Presentation at the Workshop on Open System Thinking, University of León, 15-17 May 2013.

1. Open System Thinking: at the crossroads of a complex world
Fundación Sierra‐Pambley, León (Spain)
Information andInformation and
Computation.
Dynamics of ComplexityDynamics of Complexity
as Natural Computation
Gordana Dodig Crnkovic
Professor of Computer Science
Mälardalen University,Mälardalen University,
School of Innovation, Design and
Engineering
http://www.mrtc.mdh.se/~gdc/

2. Mälardalen Universityy
2

3. My Teachingy g
CDT403 - Research Methods in Natural Sciences and EngineeringCDT403 - Research Methods in Natural Sciences and Engineering
CDT415 - Computing and Philosophy
DVA403 - Research Thinking and Writing ToolboxDVA403 - Research Thinking and Writing Toolbox
CDT314 - Formal Languages, Automata and Theory of Computation
CDT212 Information Kunskap Vetenskap (Information Knowledge Science)CDT212 - Information, Kunskap, Vetenskap (Information, Knowledge, Science)
CDT409 - Professional Ethics
http://www.idt.mdh.se/personal/gdc/work/courses.html
p. 3

4. My Research
Computing Paradigms, Natural/Unconventional Computing
Info-Computationalism, Computational aspects of Science of
f / f f S C )Information/Foundations of Information; Social Computing);
Computational knowledge generation, Computational aspects of
I lli d C i i Th f S i / Phil h f S iIntelligence and Cognition; Theory of Science/ Philosophy of Science;
Computing and Philosophy and
Ethics (Especially Ethics of Computing, Information Ethics, Roboethics and
Engineering Ethics).
http://www.idt.mdh.se/personal/gdc/work/publications.html
p. 4

5. Abstract
Information is understood as representing the world (reality as an informational web) for a cognizing
agent, while information dynamics (information processing, computation) realizes physical laws
through which all the changes of informational structures unfoldthrough which all the changes of informational structures unfold.
Processes considered rendering information dynamics have been studied, among others in:
questions and answers, observations, communication, learning, belief revision, logical inference,
game-theoretic interactions and computation. This lecture will put the computational approaches to
f f finformation dynamics into a broader context of natural computation, where information dynamics is
not only found in human communication and computational machinery but also in the entire nature.
Computation as it appears in the natural world is more general than the human process of calculation
modeled by the Turing machine Natural computing is epitomized through the interactions ofmodeled by the Turing machine. Natural computing is epitomized through the interactions of
concurrent, in general asynchronous computational processes which are adequately represented by
what Abramsky names “the second generation models of computation” which we argue to be the
most general representation of information dynamics.
We will relate the layered organization of informational structures of complex systems find in natural
and social world to their dynamics understood as natural computation.
Keywords: information dynamics; natural computationalism; info-computationalism;unified theoriesKeywords: information dynamics; natural computationalism; info computationalism;unified theories
of information; philosophy of information
p. 5

6. Based on the Articles
● Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information
2011, 2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papers
from FIS 2010 Beijing Conference, 2011.
htt // d i /j l/i f ti / i l i / l t d b ijihttp://www.mdpi.com/journal/information/special_issues/selectedpap_beijing
http://www.mdpi.com/2078-2489/2/3/460/ See also:
http://livingbooksaboutlife.org/books/Energy_Connections
● Dodig-Crnkovic G. and Burgin M., Unconventional Algorithms: Complementarity
of Axiomatics and Construction, Special issue of the journal Entropy, 14(11),
2066-2080; "Selected Papers from the Symposium on Natural/Unconventional
Computing and its Philosophical Significance", doi:10.3390/e14112066g g
http://www.mdpi.com/journal/entropy/special_issues/unconvent_computing, 2012.
p. 6

7. Conceptual Basics:
Computation as Information ProcessingComputation as Information Processing
7

8. Conceptual Basics: Levels of
Description – Levels of Abstraction –p
Levels of Organization
p. 8

9. Conceptual Basics: Network Modellsp
Protein network in yeast cells Human protein interaction networkProtein network in yeast cells Human protein interaction network
Social network
Human connectome
p. 9

10. General Remark.
Knowledge & Hinders to Knowledge
Knowledge – what we believe we know: (what we have strongKnowledge what we believe we know: (what we have strong
reasons to believe we understand and hold for correct/true).
“Philosophy: true, justified belief; certain understanding, as opposed
to opinion.” http://oxforddictionaries.comto op o ttp //o o dd ct o a es co
Ignorance – white spots on the map of knowledge – what we know
that we do not know.
False believes that look like knowledge – a part of our knowledge,
what we believe we know, but actually we are wrong!
False beliefs are much more dangerous and difficult epistemic
problems than things that we understand that we do not know.
Those are biggest hiders to new knowledge. Questioning of the “self-
evident” is necessary, but self-evident is by its nature something we
seldom suspect… (Fallibility of scientific knowledge – Popper) p. 10

