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Art of Doing Science andEngineering

Gordana Dodig-Crnkovic

Department of Computer Science and ElectronicMlardalen University


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Vetenskapsteori och metodik KIN171Litteraturlista

Marshall, C. & Rossman, G. (2006). DesigningQualitative Research. ISBN 9781412924894.

Hansson, S.O. (2003). Konsten att varavetenskaplig. Filosofi/KTH.

http://www.infra.kth.se/~soh/downloads.htm

Sherlock Holmes. I Doyle, A. Sherlock Holmesventyr: En studie i rtt. ISBN 91-85267-22-8.Se ven www.wikipedia.com

Semmelweiss - du sker sjlv information viainternet, bibliotek, artiklar osv. 9


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Sherlock Holmes

Sherlock Holmes, huvudpersonen i en serievrldsbekanta detektivhistorierav sirArthur Conan Doyle, och prototypen fr enskarpsinnig och inga mdor eller farorskyende yrkesdetektiv.

Sherlock Holmes gjorde entr i vrlden 1887 isamband med romanen En studie i rtt,

och vann stor ryktbarhet

ngra r senarenr de frsta Holmesnovellerna brjadepubliceras i tidskriften The StrandMagazine.

Holmes karakteriseras av sin imponerandeiakttagelse- och slutledningsfrmga vilkenhan d och d prvar p sinlevnadstecknare, och fljeslagare Dr.Watson.


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Sherlock Holmes

En studie i rtt (A Study in Scarlet) r denfrsta boken om detektiven SherlockHolmes och r skriven av Arthur ConanDoyle 1887. Det r i denna bok doktor

Watson och Sherlock Holmes lr knnavarandra och Sherlock Holmes-figurenintroduceras fr vrlden. Genast startaren spnnande mordgta som ger prov pSherlock Holmes skarpsinne.

http://sv.wikipedia.org/wiki/Sherlock_Holmes


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Innehll

Frord

1 Vilken kunskap vill vi ha?1.1 Vetande och handlingskunskap1.2 Vetenskapsbegreppet1.3 Ren och tillmpad vetenskap

1.4 Generell kontra speciell kunskap1.5 Handlingskunskapen1.6 Intersubjektivitet och objektivitet1.7 Faran med auktoritetstro1.8 Att utg frn den bsta tillgngliga

kunskapen1.9 Vetenskapen r en mnsklig aktivitet1.10 Det stora och det lilla tvivlet1.11 Sinnen och frnuft1.12 Empirism och rationalism1.13 Hantverkarnas bidrag1.14 Episteme och techne nrmar sig

varandra igen

Konsten att vara vetenskapligSven Ove Hansson, KTH


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2 Att resonera frnuftigt

2.1 Det rationella samtalet2.2 Fora fr vetenskapliga samtal2.3 Stegvis framlagda argument2.4 Mngtydighet och vaghet2.5 Nr behver ord vara vldefinierade?2.6 Definitioner2.7 Tre vgar till mer precisa begrepp

2.8 Vrdeladdade ord2.9 Kreativitet och kritik2.10 Intuition

3 Att observera3.1 Sinnenas ofullkomlighet3.2 Observationer r teoriberoende

3.3 Tekniken hjlper sinnena och minnet3.4 Utvalda observationer3.5 Fyra slags observationer3.6 Nr observationsidealet inte kan uppns3.7 Observatren sjlv3.8 Att vara beredd p det ovntade3.9 Kllkritik att dra slutsatser frn andras observationer

3.10 Mtningar


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4 Att gra experiment4.1 Experiment finns av mnga slag4.2 Att konstruera ett experiment4.3 Att separera4.4 Att kontrollera variablerna4.5 Experiment ska g att upprepa

4.6 Upprepning i praktiken

5 Att pvisa samband5.1 Att prva hypoteser5.2 Verifiering eller falsifiering?5.3 Falsifieringens problem

5.4 Den ndvndiga sammanvgningen5.5 Kravet om enkelhet5.6 Slumpens skrdar5.7 Statistisk hypotesprvning5.8 All forskning r inte hypotesprvande



