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REDUCTIONISM AND COMPLEXITY:CONTINUUM OR DICHOTOMY?. DON MIKULECKY PROFESSOR EMERITUS OF PHYSIOLOGY AND SENIOR FELLOW IN THE CENTER FOR THE STUDY OF BIOLOGICAL COMPLEXITY-VCU http://www.people.vcu.edu/~mikuleck/. ONE OF THE MAIN FUNCTIONS OF REDUCTIONISM IN SOCIETY.

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reductionism and complexity continuum or dichotomy

REDUCTIONISM AND COMPLEXITY:CONTINUUM OR DICHOTOMY?

DON MIKULECKY

PROFESSOR EMERITUS OF PHYSIOLOGY AND SENIOR FELLOW IN THE CENTER FOR THE STUDY OF BIOLOGICAL COMPLEXITY-VCU

http://www.people.vcu.edu/~mikuleck/

one of the main functions of reductionism in society
ONE OF THE MAIN FUNCTIONS OF REDUCTIONISM IN SOCIETY
  • IF THE SYSTEM IS CORRUPT THEN HOW CAN A PERSON WHO WANTS NOT TO PARTICIPATE IN CORRUPTION BE A PARTICIPANT?
  • HE MUST REDUCE THE SYSTEM TO UNRELATED ENDEAVORS SO THAT HE CAN ESCAPE RECOGNIZING HIS PARTICIPATION IN THE CORRUPT WHOLE
complexity
COMPLEXITY
  • REQUIRES A CIRCLE OF IDEAS AND METHODS THAT DEPART RADICALLY FROM THOSE TAKEN AS AXIOMATIC FOR THE PAST 300 YEARS
  • OUR CURRENT SYSTEMS THEORY, INCLUDING ALL THAT IS TAKEN FROM PHYSICS OR PHYSICAL SCIENCE, DEALS EXCLUSIVELY WITH SIMPLE SYSTEMS OR MECHANISMS
  • COMPLEX AND SIMPLE SYSTEMS ARE DISJOINT CATEGORIES
complexity vs complication
COMPLEXITY VS COMPLICATION
  • Von NEUMAN THOUGHT THAT A CRITICAL LEVEL OF “SYSTEM SIZE” WOULD “TRIGGER” THE ONSET OF “COMPLEXITY” (REALLY COMPLICATION)
  • COMPLEXITY IS MORE A FUNCTION OF SYSTEM QUALITIES RATHER THAN SIZE
  • COMPLEXITY RESULTS FROM BIFURCATIONS -NOT IN THE DYNAMICS, BUT IN THE DESCRIPTION!
  • THUS COMPLEX SYSTEMS REQUIRE THAT THEY BE ENCODED INTO MORE THAN ONE FORMAL SYSTEM IN ORDER TO BE MORE COMPLETELY UNDERSTOOD
some of my giants
SOME OF MY GIANTS:
  • AHARON KATZIR-KATCHALSKY (died in terrorist massacre in Lod Airport 1972)
  • LEONARDO PEUSNER (alive and well in Argentina)
  • ROBERT ROSEN (died December 29, 1998)
some references
SOME REFERENCES
  • FOR A BIBLIOGRAPHY OF ROSEN’S WORK: http://views.vcu.edu/complex/
  • Pusner, Leonardo: Two books on network thermodynamics
  • My book: Application of network thermodynamics to problems in biomedical engineering, NYU Press, 1993
recent work
Recent work:
  • New review:The Circle That Never Ends: Can Complexity Be Made Simple? In Complexity in Chemistry, Biology, and EcologyBonchev, Danail D.; Rouvray, Dennis (Eds.) 2005
  • New Book: Into the Cool: Energy Flow, Thermodynamics and Life by: Eric D. Schneider and Dorion Sagan, University of Chicago Press, 2005
the modeling relation the essence of science
THE MODELING RELATION: THE ESSENCE OF SCIENCE
  • ALLOWS US TO ASSIGN MEANING TO THE WORLD AROUND US
  • STANDS FOR OUR THINKING PROCESS
  • CAUSALITY IN THE NATURAL SYSTEM IS DEALT WITH THROUGH IMPLICATION IN A FORMAL SYSTEM
  • THERE IS AN ENCODING OF THE NATURAL SYSTEM INTO THE FORMAL SYSTEM AND A DECODING BACK
  • WHEN IT ALL HANGS TOGETHER WE HAVE A MODEL
the modeling relation a model of how we make models a science of framing