11. Things That We Dont Know
Well Enough
● COMPLEX SYSTEMS AND
EMERGENCY
PHYSICS AT VERY SMALL AND● PHYSICS AT VERY SMALL AND
VERY LARGE DIMENSIONS
● MECHANISMS OF LIFE &● MECHANISMS OF LIFE &
ORIGINS OF LIFE
● HUMAN BRAIN● HUMAN BRAIN
(including knowledge generation)
….
p. 11

12. What We Miss in the Present
Scientific Picture of the World
Specific questions:
● MECHANISTIC VS. COMPLEX
● REDUCTION VS. HOLISM (SYSTEM VIEW)
● OBSERVER DEPENDENCE VS. GOD EYE VIEW
● EMBODDIEDNESS OF ALL NATURAL PHENOMENA INCLUDING MIND
p. 12

13. Complexityp y
A complex system is a system composed of interconnected
parts that as a whole exhibit one or more propertiesparts that as a whole exhibit one or more properties
(behavior among the possible properties) not obvious from
the properties of the individual parts.[1]
A system’s complexity may be of one of two forms:
disorganized complexity and organized complexity.[2]
Disorganized complexity is a matter of a very large numberDisorganized complexity is a matter of a very large number
of parts, and organized complexity is a matter of the subject
system exhibiting emergent properties.
http://en.wikipedia.org/wiki/Complex system
Complexity is found between orderly systems with high
information compressibility and low information content and
random systems with low compressibility and high
ttp //e ped a o g/ /Co p e _syste
random systems with low compressibility and high
information content.
p. 13

14. Complexityp y
In a complex system what we see is dependent on where we are and whatIn a complex system, what we see is dependent on where we are and what
sort of interaction is used to study the system.
Study of complex systems:
Generative Models
How does the complexity arize?
Evolution is the most well known
ti h igenerative mechanism.
http://www.morphwize.com/company/index.php?option=com_k2&view=itemlist&task=tag&tag=complex+system+solutionp. 14

15. Complexity topics
Adaptation; Agent-based Modeling; Artificial Intelligence; Artificial
Life; W. Ross Ashby; Biological Order and Levels of Organization;
p y p
; y; g g ;
Cellular Automata; Chaos and Non-linear Dynamics; Cognitive
Science; Collective Cognition; Complexity Measures;
Cybernetics; Darwin Machines; Developmental Biology;
Dissipative Structures; Edge of Chaos; Emergent Properties;Dissipative Structures; Edge of Chaos; Emergent Properties;
Ergodic Theory; Evolution; Evolution of Complexity; Evolutionary
Computation; Evolutionary Economics; Information Theory;
Institutions and Organizations; Learning in Games; Machineg g
Learning, Statistical inference and Induction; Multi-Agent
Systems; Neural Nets, Connectionism, Perceptrons;
Neuroscience; Nonequilibrium Statistical Mechanics and
Thermodynamics; Pattern Formation; Physics of ComputationThermodynamics; Pattern Formation; Physics of Computation
and Information; Physical Principles in Biology; Physics; Power
Law Distributions and Long-Range Correlations; Ilya Prigogine;
Random Boolean Networks, Self-organization; Simulations;
p. 15http://vserver1.cscs.lsa.umich.edu/~crshalizi/notabene/complexity.html

16. Complexity from Simplicityp y p y
Examples
http://www youtube com/watch?v=gdQgoNitl1ghttp://www.youtube.com/watch?v=gdQgoNitl1g
Emergence - Complexity from Simplicity, Order from Chaos (1 of 2)
(4.54)
http://www.youtube.com/watch?v=ONiWmzrmfuY&NR=1
Murray Gell-Mann On Emergence (1.30)
htt // t b / t h? Lk6QU94 Ab8http://www.youtube.com/watch?v=Lk6QU94xAb8
Fractals - The Colors Of Infinity (53:45)
http://www.youtube.com/watch?v=u CaCie8R4U&feature=relatedhttp://www.youtube.com/watch?v u_CaCie8R4U&feature related
Patterns in Nature (3.04)
p. 16

17. Generative Models:
From the Origins up in Complexity
Examplesp
http://www.youtube.com/watch?v=U6QYDdgP9eg&NR=1
The Origin of Life - Abiogenesis (10)The Origin of Life Abiogenesis (10)
http://www.youtube.com/watch?v=rtmbcfb_rdc&p=0696457CAFD6D7C9
The Origin of the Genetic Code (9.37)
http://www.youtube.com/watch?v=6RbPQG9WTZM&feature=related
The Origin of the Brain (9.29)
http://www.youtube.com/watch?v=NEEXK3A57Hk
The Origin of Intelligence (5.15)
p. 17