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6 Att anvnda modeller

6.1 Tre slags modeller6.2 Idealisering6.3 Om faran med modeller6.4 Simulering

7 Att frklara7.1 Vetenskap utan frklaringar?

7.2 Frklaringar och frstelse7.3 Frklaringsstt som har vergetts7.4 Reduktioner

8 Att finna orsaker8.1 Orsak som undantagsls

upprepning8.2 Orsaksbegreppet r antropomorft8.3 Allt har inte en orsak8.4 Att faststlla orsakssamband8.5 Samverkan mellan flera

orsaksfaktorer


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9 Vetenskap, vrderingar ochvrldsbilder

9.1 Vetenskapens beslutsfattande9.2 Att skilja mellan fakta och vrderingar9.3 Vetenskap och vrldsbild


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Gordana Dodig-Crnkovic, Courses(Follow Open Source Philosophy)

http://www.idt.mdh.se/personal/gdc/work/courses.html

CDT314 - Formal Languages, Automata and Theory of Computation

CDT403 - Research Methodology for Natural Science and Technology

CDT212 - Vetenskapsmetodik (Scientific Method, in Swedish)

CDT409 - Professional Ethics in Science and Engineering

Research Ethics and Professionalism (Interdepartmental PhD course)

Interdisciplinary Research and Co-Production of Knowledge(NEW! Interdepartmental PhD course)

CD5650 - Philosophy of Computer Science, Swedish National Course(2004)


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The Art of Doing Science and Engineering: Learning to Learn

by Richard W. Hamming

Highly effective thinking is an art that engineers

and scientists can be taught to develop. Bypresenting actual experiences and analyzingthem as they are described, the authorconveys the developmental thought processesemployed and shows a style of thinking thatleads to successful results is something thatcan be learned. Along with spectacularsuccesses, the author also conveys how

failures contributed to shaping the thoughtprocesses.Provides the reader with a style of thinking that will

enhance a person's ability to function as aproblem-solver of complex technicalissues.

Consists of a collection of stories about theauthor's participation in significant discoveries,relating how those discoveries came aboutand, most importantly, provides analysis aboutthe thought processes and reasoning that tookplace as the author and his associatesprogressed through engineering problems.

http://www.cs.virginia.edu/~robins/YouAndYourResearch.html Hammings talk on research


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Random Notes from R. W. Hamming,Art of Doing Science and Engineering: Learning to Learn

Knowledge also comes from years of study of the work of others.

The belief anything can be "talked about" in words was certainly held bythe early Greek philosophers, Socrates (469-399), Plato (427-347), andAristotle (384-322).

This attitude ignored the contemporary mystery cults which asserted you

have to "experience" some things which could not be communicated inwords. Examples might be beauty, gods, arts, and love.


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Traditional scientific training has emphasized the role of words, along with a strong

belief in reductionism, hence to emphasize the possible limitations of languagewe can take up several places in this book.

Style" is such a topic. ..This talking about first person experiences will give a flavorof "bragging," ... Learning from the experiences of others saves making errorsyourself, but the study of successes is basically more important than the studyof failures. ... there are so many ways of being wrong and so few of being right,studying successes is more efficient, and furthermore when your turn comes youwill know how to succeed rather than how to fail! You must think carefully aboutwhat you hear or read

...reductionism...


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A simulation is the answer to the question: "what if ...?" 1. cheaper, 2.faster, 3. often better 4. can do what you cannot do in the lab.

Why should anyone believe the simulation is relevant?

You are responsible for your decisions, and cannot blame them on thosewho do the simulations, as much as you wish you could. Reliability is acentral question with no easy answers.

All impossibility proofs must rest on a number of assumptions which may

or may not apply in the particular situation.

"If an expert says something can be done he is probably correct, butif he says it is impossible then consider getting another opinion."


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Skepticism

Det r nstan som under renssansen: mnniskor kan utropaAd fontes! Till kllorna, allts. Man behver inte nja sigmed en second opinion; man kan frska skaffa sig tusen,och man kan bli odrgligt plst som patient.

Bodil Jnsson, Tnk om det r precis tvrtom!?


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Science, Knowledge, Truth, Meaning

WHAT IS SCIENCE?What Sciences are there?What Liberal Arts are there?

WHAT IS SCIENTIFIC METHOD?

Critique of Usual Nave Image of Scientific Method

WHAT IS KNOWLEDGE?