ENCODING

NATURAL

SYSTEM

FORMAL

SYSTEM

CAUSAL

EVENT

MANIPULATION

DECODING

FORMAL

SYSTEM

NATURAL

SYSTEM

THE MODELING RELATION: A MODEL OF HOW WE MAKE MODELS, A SCIENCE OF FRAMING
we have a useful model when
WE HAVE A USEFUL MODEL WHEN

AND

ARE SATISFACTORY WAYS OF “UNDERSTANDING”

THE CHANGE IN THE WORLD “OUT THERE”

the modeling relation a model of how we make models

ENCODING

NATURAL

SYSTEM

FORMAL

SYSTEM

CAUSAL

EVENT

IMPLICATION

DECODING

FORMAL

SYSTEM

NATURAL

SYSTEM

THE MODELING RELATION: A MODEL OF HOW WE MAKE MODELS
more on the modeling relation
MORE ON THE MODELING RELATION
  • THE FORMAL SYSTEM DOES NOT INCLUDE INFORMATION ABOUT ENCODING AND/OR DECODING
  • THEREFORE MODELING WILL ALWAYS BE AN ART
  • ONLY IN THE NEWTONIAN PARADIGM DOES THE FORMAL SYSTEM BECOME THE NATURAL SYSTEM (ENCODING AND DECODING ARE AUTOMATIC) AND ALL THAT IS LEFT TO DO IS TO MEASURE THINGS
why is objectivity a myth or why is science a belief structure
WHY IS “OBJECTIVITY” A MYTH? (OR: WHY IS SCIENCE A BELIEF STRUCTURE)
  • THE FORMAL SYSTEM DOES NOT AND CAN NOT TELL US HOW TO ENCODE AND DECODE. (MODELING IS AN ART!)
  • THE FORMAL SYSTEM DOES NOT AND CAN NOT TELL US WHEN THE MODEL WORKS, THAT IS A JUDGEMENT CALL EVEN IF OTHER FORMALISMS ARE ENLISTED TO HELP (FOR EXAMPLE: STATISTICS)
  • MODELS EXIST IN A CONTEXT: A FRAME
why what traditional science did to the modeling relation made the present situation inevitable
WHY WHAT “TRADITIONAL SCIENCE” DID TO THE MODELING RELATION MADE THE PRESENT SITUATION INEVITABLE:
  • WE ARE TOO AFRAID OF “BELIEFS” (SCEPTICISM IS “IN”)
  • WE DEVELOPED THE MYTH OF “OBJECTIVITY”
what is framing the question
WHAT IS “FRAMING THE QUESTION”?
  • Based on the work of George Lakoff
  • Cognitive Linguistics
  • Frames are the mental structures that shape the way we see the world
  • Facts, data, models, etc. only have meaning in a context
  • Leads us to a scientific application of framing: Rosen’s theory of complexity
framing the question
Framing the question
  • Don’t think of an elephant
  • Impossibility of avoiding the frame
  • In science the dominant frame is reductionism and the associated mechanical thinking
  • The dominant modern manifestations include molecular biology and nonlinear dynamics
why are there so many definitions of complexity
WHY ARE THERE SO MANY DEFINITIONS OF COMPLEXITY?
  • SCIENTISTS FOCUS ON THE FORMAL DESCRIPTION RATHER THAN THE REAL WORLD
  • THE REAL WORLD IS COMPLEX
  • FORMAL SYSTEMS COME IN VARYING SHADES AND DEGREES OF COMPLICATION
reductionism has framed complexity theory
Reductionism has framed complexity theory
  • Rather than change methods we have the changed names for what we do
  • The consequences are significant
  • It is impossible for you to believe what is being taught in this lecture and to then simply add it to your repertoire
  • The reason is that in order to see the world in a new way you have to step out of the traditional frame and into a new one. Once done, you can never go back. The ability to reframe a question is the basis for change and broadening of ideas.
what traditional science did to frame the modeling relation

FORMAL

SYSTEM

NATURAL

SYSTEM

MANIPULATION

CAUSAL

EVENT

FORMAL

SYSTEM

NATURAL

SYSTEM

WHAT “TRADITIONAL SCIENCE” DID TO FRAME THE MODELING RELATION
what traditional science did to frame the modeling relation21

