw on
expertise in subjects not usually associated with
their field including organic chemistry molecular
field, chemistry,
biology, bioengineering, and smart materials.

26. 26
Info-Computational Account of
Knowledge Generation
At the ph sical le el li ing beings are open comple
physical level, living complex
computational systems in a regime on the edge of chaos,
characterized by maximal informational content.
Complexity is found between orderly systems with high
information compressibility and low information content and
random systems with low compressibility and high
information content. (Flake)
The essential feature of cognizing living organisms is their
ability to manage complexity, and to handle complicated
environmental conditions with a variety of responses which
are results of adaptation, variation, selection, l
lt f d t ti i ti l ti learning,
i
and/or reasoning. (Gell-Mann)

27. 27
Cognition as Restructuring of an Agent in
Interaction with the Environment
As a result of evolution increasingly complex living organisms arise that are
evolution,
able to survive and adapt to their environment. It means they are able to
register inputs (data) from the environment, to structure those into
information, and in more developed organisms into knowledge. The
p g g
evolutionary advantage of using structured, component-based
approaches is improving response-time and efficiency of cognitive
processes of an organism.
The Dual network model, suggested by Goertzel for modeling cognition in a
living organism describes mind in terms of two superposed networks: a
self-organizing associative memory network and a perceptual-motor
network,
process hierarchy, with the multi-level logic of a flexible command
structure.

28. 28
Cognition as Restructuring of an Agent in
Interaction with the Environment
Naturalized knowledge generation acknowledges the body as our basic
cognitive instrument. All cognition is embodied cognition, in both
microorganisms and humans (Gärdenfors, Stuart). In more complex
cognitive agents, knowledge is built upon not only reasoning about input
g g g p y g p
information, but also on intentional choices, dependent on value systems
stored and organized in agents memory.
It is not surprising that present day interest in knowledge generation places
information and computation (communication) in focus, as information and
its processing are essential structural and dynamic elements which
characterize structuring of input data (data → information → knowledge)
by an interactive computational process going on in the agent during the
adaptive interplay with the environment.

29. 29
Natural Computing in Cognizing Agents
- Agent-centered (information and
computation is in the agent)
- Agent is a cognizing biological organism
or an intelligent machine or both
- Interaction with the physical world and
other agents is essential
- Kind of physicalism with information as a
s u of e universe
stuff o the u e se
- Agents are parts of different cognitive
communities
- Self-organization
- Circularity (recursiveness) is central for
biological organisms
http://www.conscious-robots.com

30. 30
Self-Reflection
http://brain.oxfordjournals.org/cgi/content/full/125/8/1808

31. 31
What is computation? How does nature
compute? Learning from Nature *
“It always bothers me that according to the laws as we understand
It that,
them today, it takes a computing machine an infinite number of
logical operations to figure out what goes on in no matter how tiny
a region of space and no matter how tiny a region of time …
space,
So I have often made the hypothesis that ultimately physics will not
require a mathematical statement, that in the end the machinery
i th ti l t t t th t i th d th hi
will be revealed, and the laws will turn out to be simple, like the
chequer board with all its apparent complexities.”
Richard Feynman “The Character of Physical Law”
The Law
* 2008 Midwest NKS Conference, Fri Oct 31 - Sun Nov 2, 2008
Indiana University — Bloomington, IN

32. 32
An Ongoing Paradigm Shift
Information/Computation as basic building blocks of
•
understanding
Discrete/Continuum as two complementary levels of
•
description
Natural interactive computing beyond Turing limit – not
•
only computing as is but also computing as it may be
Complex dynamic systems (grounds for future
•
communication across cultural gaps of research)

33. 33
An Ongoing Paradigm Shift
g g g
Emergency (emergent property - a quality possessed by the whole
•
but not by its p
y parts)
)
Logical pluralism
•
Philosophy (“Everything must go” approach synthetic
Phil h (“E thi t” h th ti
•
besides analytic approaches, philosophy informed by
sciences)
Human-centric (agent-centric) models
•
Circularity and self reflection (computing cybernetics)
self-reflection (computing,
•
Ethics returns to researchers agenda (Science as a
•
constructivist project – what is it we construct and why?)

34. 34
There is a crack, a crack in everything ..
, y g
Ring the bells that still can ring
Forget your perfect offering
There is a crack, a crack in everything
That's how the light gets in.
Leonard Cohen

35. 35
An Example ..
Until the 18th century, alchemy was regarded as the ‘art of all
arts, the science of all sciences’. Whereas one branch of
alchemy developed into modern natural sciences its other
sciences,
offshoots became the dark side of science, and were either
forgotten or suppressed.
The crisis consists precisely in the fact that the old is dying and
the new cannot be born…
Antonio G
A t i Gramsci, Prison Notebooks
i Pi Ntb k
From the lecture “The dark side: relevance and accountability in interdisciplinary collaborations” Ronald Jones
& Rolf Hughes, Konstfack, Stockholm

36. 36
Summary
Philosophy in general and especially Computing and Philosophy
can contribute to Sciences of Information by:
Providing
P idi a common l language and an unified platform (f
d ifi d l tf (framework)
k)
for specialist sciences to communicate and create holistic (multi-
disciplinary/inter-disciplinary/transdisciplinary) views
Deepening understanding of info-computational mechanisms and
processes and their relationship to life and knowledge
Prompting development of new unconventional computational
pg p p
methods

37. 37
Summary
Helping understanding and improvement of learning processes
providing broader, more general context and agendas
Contributing to argument for evolution of biological life, cognition
and intelligence
Encouraging learning from nature about optimizing solutions with of
finite resources constraints
and so on..

38. 38
References
Gordana Dodig-Crnkovic
Semantics of Information as Interactive Computation
in Manuel Moeller, Wolfgang Neuser, and Thomas Roth-Berghofer (eds.),
Fifth International Workshop on Philosophy and Informatics,
Kaiserslautern 2008 ((DFKI Technical Reports; Berlin: S
Springer)
)
Gordana Dodig-Crnkovic
Where do New Ideas Come From? How do They Emerge?
Epistemology as Computation (Information Processing)
Chapter for a book celebrating the work of Gregory Chaitin,
Randomness & Complexity, from Leibniz to Chaitin,
C. Calude d World Scientific, Singapore, 2007 B k C
C C l d ed., W ld S i tifi Si Book Cover
Gordana Dodig-Crnkovic
Epistemology Naturalized: The Info-Computationalist Approach
APA Newsletter on Philosophy and Computers, Spring 2007 Volume 06,
Number 2

39. 39
Gordana Dodig-Crnkovic
Knowledge G
K l d Generation as N t l C
ti Natural Computation,
t ti
Proceedings of International Conference on Knowledge Generation,
Communication and Management (KGCM 2007), Orlando, Florida, USA,
July 8-11, 2007
Gordana Dodig-Crnkovic
Investigations into Information Semantics and Ethics of Computing
PhD Thesis, Mälardalen University Press September 2006
Thesis Press,
Dodig-Crnkovic G. and Stuart S., eds.
Computation, Information, Cognition – The Nexus and The Liminal
p , , g
Cambridge Scholars Publishing, Cambridge 2007
Gordana Dodig-Crnkovic
Shifting the Paradigm of the Philosophy of Science the Philosoph of
Philosoph Science: Philosophy
Information and a New Renaissance
Minds and Machines: Special Issue on the Philosophy of
Information,November 2003, Volume 13, Issue 4
/
http://www.springerlink.com/content/g14t483510156726/