18. Self-Organizationg
Examples
http://www youtube com/watch?v=SkvpEfAPXn4&feature=fvwrelhttp://www.youtube.com/watch?v=SkvpEfAPXn4&feature=fvwrel
Robots with a mind of their own (1.38)
http://www youtube com/watch?v=QdQBH 5Aabs&feature=relatedhttp://www.youtube.com/watch?v=QdQBH_5Aabs&feature=related
Self-replicating Kinematic Cellular Automata (0.06)
http://www youtube com/watch?v=KPP 4 LEHXQhttp://www.youtube.com/watch?v=KPP-4-LEHXQ
Self Organization of vertical magnetic dipoles floating on water (2.53)
“SELF STAR” PROPERTIES IN ORGANIC SYSTEMS:“SELF-STAR” PROPERTIES IN ORGANIC SYSTEMS:
self-organization, self-configuration (auto-configuration), self-optimisation
(automated optimization), self-repair (self-healing), self-protection (automated
computer security), self-explaining, and self-awareness (context-awareness).computer security), self explaining, and self awareness (context awareness).
p. 18

19. Complex Adaptive Systemsp p y
Examples
http://www.youtube.com/watch?v=TCOmBVrnDeA&feature=related
Complex adaptive systems (1.02)
http://www.youtube.com/watch?v=bnKhzRpXPvM&feature=related
A New Frontier: Systems Biology (12.55)
http://www.youtube.com/watch?v=KO7BIrHVQU0&playnext=1&list=PL04270148Fp y p y
BDF86A6
Cities and Countries Part Two: Cultural Systems (4:45)
http://www.youtube.com/watch?v=t2JHbhYyoJc
Eunice E. Santos receives 2010 IEEE Computer Society Technical Achievement
Award (1.38)
p. 19

20. Modelling of Complexityg p y
Li t d ibilit● Linear systems – decomposibility -
Modelled by Analysis – Top-down – Global (Reductionism)
● Non-linear systems – behave as a whole –
Modelled by Synthesis - Bottom-up, Distributed
(Holism, System approaches)
● Complex behavior can emerge from SIMPLE GENERATORS!
http://www.youtube.com/watch?v=7WNUQspHyoA
Complexity & Chaos. Emergence & Complexity (5.43)
p. 20

21. Agent-based Modelsg
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.
Monte Carlo Methods are used to introduce randomness.
The basic of ABMs the study of complexity and emergence.
http://www.youtube.com/watch?v=2C2h-vfdYxQ&feature=related
Composite Agents (5.06)
http://en wikipedia org/wiki/Agent based modelhttp://en.wikipedia.org/wiki/Agent-based_model
p. 21

22. Information
“The word “information” has been given different meanings by
various writers in the general field of information theory It isvarious writers in the general field of information theory. It is
likely that at least a number of these will prove sufficiently useful
in certain applications to deserve further study and permanent
recognition.
It is hardly to be expected that a single concept of information
would satisfactorily account for the numerous possible
applications of this general field. “
C Shannon, 1993, “The Lattice Theory of Information”
22

23. Information
“Yet, despite its ubiquity in everyday experience, the concept of
information is conspicuously absent in the theories of classical physicsinformation is conspicuously absent in the theories of classical physics
(such as classical mechanics and electromagnetism). The reason for
this is two-fold. First, in the classical framework, the universe evolves
deterministically and reversibly - the past fully determines the future
and conversely the future determines the past so that no informationand, conversely, the future determines the past, so that no information
is lost or gained over the passage of time.
Second, agents can always, in principle, perform measurements that
yield perfect and complete information about the state of reality.
Th f t i i i l h th “G d' " i fTherefore, every agent in principle has the same “God's eye" view of
reality. (…)
For these two reasons, the concept of information is essentially
redundant in classical physics.”p y
Goyal P., Deciphering Quantum Theory. In "Are we there yet? The search for a theory of
everything", Ed. M. Emam (Bentham Press, 2011)
http://www.philipgoyal.org/resources/Papers/Introductory-Papers/DecipheringQT2010.pdf
23

24. What is Information?
A special issue of the Journal of Logic, Language and
Information (Volume 12 No 4 2003) is dedicated to theInformation (Volume 12 No 4 2003) is dedicated to the
different facets of information.
A H db k h Phil h f I f i (VA Handbook on the Philosophy of Information (Van
Benthem, Adriaans) is in preparation as one volume
Handbook of the philosophy of science.
http://www.illc.uva.nl/HPI/
p. 24