SCIENCE, TRUTH AND MEANING


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Theory of Science Lectures

Lecture 1 SCIENCE, KNOWLEDGE, TRUTH, MEANING. FORMAL

LOGICAL SYSTEMS LIMITATIONS LANGUAGE ANDCOMMUNICATION

Lecture 2 SCIENCE, RESEARCH, TECHNOLOGY, SOCIETALASPECTS OF SCIENCE AND TECHNOLOGY. PROGRESS INSCIENCE A BRIEF RETROSPECTIVE OF SCIENTIFIC THEORY

Lecture 3 LANGUAGE AND COMMUNICATION, CRITICALTHINKING AND PSEUDOSCIENCE - DEMARCATION

Lecture 4 GOLEM LECTURE. ANALYSIS OF SCIENTIFICCONFIRMATION: THEORY OF RELATIVITY, COLD FUSION,GRAVITATIONAL WAVES

Lecture 5 COMPUTING HISTORY OF IDEAS

Lecture 6 PROFESSIONAL & RESEARCH ETHICS


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Red Thread: Critical Thinking

A red thread in this course: critical thinking.

We use critical thinking as method when approaching science.

We think (critically!) about critical thinking.


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Red Thread: Critical Thinking

Reserve your right to think,for even to think wrongly

is better than not to think at all.

Hypatia, natural philosopher and mathematician


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Concentric Rinds(Concentric Space Filling/Regular Sphere Division). Maurits Cornelis Escher

What Is Science?


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SCIENTISTS

"Scientists are people of very dissimilar temperaments doingdifferent things in very different ways.

Among scientists are collectors, classifiers and compulsive

tidiers-up; many are detectives by temperament and many are

explorers; some are artists and others artisans.There are poet-scientists and philosopher-scientists and even a

few mystics."

Peter Medawar, Pluto's Republic


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Science: Definitions by Goal and Process (1)

Science (Lat. scientia, from scire, to know ) is wonder aboutnature. Like philosophy, science poses questions but also has the specific means to answer them, as long as theyconcern the state and behavior of the physical world.


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Science: Definitions by Goal and Process (2)

Science is the systematic study of the properties of the physicalworld, by means of repeatable experiments and measurements,and the development of universal theories that are capable ofdescribing and predicting observations. Statements in science

must be precise, such that other people can test them (in orderto establish universality).


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Science: Definitions by Contrast

To do science is to search for repeated patterns, not simply toaccumulate facts.

Robert H. MacArthur

Religion is a culture of faith; science is a culture of doubt.Richard Feynman


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Dewey Decimal Classificationhttp://www.geocities.com/Athens/Troy/8866/15urls.html

000 - Computers, Information & General Reference100 - Philosophy & Psychology200 - Religion300 - Social sciences

400 - Language500 - Science600 - Technology700 - Arts & Recreation800 - Literature

900 - History & Geography


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Scientific Comunities as Family Trees

Josh Dever at the University of Texas is compiling a "family tree" ofphilosophers related by the Ph.D. supervisor relation (orequivalent).

The tree is online at

https://webspace.utexas.edu/deverj/personal/philtree/philtree.html


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The narrow inductivist conception of scientific inquiry

1. All facts are observed and recorded.

2. All observed facts are analyzed, compared and classified,without hypotheses or postulates other than those necessarilyinvolved in the logic of thought.

3. Generalizations inductively drawn as to the relations,classificatory or causal, between the facts.

4. Further research employs inferences from previouslyestablished generalizations.

Critique of Usual Nave Imageof Scientific Method (1)


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This narrow idea of scientific inquiry is groundless for severalreasons:

1. A scientific investigation could never get off the ground, for acollection of all facts would take infinite time, as there are infinite

number of facts.The only possible way to do data collection is to take onlyrelevant facts. But in order to decide what is relevant and what isnot, we have to have a theory or at least a hypothesis about whatis it we are observing.


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A hypothesis (theory) is needed to give the direction to ascientific investigation!

2. A set of empirical facts can be analyzed and classified inmany different ways. Without hypothesis, analysis andclassification are blind.

3. Induction is sometimes imagined as a method that leads, bymechanical application of rules, from observed facts to

general principles. Unfortunately, such rules do not exist!


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Why is it not possible to derive hypothesis (theory)directly from the data? (1)

For example, theories about atoms contain terms like atom,electron, proton, etc; yet what one actually measures are spectra(wave lengths), traces in bubble chambers, calorimetric data, etc.