FORMAL

SYSTEM

NATURAL

SYSTEM

MANIPULATION

FORMAL

SYSTEM

NATURAL

SYSTEM

WHAT “TRADITIONAL SCIENCE” DID TO FRAME THE MODELING RELATION
why what traditional science did to the modeling relation made the present situation inevitable22
WHY WHAT “TRADITIONAL SCIENCE” DID TO THE MODELING RELATION MADE THE PRESENT SITUATION INEVITABLE:
  • WE MORE OR LESS FORGOT THAT THERE WAS AN ENCODING AND DECODING
slide23
WHY WHAT “TRADITIONAL SCIENCE” DID TO THE MODELING RELATION MADE THE PRESENT SITUATION INEVITABLE: IT FRAMED THE QUESTIN
  • THE “REAL WORLD” REQUIRES MORE THAN ONE “FORMAL SYSTEM” TO MODEL IT (THERE IS NO “UNIVERSAL MODEL”)
syntax vs semantics
Syntax vs Semantics
  • The map is not the territory
  • An equation is just an equation without interpretation
  • This means we use formalisms in a context
  • This context dependence also exists in nature
  • This is one reason why there can never be a largest model
context dependence necessarily introduces circularity
Context dependence necessarily introduces circularity
  • A process happens in a context
  • The process usually changes that context
  • If the context changes the process usually changes as a result.
  • Living systems are replete with examples of this
self reference circularity and the genome
SELF-REFERENCE, CIRCULARITY AND THE GENOME

REPLICATION

ENZYMES

DNA

PROTEINS

TRANSCRIPTION

homeostasis
HOMEOSTASIS

MILLEU FOR CELLS, TISSUES AND ORGANS

TISSUES

AND

ORGANS

CELLS

can we get rid of self reference that is circularity
CAN WE GET RID OF SELF-REFERENCE, THAT IS, CIRCULARITY?
  • IT HAS BEEN TRIED
  • IT FAILED
  • THE ALTERNATIVE IS TO “GO AROUND” IT – THAT IS TO IGNORE CASES WHERE IT POPS UP
  • WHAT IF IT IS VERY COMMON?
what is complexity
WHAT IS COMPLEXITY?
  • TOO MANY DEFINITIONS, SOME CONFLICTING
  • OFTEN INTERCHANGED WITH “COMPLICATED”
  • HAS A REAL MEANING BUT AFTER THE QUESTION IS REFRAMED
  • THAT MEANING ITSELF IS COMPLEX(THIS IS SELF-REFERENTIAL: HOW CAN WE DEFINE “COMPLEX” USING “COMPLEX”?)
rosen s concept for complexity a new frame
ROSEN’S CONCEPT FOR COMPLEXITY: A NEW FRAME

Complexity is the property of a real world system that is manifest in the inability of any one formalism being adequate to capture all its properties. It requires that we find distinctly different ways of interacting with systems. Distinctly different in

the sense that when we make successful models, the formal systems needed to describe each distinct aspect are NOT

derivable from each other

slide31

The Mexican sierra [fish] has "XVII-15-IX" spines in the dorsal fin. These can easily be counted ... We could, if we wished, describe the sierra thus: "D. XVII-15-IX; A. II-15-IX," but we could see the fish alive and swimming, feel it plunge against the lines, drag it threshing over the rail, and even finally eat it. And there is no reason why either approach should be inaccurate.

Spine-count description need not suffer because another approach is also used. Perhaps, out of the two approaches we thought there might emerge a picture more complete and even more accurate that either alone could produce. -- John Steinbeck, novelist, with Edward Ricketts, marine biologist (1941)