25. Information as The Primary Stuff ofy
the Universe - Paninformationalism
If information is to replace matter/energy as the primary stuff of
the universe, as H von Baeyer (2003) suggests, it will provide a
new basic unifying framework for describing and predictingnew basic unifying framework for describing and predicting
reality in the twenty-first century.
L Floridi proposes Informational Structural Realism - a view ofp p
the world as the totality of informational objects dynamically
interacting with each other (Synthese Vol.161, Number 2, 219
253, A defence of informational structural realism).
p. 25

26. Computation as Informationp
Processing
With information as the primary stuff of the universe, the most
general view of computation is as time-dependent behavior
(dynamics) of information.(dynamics) of information.
This results in a Dual-aspect Universe: informational structure with
computational dynamics (Info Computationalism Dodig Crnkovic)computational dynamics. (Info-Computationalism, Dodig Crnkovic)
Information and computation are closely related – no computation
ith t i f ti ( t t ) d i f ti ith twithout information (structure), and no information without
computation (dynamics).
p. 26

27. Computation. Computing Nature and
Nature Inspired ComputationNature Inspired Computation
If it looks like a duck,
if it walks like a duck
and it quacks like a duck,
is it a duck?
(If it looks like computation is it computation?)
http://www.bsse.ethz.ch/synbio
http://synbio.mit.edu/research.html
27

28. The Universe as a Computer -
P t ti liPancomputationalism
We are all living inside a gigantic computer.
No, not The Matrix: the Universe.
Every process, every change that takes
place in the Universe, may be considered
as a kind of computation.
K Zuse, N Wiener, E Fredkin, S Wolfram, G
Chaitin, S Lloyd, G ‘t Hooft, C Seife, D
Deutsch CF von Weizsäcker JA WheelerDeutsch, CF von Weizsäcker, JA Wheeler,
among others
Simulation of the formation of
cosmological structures
http://www.youtube.com/watch?feature=player_embedded&v=-ZcEDqyMbFw
http://www.idt.mdh.se/personal/gdc/work/Pancomputationalism.mht
http://irfu.cea.fr/Sap/en/Phocea/Vie_des_labos/Ast/ast_sstheme.php?id_ast=1286
28

29. Turing Machine Model of Computation
Tape
............
Control Unit
Read-Write head
Control Unit
1. Reads a symbol
W i b l2. Writes a symbol
3. Moves Left or Right
The definition of computation is currently under debate, and an entire issue of the
journal Minds and Machines (1994, 4, 4) was devoted to the question “What is
Computation?”Computation?
http://plato.stanford.edu/entries/turing-machine/ 29

30. Beyond Conventional Computingy p g
Machinery: Natural Computing
● According to the Handbook of Natural Computing
natural computing is “the field of research that
investigates both human-designed computinginvestigates both human designed computing
inspired by nature and computing taking place in
nature.”
● It includes among others areas of cellular automata● It includes among others areas of cellular automata
and neural computation, evolutionary computation,
molecular computation, quantum computation, nature-
inspired algorithms and alternative models ofp g
computation.
p. 30

31. Beyond Conventional Computingy p g
Machinery: Natural Computing
● Denning argue that computing today is a natural
science. Natural computation provides a basis for a
unified understanding of phenomena of embodiedunified understanding of phenomena of embodied
cognition, intelligence and knowledge generation.
● ”I invite readers not on a visit to an archaeological● I invite readers not on a visit to an archaeological
museum, but rather on an adventure in science in
making” Prigogine in The End of Certainty: Time,
Chaos and New Laws of Nature, 1997Chaos and New Laws of Nature, 1997
p. 31

32. Turing Machines Limitations –
Self-generating Systems
According to George Kampis, http://hps.elte.hu/~gk/ complex
biological systems must be modeled as self-referential, self-
organizing systems called "component-systems" (self-
generating systems), whose behavior, though computational in
a generalized sense, goes far beyond Turing machine model.
p. 32

33. The Wildfire Spread of Computationalp p
Ideas
"Everyone knows that computational and information technology has
spread like wildfire throughout academic and intellectual life. But the
spread of computational ideas has been just as impressive. Biologists not
only model life forms on computers; they treat the gene, and even whole
organisms, as information systems. Philosophy, artificial intelligence, and
cognitive science don't just construct computational models of mind; theyg j p ; y
take cognition to be computation, at the deepest levels. …
Physicists don't just talk about the information carried by a subatomic
particle; they propose to unify the foundations of quantum mechanics with
notions of information “notions of information.
Brian Cantwell Smith, The Wildfire Spread of Computational Ideas, 2003
p. 33

34. Cognition as Computation
(Processing of Information)(Processing of Information)
Networks at the Basis of Cognition
100 billions of neurons connected
with tiny "wires" in total longer
more than two times the earth
circumference. This intricate and
apparently messy neural circuit
th t i ibl fthat is responsible for our
cognition and behavior.
http://www.istc.cnr.it/group/locen
Biophysics of Computation: Information Processing in Single Neurons
Christof Koch, 1999. http://www.klab.caltech.edu/~koch/biophysics-book/ p. 34