So the theory is formulated on a completely different (and more

abstract) level than the observable data!

The transition from data to theory requests creative imagination!


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Why is it not possible to derive hypothesis (theory)directly from the data? (2)

Scientific hypothesis is formulated based on educated guesses at the

connections between the phenomena under study, at regularities andpatterns that might underlie their occurrence. Scientific guesses arecompletely different from any process of systematic inference.

The discovery of important mathematical theorems, like the discoveryof important theories in empirical science, requires inventive ingenuity.


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1. Wonder. Pose a question.(Formulate a problem).

1. Wonder. Pose a question(of the What is X ? form).

5. Act accordingly.5. Act accordingly.

4. Accept the hypothesis as provisionally true. Returnto step 3 if there are predictable consequences of thetheory which have not been experimentallyconfirmed.

4. Accept the hypothesis as provisionally true.Return to step 3 if you can conceive any othercase which may show the answer to bedefective.

3. Testing. Construct and performan experiment, which makes it possible to observewhether the consequences specified in one or more

of those hypothetical propositions actually followwhen the conditions specified in the sameproposition(s) pertain. If the test fails, return to step 2,otherwise go to step 4.

3. Elenchus ; testing, refutation, or cross-examination. Perform a thought experiment byimagining a case which conforms to the definiens

but clearly fails to exemplify the definiendum, orvice versa. Such cases, if successful, are calledcounterexamples. If a counterexample isgenerated, return to step 2, otherwise go to step4.

2. Hypothesis. Suggest a plausible answer (a theory)

from which some empirically testable hypotheticalpropositions can be deduced.

2. Hypothesis. Suggest a plausible answer (a

definition or definiens) from which someconceptually testable hypothetical propositionscan be deduced.

Scientific MethodSocratic Method


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The Scientific Method A Complex Adaptive System

The hypotetico-deductive cycle

EXISTING THEORIES

AND OBSERVATIONS

1

SELECTION AMONG

COMPETING THEORIES

6

EXISTING THEORY CONFIRMED

(within a new context) or

NEW THEORY PUBLISHED

5

Hypotesen

mste

justeras

PREDICTIONS

3

HYPOTHESIS

2

TESTS AND NEW

OBSERVATIONS

4

Hypothesis must

be redefined

Hypothesis must

be adjusted

The scientific-community cycle

Consistency achieved


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Formulating Research Questions and Hypotheses

Different approaches:

Intuition (Educated) Guess

AnalogySymmetry

Paradigm

Metaphor

.... and many more...


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Criteria to Evaluate Theories

When there are several rivaling hypotheses number of criteria canbe used for choosing a best theory.

Following can be evaluated:

Theoretical scope

Heuristic value (heuristic: rule-of-thumb or argumentderived from experience)

Parsimony (simplicity, Ockhams razor)

Esthetics

Etc.


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Criteria which Good Scientific Theory Shall Fulfill

Logically consistent

Consistent with accepted facts Testable

Consistent with related theories

Interpretable: explain and predict

Parsimonious Pleasing to the mind (Esthetic, Beautiful)

Useful (Relevant/Applicable)


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Ockhams Razor (Occams Razor)(Law Of Economy, Or Law Of Parsimony, Less Is More!)

A philosophical statement developed by William of Ockham,(12851347/49), a scholastic, that Pluralitas non est ponendasine necessitate; Plurality should not be assumed withoutnecessity.

The principle gives precedence to simplicity; of two competingtheories, the simplest explanation of an entity is to be preferred.


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What Is Knowledge?Platos Definition

Knowledge is justified, true belief.

The problem with this concerns the word justified. Allinterpretations of justified are deemed inadequate.

These analyses are an excellent example of the critique of theoriesof knowledge, but do not provide an answer to what knowledgeis.


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What Is Knowledge?Platos Definition Gettier Problem

Edmund Gettier, in the paper called "Is Justified True Belief

Knowledge? argues that knowledge is not the same as justifiedtrue belief.


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Knowledge and ObjectivityObservations

All observation is potentially contaminated, whether by our

theories, our worldview or our past experiences.

It does not mean that science cannot objectively [inter-subjectivity] choose from among rival theories on the basis ofempirical testing.