complex systems vs simple mechanisms
COMPLEX

NO LARGEST MODEL

WHOLE MORE THAN SUM OF PARTS

CAUSAL RELATIONS RICH AND INTERTWINED

GENERIC

ANALYTIC  SYNTHETIC

NON-FRAGMENTABLE

NON-COMPUTABLE

REAL WORLD

SIMPLE

LARGEST MODEL

WHOLE IS SUM OF PARTS

CAUSAL RELATIONS DISTINCT

N0N-GENERIC

ANALYTIC = SYNTHETIC

FRAGMENTABLE

COMPUTABLE

FORMAL SYSTEM

COMPLEX SYSTEMS VS SIMPLE MECHANISMS
an example of reframing the question to get an answer the work of robert rosen
An Example of Reframing the question to get an answer : The work of Robert Rosen
  • What is life?
  • Why is an organism different from a machine?
robert rosen the well posed question and its answer why are organisms different from machines
ROBERT ROSEN: THE WELL POSED QUESTION AND ITS ANSWER-WHY ARE ORGANISMS DIFFERENT FROM MACHINES?
  • Rosen used relational ideas to apply category theory to living systems
  • These were called “Metabolism/Repair” systems oo M-R systems
  • Causal mappings were diagramed a syntax involving category theory mappings and the semantics were used along with this to apply the causal interpretaion
  • The result was a clear demonstration that the machine and the organism are disjoint in this context
  • An organism is closed to efficient cause while a machine is not
among other conclusion that can be drawn from this elegant study is one that might seem surprising
AMONG OTHER CONCLUSION THAT CAN BE DRAWN FROM THIS ELEGANT STUDY IS ONE THAT MIGHT SEEM SURPRISING
  • Since machines are causally impoverished, they lead to an infinite regress of causes.
  • Descartes led us to use the machine metaphor for organisms
  • In so doing he made a concept of God necessary
  • Today, “Intelligent Design” is based on this erroneous Cartesian metaphor: The Machine Metaphor
  • Real orgainisms are closed causually and escape this fallacy
what is science
WHAT IS SCIENCE?
  • HAS MANY DEFINTIONS
  • SOME OF THESE ARE IN CONFLICT
  • SCIENCE IS A BELIEF STRUCTURE
  • SCIENCE OF METHOD VS SCIENCE OF CONTENT
slide38
WHAT ARE SOME OF THE THINGS THAT MAKE “COMPLEXITY THEORY” NECESSARY? (WHAT HAS “TRADITIONAL SCIENCE” FAILED TO EXPLAIN?)
  • WHY IS THE WHOLE MORE THAN THE SOME OF THE PARTS?
  • SELF-REFERENCE AND CIRCULARITY
  • THE LIFE/ORGANISM PROBLEM
  • THE MIND/BODY PROBLEM
circularity self reference causes problems for logic and science
CIRCULARITY (SELF-REFERENCE) CAUSES PROBLEMS FOR LOGIC AND SCIENCE
  • I AM A CORINTHIAN
  • ALL CORINTHIANS ARE LIARS
  • OR
  • “THE STATEMENT ON THE OTHER SIDE IS FALSE”-ON BOTH SIDES
where do cells come from
WHERE DO CELLS COME FROM?
  • DNA?
  • GENES?
  • PROTEINS?
  • OTHER CELLS?
  • SPONTANEOUS GENERATION?
the cell theory
THE CELL THEORY
  • CELLS COME FROM OTHER CELLS
why what traditional science did to the question made the present situation inevitable
WHY WHAT “TRADITIONAL SCIENCE” DID TO THE QUESTION MADE THE PRESENT SITUATION INEVITABLE:
  • THE MACHINE METAPHOR TELLS US TO ASK “HOW?”
  • REAL WORLD COMPLEXITY TELLS US TO ASK “WHY?”
the four becauses why a house
THE FOUR BECAUSES: WHY A HOUSE?
  • MATERIAL: THE STUFF IT’S MADE OF
  • EFFICIENT: IT NEEDED A BUILDER
  • FORMAL: THERE WAS A BLUEPRINT
  • FINAL: IT HAS A PURPOSE
why is the whole more than the some of the parts
WHY IS THE WHOLE MORE THAN THE SOME OF THE PARTS?