35. http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/full
http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/
35

36. What is Cognition?
● Process of perceiving
g
● Process of perceiving,
reasoning, decision
making and thinking.
● Body’s & brain ’s way of
processing information to
create meaning / makecreate meaning / make
sense for an agent.
36

37. Cognitive Network TechnologyCog t e et o ec o ogy
http://iptechwiki.cttc.es/Cognitive_Network_Optimization_Techniques 37

38. The Human Brain Projectj
(€500 million 10-year, FET flagship)
The human brain project has ten years to build the brain withThe human brain project has ten years to build the brain with
supercomputers in collaboration between 80 research
institutions in Europe. http://www.humanbrainproject.eu/
EPFL Lausanne, CH
Prof Henry MarkramProf. Henry Markram
28th January 2013
http://www.popsci.com/science/article/2013-02/
how-simulate-human-brain-one-neuron-time-13-billion p. 38

39. Naturalized Epistemology
Data – Information - KnowledgeData Information - Knowledge
Information Processing
“Dynamics lead to statics, statics leads to dynamics, and the
simultaneous analysis of the two provides the beginning of an
understanding of that mysterious process called mind ”understanding of that mysterious process called mind.
Goertzel, Chaotic Logic
Self-generating computation systems
“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 software definesthe hardware defines the software and the software defines
new hardware. Then the circle starts again.”
George Kampis p 223 Self-Modifying Systems in Biology andGeorge Kampis, p. 223 Self-Modifying Systems in Biology and
Cognitive Science
39

40. Naturalized Epistemology
Data – Information - Knowledge asData Information - Knowledge as
Information Processing
Interactive explanation is future oriented, based on the fact that the
agent is concerned with the anticipated future potentialities ofagent is concerned with the anticipated future potentialities of
interaction.
The actions are oriented internally to the system, which optimizes
their external outcome.their external outcome.
The environment in the interactive case represents resources and
constraints for the agent.
Information exchange with the environment is a part of interactiveInformation exchange with the environment is a part of interactive
relation.
40

41. How Does Nature Compute?
Evolution + Self-OrganizationEvolution + Self-Organization
C iti f th l ti h ti thCritics of the evolutionary approach mention the
impossibility of “blind chance” to produce such
highly complex structures as intelligent living
organisms Proverbial monkeys typingorganisms. Proverbial monkeys typing
Shakespeare are often used as illustration (an
interesting account is given by Gell-Man in his
Quark and the Jaguar )Quark and the Jaguar.)
Chaitin and Bennet: Typing monkeys’ argument
does not take into account physical laws of the
universe which dramatically limit what can beuniverse, which dramatically limit what can be
typed. Moreover, the universe is not a typewriter,
but a computer, so a monkey types random input
into a computer The computer interprets theinto a computer. The computer interprets the
strings as programs.
41

42. Naturalizing Epistemology
Naturalized epistemology (Feldman, Kornblith, Stich) is, in general, anp gy ( , , ) , g ,
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
t j t h thi t ti ll d ? Wh t ttlnot just see how this construction really proceeds? Why not settle
for psychology? ("Epistemology Naturalized", Quine 1969; emphasis
mine)
I will re-phrase the question to be: Why not settle for computing?
Epistemology is the branch of philosophy that studies the nature, methods, limitations,
and validity of knowledge and belief.
42

43. Why not Settle for Computing
h M d li C iti ?when Modeling Cognition?
In this framework knowledge is seen as a result of the structuring of
input data:
data → information → knowledgedata → information → knowledge
by an interactive computational process going on in the nervous
system during the adaptive interplay of an agent with the
environment which clearly increases its ability to cope with theenvironment, which clearly increases its ability to cope with the
dynamical changing of the world.
Hebbian learningHebbian learning
http://en.wikipedia.org/wiki/Hebbian_theory
p. 43

44. Data – Information - Knowledge asg
Information Processing
Pragmatism suggests that interaction is the most appropriate
framework for understanding of cognition.
Interactive explanation is future oriented; based on the fact that
the agent is concerned with anticipated future potentialities of
interaction.
44

45. Data – Information - Knowledge asg
Information Processing
The actions are oriented internally to the system, which
optimizes their internal outcome, while the environment in the
interactive case represents primarily resources for the agent.
Correspondence (mutual influence) with the environment is a
part of interactive relation.
[One can say that living organisms are “about” the environment, that they have
developed adaptive strategies to survive by internalizing environmental
constraints. The interaction between an organism and its environment is realized
through the exchange of physical signals that might be seen as data, or when
structured, as information. Organizing and mutually relating different pieces of
information results in knowledge. In that context, computationalism appears as
the most suitable framework for naturalizing epistemology. ]
45