Although science cannot provide one with hundred percentcertainty, yet it is the most, if not the only, objective mode ofpursuing knowledge.

P ti d Di t Ob ti


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Perception and Direct Observation



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"Reality is merely anillusion, albeit a very

persistent one." -Einstein


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Checker-shadow illusion

http://web.mit.edu/persci/people/adelson/checkershadow_illusion.html

See even:

http://persci.mit.edu/people/adelson/publications/gazzan.dir/gazzan.htm

Lightness Perception and Lightness Illusions

http://www.ihu.his.se/~christin/Vetenskapsteori/Vetenskapsteorikurser

T h d R li


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Truth and Reality

Noumenon("Ding an sich")

is distinguishedfromphenomenon("Erscheinung"),an observableevent or physicalmanifestation,and the twowords serve asinterrelatedtechnical terms in

Kant'sphilosophy.


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Whole vs. Parts

tusk spear

tail rope trunk snake

side wall

leg tree

The flaw in all their reasoning is that speculating on the WHOLEfrom too few FACTS can lead to VERY LARGE errors injudgment.


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Science and Truth

Science as Consensus

Science as Controversy


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Scientific Truth (1)

Physics professor is walking across campus, runs into Math

professor. Physics professor has been doing an experiment,and has worked out an empirical equation that seems to explainhis data, and asks the Math professor to look at it.


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Scientific Truth (2)

A week later, they meet again, and the Math professor says the

equation is invalid. By then, the Physics professor has used hisequation to predict the results of further experiments, and he isgetting excellent results, so he asks the Math professor to lookagain.

Another week goes by, and they meet once more. The Mathprofessor tells the Physics professor the equation does work,but only in the trivial case where the numbers are real and

positive."


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TRUTH VS. PROVABILITY

ACCORDING TO GDEL

After: Gdel, Escher, Bach - an Eternal Golden Braidby Douglas Hofstadter.


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NON-ARISTOTELIAN LOGIC

The term non-Aristotelian logic, sometimes shortened to null-A, means any

non-classical system of logic which rejects some of Aristotle'spremises.

Related topics:Intuitionistic logicFuzzy logic

General SemanticsMeta-systemsMulti-valued logicParaconsistent logicQuantum logicIs logic empirical?Theory of mind


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The Limits of Reason - G J Chaitin

The limits of reason

Scientific American 294, No. 3 (March 2006), pp. 74-81.Epistemology as information theory: from Leibniz to

Collapse 1 (2006), pp. 27-51.Reprinted in Teoria algoritmica della complessit, 2006.

Meta Math!

first paperback editionVintage, 2006.Speculations on biology, information and complexity

Bulletin of the European Association for Theoretical Computer Science 91(February 2007), pp. 231-237.


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Cybernetics as a Language for Interdisciplinary Communication

Stuart A. UmplebyThe George Washington University

Washington, DCwww.gwu.edu/~umpleby


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How is interdisciplinary communication possible?

We would need to share a common language

Perhaps there is a common deep structure which is hidden by ourmore specialized discipline-oriented terms and theories

Stuart A. Umpleby


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What is the origin of the deep structure?

There are at least three possibilities:

1. Common processes and structures in the external world

2. Common human cognitive structures and processes(Mental models)

3. Logic (Mathematics)

After Stuart A. Umpleby


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2. Common processes in the external world

General systems theory, particularly James G. Millers living

systems theory, claims that there are certain functions that aliving system must perform

Miller suggested that living systems exist at seven levels

cell,organ,organism,group,organization,nation,supranational organization

Stuart A. Umpleby


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1. Mathematical Isomorphisms

Anatol Rapoport suggested that the aim of general systems

theory is to identify mathematical isomorphisms

The word 'isomorphism' applies when two complex structures can be mapped ontoeach other, in such a way that to each part of one structure there is acorresponding part in the other structure, where 'corresponding' means that thetwo parts play similar roles in their respective structures." (Douglas Hofstadter,Gdel, Escher, Bach, p. 49)

Not many isomorphisms have been discussed in the literature

Their theoretical importance is not clear



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Nineteen critical subsystems in living systems

Matter-energy processing subsystems ingestor, distributor, converter,

producer,matter-energy storage, extruder, motor, supporter

Information processing subsystems input transducer, internaltransducer, channel and net, decoder, associator, memory, decider,encoder, output transducer

Subsystems that process both reproducer, boundary

Stuart A. Umpleby


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3. Conceptual models

In cybernetics there are basically three conceptualizations

Regulation Self-organization

Reflexivity

Stuart A. Umpleby


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How can these models be used?