  • BECAUSE REDUCING A REAL SYSTEM TO ATOMS AND MOLECULES LOOSES IMPORTANT THINGS THAT MAKE THE SYSTEM WHAT IT IS
  • BECAUSE THERE IS MORE TO REALITY THAN JUST ATOMS AND MOLECULES (ORGANIZATION, PROCESS, QUALITIES, ETC.)
self reference and circularity
SELF-REFERENCE AND CIRCULARITY
  • THE “LAWS” OF NATURE THAT TRADITIONAL SCIENCE TEACHES ARE ARTIFACTS OF A LIMITED MODEL
  • THE REAL “RULES OF THE GAME” ARE CONTEXT DEPENDENT AND EVER CHANGING- THEY MAKE THE CONTEXT AND THE CONTEXT MAKES THEM (SELF-REFERENCE)
example the life organism problem
EXAMPLE: THE LIFE/ORGANISM PROBLEM
  • LIFE IS CONSISTENT WITH THE LAWS OF PHYSICS
  • PHYSICS DOES NOT PREDICT LIFE
  • LIVING CELLS COME FROM OTHER LIVING CELLS
  • AN ORGANISM MUST INVOLVE CLOSED LOOPS OF CAUSALITY
  • LIFE DOES INVOLVE PURPOSE: See Into the cool
complexity is inescapable even in reductionism
Complexity is inescapable even in reductionism
  • Thermodynamics is an example of how attempts to remove complexity from reductionist thought can not succeed
  • The nature of thermodynamic reasoning had resisted this tendency very well and we will look at why this is so
some consequences
SOME CONSEQUENCES
  • REDUCTIONISM DID SERIOUS DAMAGE TO THERMODYNAMICS
  • THERMODYNAMICS IS MORE IN HARMONY WITH TOPOLOGICAL MATHEMATICS THAN IT IS WITH ANALYTICAL MATHEMATICS
  • THUS TOPOLOGY AND NOT MOLECULAR STATISTICS IS THE FUNDAMENTAL TOOL
examples
EXAMPLES:
  • CAROTHEODRY’S PROOF OF THE SECOND LAW OF THERMODYNAMICS
  • THE PROOF OF TELLEGEN’S THEOREM AND THE QUASI-POWER THEOREM
  • THE PROOF OF “ONSAGER’S” RECIPROCITY THEOREM
the nature of thermodynamic reasoning
THE NATURE OF THERMODYNAMIC REASONING
  • THERMODYNAMICS IS ABOUT THOSE PROPERTIES OF SYSTEMS WHICH ARE TRUE INDEPENDENT OF MECHANISM
  • THEREFORE WE CAN NOT LEARN TO DISTINGUISH MECHANISMS BY THERMODYNAMIC REASONING
networks in nature
NETWORKS IN NATURE
  • NATURE EDITORIAL: VOL 234, DECEMBER 17, 1971, pp380-381
  • “KATCHALSKY AND HIS COLLEAGUES SHOW, WITH EXAMPLES FROM MEMBRANE SYSTEMS, HOW THE TECHNIQUES DEVELOPED IN ENGINEERING SYSTEMS MIGHT BE APPLIED TO THE EXTREMELY HIGHLY CONNECTED AND INHOMOGENEOUS PATTERNS OF FORCES AND FLUXES WHICH ARE CHARACTERISTIC OF CELL BIOLOGY”
thermodynamics of open systems
THERMODYNAMICS OF OPEN SYSTEMS
  • THE NATURE OF THERMODYNAMIC REASONING
  • HOW CAN LIFE FIGHT ENTROPY?
  • WHAT ARE THERMODYNAMIC NETWORKS?
dissipation and the second law of thermodynamics
DISSIPATION AND THE SECOND LAW OF THERMODYNAMICS
  • ENTROPY MUST INCREASE IN A REAL PROCESS
  • IN A CLOSED SYSTEM THIS MEANS IT WILL ALWAYS GO TO EQUILIBRIUM
  • LIVING SYSTEMS ARE CLEARLY “SELF - ORGANIZING SYSTEMS”
  • HOW DO THEY REMAIN CONSISTENT WITH THIS LAW?
how can life fight entropy
HOW CAN LIFE FIGHT ENTROPY?
  • DISSIPATION AND THE SECOND LAW OF THERMODYNAMICS
  • PHENOMENOLOGICAL DESCRIPTION OF A SYTEM
  • COUPLED PROCESSES
  • STATIONARY STATES AWAY FROM EQUILIBRIUM
phenomenological description of a sytem
PHENOMENOLOGICAL DESCRIPTION OF A SYTEM
  • WE CHOSE TO LOOK AT FLOWS “THROUGH” A STRUCTURE AND DIFFERENCES “ACROSS” THAT STRUCTURE (DRIVING FORCES)
  • EXAMPLES ARE DIFFUSION, BULK FLOW, CURRENT FLOW
a generalisation for all linear flow processes
A GENERALISATION FOR ALL LINEAR FLOW PROCESSES