46. Cognition as Computation
Information/computation mechanisms areInformation/computation mechanisms are
fundamental for evolution of intelligent agents.
Their role is to adapt the physical structure and
behavior that will increase organisms chances ofg
survival, or otherwise induce some other
behavior that might be a preference of an agent.
http://www.worldhealth.net/news/
hormone-therapy-helps-improve-cognition
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
46

47. Naturalist
Understanding ofUnderstanding of
Cognition
A great conceptual advantage of cognition as a central focus of study is
that all living organisms possess some cognition, in some degree.g g p g , g
Maturana and Varela (1980)
Maturana’s and Varelas’ understanding ofg
cognition is most suitable as the basis for a
computationalist account of the naturalized
evolutionary epistemology.
See also:
Gordana Dodig-Crnkovic
Where do New Ideas Come From?
How do they Emerge?
Epistemology as Computation (InformationEpistemology as Computation (Information
Processing)
in Randomness & Complexity, from Leibniz to
Chaitin, C. Calude ed. 2007
p. 47

48. Unified Info-Computational
N t li i f E i t lNaturalizing of Epistemology
At the physical level, living beings are open 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
ibilit d hi h i f ti t tcompressibility and high information content.
Fixed points are like crystals in that they are for the most part static and
orderly Chaotic dynamics are similar to gases which can be described onlyorderly. Chaotic dynamics are similar to gases, which can be described only
statistically. Periodic behavior is similar to a non-crystal solid, and
complexity is like a liquid that is close to both the solid and the gaseous
states. In this way, we can once again view complexity and computation as
existing on the edge of chaos and simplicity. (Flake 1998)
p. 48

49. Unified Info-Computational
N t li i f E i t lNaturalizing of Epistemology
Dynamics of Open Systems
Based on the natural phenomena understood as info computationalBased on the natural phenomena understood as info-computational,
computer in general is conceived as an open interactive system (digital
or analogue; discrete or continuous) in the communication with the
environment.
Classical Turing machine is a subset of a more general
interactive/adaptive/self-organizing universal natural computer. Living
fsystem is defined as "open, coherent, space- time structure maintained
far from thermodynamic equilibrium by a flow of energy through it."
(Chaisson, 2002.)
On a computationalist view, the dynamics of an organism are
constituted by computational processes. In the open system of living
cell an info-computational process takes place using DNA, exchangingp p p g g g
information, matter and energy with the environment.
p. 49

50. Cognition as Re-structuring an
A t th h I t ti ith
Info computationalist project of naturalizing epistemology by
Agent through Interaction with
the Environment
Info-computationalist project of naturalizing epistemology by
defining cognition as information processing phenomenon is
closely related to the development of multilevel dynamical
computational models and simulations of cognizing systemscomputational models and simulations of cognizing systems,
and has important consequences for the development of
artificial intelligence and artificial life.
Natural computation opens possibilities to implement embodied
cognition into artificial agents, and perform experiments on
simulated eco-systemssimulated eco systems.
p. 50

51. Unified Info-Computational Theory
Continuum-discrete controversy is bridged by the same dual-aspect
approach (Info-Computationalism).
Thi t th t i t t ti l i d hi h l iThis counters the argument against computational mind which claims
that computational mind must be discrete.
It is also an answer to the critique that the universe might not beIt is also an answer to the critique that the universe might not be
computational as it might not be entirely digital.
Computation as information processing can be both continuous and
discretediscrete.
p. 51

52. Unified Info-Computational Theory
Computationalist and informationalist frameworks meet in the common
domain of complex systemsdomain of complex systems.
The Turing Machine model is about mechanical, syntactic symbol
manipulation as implemented on the hardware level.
C l it i t b f d th ft l l Diff t l l fComplexity is to be found on the software level. Different levels of
complexity have different meanings for different cognizing agents.
p. 52

53. Computation in Closed vs. Openp p
Systems
● Since the 1950s computational machinery has been● Since the 1950s computational machinery has been
increasingly used to exchange information and computers
gradually started to connect in networks and communicate. In
the 1970s computers were connected via telecommunicationsthe 1970s computers were connected via telecommunications.
● The emergence of networking involved a rethinking of the
nature of computation and boundaries of a computernature of computation and boundaries of a computer.
Computer operating systems and applications were modified to
access the resources of other computers in the network. In
1991 CERN created the World Wide Web which resulted in1991 CERN created the World Wide Web, which resulted in
computer networking becoming a part of everyday life for
common people.
p. 53

54. Computation in Closed vs. Openp p
Systems
● With the development of computer networks two characteristics● With the development of computer networks, two characteristics
of computing systems have become increasingly important:
parallelism/concurrency and openness – both based on
communication between computational unitscommunication between computational units.
● Comparing new open-system with traditional closed-system
computation models Hewitt characterizes the Turing machinecomputation models, Hewitt characterizes the Turing machine
model as an internal (individual) framework and his own
Actor model of concurrent computation as an external
(sociological) model of computing(sociological) model of computing.
p. 54