To find common ground with a person in a different field, listen

to identify which of these models is being used When you have identified which model is being used,

cybernetics provides a set of theories and methods to beemployed

Often more than one, indeed all three, models can be used

Stuart A. Umpleby


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1. Regulation

Two analytic elements regulator and system being regulated

Engineering examples thermostat and heater, automatic pilotand airplane Biological examples feeling of hunger and food in stomach,

light in eye and iris opening Social system examples manager and organization, therapist

and patient

Stuart A. Umpleby


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Methods to use in regulation

Is there requisite (necessary) variety?

What is the variety in the system to be controlled?What variety is available to match it?

Choose the level of analysis in order to achieve requisite variety

Define a model of cause and effect list actions and theirexpected consequences

Stuart A. Umpleby


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Coping with complexity

When faced with a complex situation, there are only two choices

1. Increase the variety in the regulator: hire staff or subcontract

2. Reduce the variety in the system being regulated: reduce the

variety one chooses to control

Stuart A. Umpleby


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The management of complexity

There has been a lot of discussion of complexity, as if it exists

in the world

Cyberneticians prefer to speak about the management ofcomplexity

Their view is that complexity is observer dependent, that thesystem to be regulated is defined by the observer

This point of view greatly expands the range of alternatives

Stuart A. Umpleby

Self-organization


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Digital Video Feedback and Morphogenesis

Video Feedback systems tend towardeither stability or chaos. While thestable attractor offers some interest inthe subtleties of its decay, theunstable attractor offers an unlimitedsupply of endless evolving motifs anda window on emergent behaviour.

The system can be get into chaoticemergence via camera movement(rotation and positioning). Theimportant thing was to catch themovement of catching a shape in a

particular temporal phase to feed backinto the system advancing thecomplexity and initiating lifelikemorphogenesis.

http://www.transphormetic.com/Talysis01.htm

Microtubules viewed as molecular ant colonies -


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reactive adaptive self-organizing systemsPopulations of ants and other social insects self-

organize and develop emergent propertiesthrough stigmergy in which individual antscommunicate with one another via chemicaltrails of pheromones that attract or repulse other

ants. In this way, sophisticated properties andfunctions develop.Under appropriate conditions, in vitro microtubule

preparations, initially comprised of only tubulinand GTP, behave in a similar manner. They self-organize and develop other higher-levelemergent phenomena by a process whereindividual microtubules are coupled together by

the chemical trails they produce by their ownreactive growing and shrinking.Viewing microtubules as populations of molecular

ants may provide new insights as to how thecytoskeleton may spontaneously develop high-level functions. It is plausible that suchprocesses occur during the early stages ofembryogenesis and in cells.

Microtubules are long tubular-shaped supramolecularassemblies with inner and outer diameters of approx. 16nm and 24 nm respectively. are a major filamentarycomponent of the cytoskeleton. They have two majorroles; they organize the cell interior, and they permit andcontrol the directional movement of intracellular particlesand organelles from one part of the cell to another.

Proposed mechanism for the formation of the self-organized structure

http://www.biolcell.org/boc/098/0603/boc0980603.htm

Microtubules viewed as molecular ant colonies -reactive adaptive self-organizing systems


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reactive adaptive self-organizing systems

Self-organization by reactive processesNormally solutions of reacting chemicals in a test-

tube do not self-organize. (Kolmogorov et al.,1937; Rashevsky, 1940; Turing, 1952; Prigogineand Nicolis, 1971; Nicolis and Prigogine, 1977)

have proposed that some types of chemicalreaction might show strongly non-linear reactiondynamics due to being sufficiently far-from-equilibrium. They predicted that in somecases this could result in macroscopic self-organization.

Some chemical systems based on reactions originallydiscovered in the 1920s (Bray, 1921) and 1950s

(Belousov, 1951, 1958) have been shown toself-organize this way (Castets et al., 1990;Ouyang and Swinney, 1991).