FLOW = CONDUCTANCE x FORCE

FORCE = RESISTANCE x FLOW

CONDUCTANCE = 1/RESISTANCE

coupled processes
COUPLED PROCESSES
  • KEDEM AND KATCHALSKY, LATE 1950’S
  • J1 = L11 X1 + L12 X2
  • J2 = L21 X1 + L22 X2
stationary states away from equilibrium and the second law of thermodynamics
STATIONARY STATESAWAY FROM EQUILIBRIUMAND THE SECOND LAW OF THERMODYNAMICS
  • T Ds/dt = J1 X1 +J2 X2 > 0
  • EITHER TERM CAN BE NEGATIVE IF THE OTHER IS POSITIVE AND OF GREATER MAGNITUDE
  • THUS COUPLING BETWEEN SYSTEMS ALLOWS THE GROWTH AND DEVELOPMENT OF SYSTEMS AS LONG AS THEY ARE OPEN!
stationary states away from equilibrium
STATIONARY STATES AWAY FROM EQUILIBRIUM
  • LIKE A CIRCUIT
  • REQUIRE A CONSTANT SOURCE OF ENERGY
  • SEEM TO BE TIME INDEPENDENT
  • HAS A FLOW GOING THROUGH IT
  • SYSTEM WILL GO TO EQUILIBRIUM IF ISLOATED
the resting cell
THE RESTING CELL
  • High potassium
  • Low Sodium
  • Na/K ATPase pump
  • Resting potential about 90 - 120 mV
  • Osmotically balanced (constant volume)
what are thermodynamic networks
WHAT ARE THERMODYNAMIC NETWORKS?
  • ELECTRICAL NETWORKS ARE THERMODYNAMIC
  • MOST DYNAMIC PHYSIOLOGICAL PROCESSES ARE ANALOGS OF ELECTRICAL PROCESSES
  • COUPLED PROCESSES HAVE A NATURAL REPRESENTATION AS MULTI-PORT NETWORKS
electrical networks are thermodynamic
ELECTRICAL NETWORKS ARE THERMODYNAMIC
  • RESISTANCE IS ENERGY DISSIPATION (TURNING “GOOD” ENERGY TO HEAT IRREVERSIBLY - LIKE FRICTION)
  • CAPACITANCE IS ENERGY WHICH IS STORED WITHOUT DISSIPATION
  • INDUCTANCE IS ANOTHER FORM OF STORAGE
reaction kinetics and thermodynamic networks
REACTION KINETICS AND THERMODYNAMIC NETWORKS
  • START WITH KINETIC DESRIPTION OF DYNAMICS
  • ENCODE AS A NETWORK
  • TWO POSSIBLE KINDS OF ENCODINGS AND THE REFERENCE STATE
example atp synthesis in mitochondria
EXAMPLE: ATP SYNTHESIS IN MITOCHONDRIA

EH+ <--------> [EH+]

H+

[H+]

E <-------------> [E]

S

P

E

MEMBRANE

the same kinetic system has at least two network representations both valid
THE SAME KINETIC SYSTEM HAS AT LEAST TWO NETWORK REPRESENTATIONS, BOTH VALID
  • ONE CAPTURES THE UNCONSTRAINED BEHAVIOR OF THE SYSTEM AND IS GENERALLY NON-LINEAR
  • THE OTHER IS ONLY VALID WHEN THE SYSTEM IS CONSTRAINED (IN A REFERENCE STATE) AND IS THE USUAL THERMODYNAMIC DESRIPTION OF A COUPLED SYSTEM
some published network models of physiological systems
SR (BRIGGS,FEHER)

GLOMERULUS (OKEN)

ADIPOCYTE GLUCOSE TRANSPORT AND METABOLISM (MAY)

FROG SKIN MODEL (HUF)

TOAD BLADDER (MINZ)

KIDNEY (FIDELMAN,WATTLINGTON)

FOLATE METABOLISM (GOLDMAN, WHITE)

ATP SYNTHETASE (CAPLAN, PIETROBON, AZZONE)

SOME PUBLISHED NETWORK MODELS OF PHYSIOLOGICAL SYSTEMS
conclusions
CONCLUSIONS
  • THE REAL WORLD IS COMPLEX
  • THE WORLD OF “SIMPLE MECHANISMS” IS A SURROGATE WORLD CREATED BY TRADITIONAL SCIENCE
  • WE ARE AT A CROSSROADS: A NEW WORLDVIEW IS NEEDED
  • THERE WILL ALWAYS BE RISK ASSOCIATED WITH ATTEMPTS TO PROGRESS
  • YOUR CRYSTAL BALL MAY BE AS GOOD AS MINE OR BETTER
post script
POST SCRIPT
  • WE LIVE IN A WORLD DOMINATED BY COMPUTERS
  • MOST COMPLEXIFIERS BELIEVE THAT COMPLEXITY IS SOMETHING WE CAN DEAL WITH ON THE COMPUTER
  • THIS NOTION OF COMPLEXITY FOCUSES ON THE MECHANICAL ASPECTS OF THE REAL WORLD
  • WHAT MAKES THE REAL WORLD COMPLEX IS ITS NON-COMPUTABILITY