55. Computation as Interaction andp
Interactive Computing
The following characteristics distinguish this new interactiveThe following characteristics distinguish this new, interactive
notion of computation (Wegner, Goldin):
C t ti l bl i d fi d f i t k [i● Computational problem is defined as performing a task, [in a
dynamical environment – my addition] rather than
(algorithmically) producing an answer to a question.
● Dynamic input and output are modelled by dynamic streams
which are interleaved; later values of the input stream may
d d li l i th t t t d idepend on earlier values in the output stream and vice versa.
p. 55

56. Computation as Interaction andp
Interactive Computing
● The environment of the computation is a part of the model,
playing an active role in the computation by dynamically
supplying the computational system with the inputs, andsupplying the computational system with the inputs, and
consuming the output values from the system.
● Concurrency: the computing system (agent) computes in
parallel with its environment, and with other agents. (Agents canparallel with its environment, and with other agents. (Agents can
consist of agents networks, recursively.)
● Effective non-computability: the environment cannot be
assumed to be static or effectively computable. We cannotassumed to be static or effectively computable. We cannot
always pre-compute input values or predict the effect of the
system's output on the environment.
p. 56

57. Concurrencyy
The advantages of concurrency theory that is used to simulate
observable natural phenomena are that:
“it is possible to express much richer notions of time and space
in the concurrent interactive framework than in a sequential
one. In the case of time, for example, instead of a unique total
order, we now have interplay between many partial orders of
events--the local times of concurrent agents--with potential
synchronizations, and the possibility to add global constraints
on the set of possible scheduling. This requires a much more
complex algebraic structure of representation if one wants tocomplex algebraic structure of representation if one wants to
"situate" a given agent in time, i.e., relatively to the occurrence
of events originated by herself or by other agents.“
V Schachter “How Does Concurrency Extend the Paradigm ofV. Schachter, How Does Concurrency Extend the Paradigm of
Computation?,” Monist, vol. 82, no. 1, pp. 37–58, 1999.
p. 57

58. Digital vs. Analog, Discrete vs.g g,
Continuous and Symbolic vs.
Sub-symbolic Computationy p
● “Symbolic simulation is thus a two-stage affair: first the
i f i f t t f th th t h dmapping of inference structure of the theory onto hardware
states which defines symbolic computation; second, the
mapping of inference structure of the theory onto hardware
states which (under appropriate conditions) qualifies thestates which (under appropriate conditions) qualifies the
processing as a symbolic simulation.
● Analog simulation, in contrast, is defined by a single
mapping from causal relations among elements of themapping from causal relations among elements of the
simulation to causal relations among elements of the
simulated phenomenon.”
R. Trenholme, “Analog Simulation,” Philosophy of Science, vol. 61, no. 1,
pp. 115–131, 1994. p. 58

59. Symbolic vs. Sub-symbolicy y
Computation
Douglas Hofstadter in his dialogue “Prelude…Ant fugue” in Godel, Escher, Bach.
p. 59

60. The Unreasonable Ineffectiveness
of Mathematics in Biology and Bias
of Mathematicians
● “The unreasonable effectiveness of mathematics” as
observed in physics by Wigner is missing in biologyobserved in physics by Wigner is missing in biology.
● Mathematics is disembodied* while computing is
b di d i hi th t i t ith thembodied in a machine that can communicate with the
physical world in real time.
* Actually embodied in a human who takes the embodiment as granted
and disregards its constraints. p. 60

61. The Unreasonable Ineffectiveness
Of Mathematics In Biology And Bias
Of Mathematicians
Beyond the limits of TM model:
● Embodiment invalidating the `machine as data' and universality
paradigm.
● The organic linking of mechanics and emergent outcomes
delivering a clearer model of supervenience of mentality on
brain functionality, and a reconciliation of different levels of
effectivity.
● A reaffirmation of experiment and evolving hardware, for both
AI and extended computing generally.
● The validating of a route to creation of new information through
interaction and emergence.
S. B. Cooper, “Turing’s Titanic Machine?,” Communications of
the ACM, vol. 55, no. 3, pp. 74–83, 2012p. 61

62. Info-computationalism,p ,
in a Nutshell
● Nature can be described as a complex informational
structure for a cognizing agent.
● Computation is information dynamics (information
processing)
● Computation is constrained and governed by the laws
of physics on the fundamental level.p y
p. 62