Viewing microtubules as populations of molecularants may provide new insights as to how thecytoskeleton may spontaneously develop high-level functions. It is plausible that suchprocesses occur during the early stages of

embryogenesis and in cells.

http://www.biolcell.org/boc/098/0603/boc0980603.htm

Replication of form


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Physical biology of molecular motors involved in intracellular


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Physical biology of molecular motors involved in intracellularorganisation

Network of microtubules and two kinds of motorproteins created by self-organisation in vitro

Motor proteins are key determinants for the spatialorganisation of eukaryotic cells. They arethermodynamic non-equilibrium machinesplaying a crucial role for the dynamic natureof cellular order. In fact, they provide aparadigm for the concept of intracellular orderdepending on molecular dynamics. How

exactly the collective behaviour of variousmotors with different kinetic properties drivesthe organisation of the cytoskeleton is notunderstood.

http://www-db.embl.de/jss/EmblGroupsOrg/g_175.html


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Origami Programmable Cell Sheet

The objective is to produce a language for describing global shape that canbe compiled to local interactions amongst a large number of cells thatwork robustly inspite of imprecise positioning and individual celllimitations and failures. The long term goal is to contribute to theunderstanding of engineered self-organisation, i.e. rather thanobserving emergent global behavior from given local rules, how doesone derive local rules for a particular global goal? What are the highlevel languages for describing global goals, and what are the primitivesfor constructing local rules?

Origami is an example of a language that constructively describes globalstructures.Using a small set of axioms (called Huzita's axioms) and only two types of

folds (mountain and valley), one can construct a very wide variety ofcomplex shapes.The initial conditions are very simple and always thesame.

The methods of combination are very simple. Axioms generate new creasesfrom existing points and creases and new points can be formed only by

the intersection of previous folds.Origami is a scale-independent language - i.e. the sequence of folds for aparticular shape is independent of the size of the sheet.

http://www-swiss.ai.mit.edu/projects/amorphous/Progmat/thesis/origami.html


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Origami Programmable Cell Sheet

Huzita's Origami Axioms: Given two points p1 and p2, we can fold a line

through them Given two points p1 and p2, we can fold p1 onto

p2 (make a crease that bisects the line p1p2 atright angles)

Given two lines L1 and L2 we can fold L1 onto

L2 (crease is a bisector of the angle between L1and L2) Given p1 and L1 we can make a fold through p1

perpendicular to L1 Given p1 and p2 and line l1, we can make a fold

that places p1 on l1 and passes through p2

Given p1 and p2 and lines l1 and l2, we canmake a fold that places p1 on l1 and p2 on l2.

http://www-swiss.ai.mit.edu/projects/amorphous/Progmat/thesis/origami.html


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Self-Organizing Systems Resources

Archive of papers - adaptation/self-organizing systemsEVALife - Self-organisation in life-cyclesExtropia - open source softwareFractal Structures and Self-Organization - TMR NetworkOne-Over-F Noise - bibliographyScalable Self-Organizing Simulations - DARPASelf-Organising Adaptive Systems - BT Labs

Self-Organising Nanopatterns - Sandia National LaboratorySelf-organization and fractals - GeodynamicsShalizi's Notebook - self-organizationSOS on The Web - smallSymbiotic Intelligence Project - self-organizing knowledgeStigmergic Systems - Peter SmallThe Self-Organization of the European Information Society - EU

TSER ProjectWEBSOM Self-Organizing Maps - web intelligence

Introduction to Complex Systems


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by David Kirshbaum

A Complex System is any system which involves a number of elements, arranged instructure(s) which can exist on many scales. These go through processes of

change that are not describable by a single rule nor are reducible to only onelevel of explanation, these levels often include features whose emergencecannot be predicted from their current specifications. Complex Systems Theoryalso includes the study of the interactions of the many parts of the system.

Previously, when studying a subject, researchers tended to use a reductionistapproach which attempted to summarize the dynamics, processes, and changethat occurred in terms of lowest common denominators and the simplest, yetmost widely provable and applicable elegant explanations.

But since the advent of powerful computers which can handle huge amounts ofdata, researchers can now study the complexity of factors involved in a subject

and see what insights that complexity yields without simplification or reduction.

http://www.calresco.org/intro.htm


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Structure and dynamics of animal social networks

Interactions between agents (whatever they may be) can berepresented by a network. In animal social systems the nodesrepresent individual animals and the lines between them socialties.