63. Morphological Computing.p g p g
Meaning Generation From Raw
Data To Semantic Information
● Turing proposed diffusion reaction model of morphogenesis as
the explanation of the development of biological patterns such as
the spots and stripes on animal skin.
● Morphogenesis is a process of morphological computing.
Physical process – though not computational in the traditional
sense, presents natural (unconventional), morphological
computation. Essential element in this process is the interplay
between the informational structure and the computational
i f ti lf t t i d i f ti i t tiprocess - information self-structuring and information integration,
both synchronic and diachronic, going on in different time and
space scales in physical bodies.
p. 63

64. Morphological Computing.p g p g
Meaning Generation From Raw
Data To Semantic Information
● Info-computational naturalism describes nature as
informational structure – a succession of levels ofinformational structure a succession of levels of
organization of information.
● Morphological computing on that informational● Morphological computing on that informational
structure leads to new informational structures via
processes of self-organization of information.
● Evolution itself is a process of morphological
computation on a long-term scale.
p. 64

65. Conclusions & Future Work
Present account highlights several topics ofPresent account highlights several topics of
importance for the development of new understanding
of computation and its role in the physical world:
● Natural computation● Natural computation
● The relationship between model and physical
implementation
I t ti it f d t l f t ti l● Interactivity as fundamental for computational
modelling of concurrent information processing
systems such as living organisms and their networks
N d l t i th ti l d lli d d● New developments in mathematical modelling needed
to support this generalized framework.
p. 65

66. Conclusions & Future Work
● Besides the Turing machine as a well developed and● Besides the Turing machine as a well developed and
generally established model of computation, variety of
new ideas, still under development are taking shape
and have good prospects to extend our understandingand have good prospects to extend our understanding
of computation and its relationship to physical
implementations.
● It will be instructive within the info-computational
framework to study processes of self organization of
information in physical agents (as well as in populationinformation in physical agents (as well as in population
of agents) able to re-structure themselves through
interactions with the environment as a result of
morphological (morphogenetic) computation.p g ( p g ) p
p. 66

67. “(O)ur task is nothing less than to discover a new,( ) g ,
broader, notion of computation, and to understand the
world around us in terms of information processing.”
G. Rozenberg and L. Kari, “The many facets of natural computing,”g , y p g,
Communications of the ACM, vol. 51, pp. 72–83, 2008.
p. 67

68. Qualitative Complexityp y
Ecology Cognitive Processes and the Re-Ecology, Cognitive Processes and the Re
Emergence of Structures in Post-Humanist
Social Theory
By John Smith, Chris Jenks
p. 68

69. Special Sciences andSpecial Sciences and
the Unity of Science
p. 69

70. Books in the New Paradigm:
A New Kind of Science
Book available at:
http://www.wolframscience.com
Based on cellular automata, complexity emerging
from repeating very simple rules
See also
http://www.youtube.com/watch?v=_eC14GonZnU
A New Kind of Science - Stephen Wolfram
p. 70

71. Programming the Universe:
A Quantum ComputerA Quantum Computer
Scientist Takes on the
CosmosCosmos
b S th Ll dby Seth Lloyd
p. 71

72. The Universe as Quantum
I f iInformation
p. 72

73. A New Paradigm of Computing
I i C i– Interactive Computing
Interactive Computation: the New Paradigm
Springer-Verlag in September 2006g g
Dina Goldin, Scott Smolka, Peter Wegner, eds.
--------------------------------------
Dina Goldin, Peter Wegner
The Interactive Nature of Computing:
Refuting the Strong Church - Turing ThesisRefuting the Strong Church - Turing Thesis
Minds and Machines
Volume 18 , Issue 1 (March 2008) p 17 - 38
http://www.cs.brown.edu/people/pw/strong-cct.pdf
p. 73

74. Self-modifying Systems in
Bi l d C i i S iBiology and Cognitive Science
The theme of this book is the self-generation
of information by the self-modification ofy
systems. The author explains why biological
and cognitive processes exhibit identity
changes in the mathematical and logical
sense This concept is the basis of a newsense. This concept is the basis of a new
organizational principle which utilizes shifts of
the internal semantic relations in systems.
More about the book:
ftp://wwwc3.lanl.gov/pub/users/joslyn/kamp_rev.pdf
p. 74

75. A Computable Universe
p. 75

76. Computation, Information, Cognitionp g
Computation, Information,
Cognition
Editor(s): Gordana Dodig Crnkovic and
Information and Computation
Editor(s): Gordana Dodig Crnkovic and
Mark Burgin, World Scientific, 2011
Computing Nature
Editor(s): Gordana Dodig Crnkovic and
Raffaela Giovagnoli Springer 2013Editor(s): Gordana Dodig Crnkovic and
Susan Stuart, Cambridge Scholars
Publishing, 2007
g , , Raffaela Giovagnoli, Springer, 2013
http://dx.doi.org/10.1007/978-3-642-37225-4 p. 76