There is a growing interest, among mathematicians, statisticalphysicists, sociologists and others in understanding andcharacterizing the structure of such networks, and the dynamicsof processes (such as the transmission of disease or other"information") on networks.

Algorithms are developed to search a complex animal social networkfor "communities", or sets of nodes that are better connectedamong themselves than they are to the rest of the network, andto try to understand what causes the population to contain thesestructures.

Most of the animal social networks constructed so far are built via an

accumulation of many surveys of the population. An alternativeapproach is to monitor interactions in real time, to try tounderstand not only how information might be transmittedthrough a network, but also how the nature of the informationmight be having an effect on the structure of the network.

Some of the systems of interest, include tropical fish, Galapagos sealions, ants and deer.

http://people.bath.ac.uk/pysrj/

Examples of self-organization


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Large-scale lattice Boltzmann simulations of complex fluids:advances through the advent of computational grids

Institute for Computational Physics. Physics on HighPerformance Computers

http://www.ica1.uni-stuttgart.de/publications/2005/HCVC05/

Supramolecular chemistry and self-assembling


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molecules

Supramolecular chemists are nowextending their research beyond the

design of molecules that can be usedfor molecular recognition or catalysis.They are actively exploring systems that

undergo self-organisation - systemsthat can spontaneously generate well-defined functional supramolecular

architectures by self-assembly fromtheir components.

This spontaneous but controlled formationof nanoscale architectures could beused to engineer and processfunctional nanostructures, offering a

powerful alternative tonanofabrication, going fromconstruction to self-construction.

Molecular fragments self-assemble to form a dynamic library of

potentially bioactive compounds

"Self-organisation by selection takes advantage of dynamicdiversity to allow variation in response to internal or

external factors in a Darwinian fashion."

"Constitutional dynamic chemistry paves the way towards anadaptive and evolutive chemistry, a further step towards

unravelling the science of complex matter." http://www.rsc.org/Publishing/ChemScience/Volume/2007/02/A_natural_selection.asp


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Self-referenceReflexivity


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Douglas Hofstadters Writings

Self-reference is ubiquitous. It happens every time any onesays I or me or word or speak or mouth. It

happens every time a newspaper prints a story aboutreporters, every time someone writes a book aboutwriting, designs a book about book design, makes amovie about movies, or writes an article about self-reference. Many systems have the capability torepresent or refer to themselves somehow, todesignate themselves (or elements of themselves)

within the system of their own symbolism. Wheneverthis happens, it is an instance of self-reference.

SL #642: My proposal [...] is to see the I as a hallucination perceived bya hallucination, which sounds pretty strange, or perhaps even stranger: theI as a hallucination hallucinated by a hallucination.SL #641: That sounds way beyond strange. That sounds crazy.SL #642: Perhaps, but like many strange fruits of modern science, it cansound crazy yet be right. At one time it sounded crazy to say that the earthmoved and the sun was still....(I Am a Strange Loop, p. 293 )

Self-reference(R fl i i )


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(Reflexivity)

This model has traditionally

been avoided and is logicallydifficult

Inherent in social systemswhere observers are alsoparticipants, in individual living

organisms

Every statement reveals anobserver as much as what isobserved


E l f fl i it i l ith


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Examples of reflexivity recursive algorithms

This weedlike plant is based on asimple recursive algorithm.

Recursion is a popular techniqueused to describe trees and thelike, because of the self-referential nature of a tree.

Basically, you would describe a treeby stating that a branch issomething from which smallerbranches sprout, and that the rootof a tree is a big branch.

Self-reference can lead toundecidability (and paradoxes like

set of all sets that are notmembers of themselves)

Observation


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Self-awareness

Stuart A. Umpleby

Reflexivity in a social system


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93Stuart A. Umpleby


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A reflexive theory operates at two levels

Ideas

Variables Groups

Events

Stuart A. Umpleby


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Equilibrium Theory Reflexivity Theory

- +Stock Stock + Demandprice - Demand price+ +

Equilibrium theory assumes negative feedback; reflexivity theory observes positivefeedback

Stuart A. Umpleby

Equilibrium vs Reflexivity


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Equilibrium vs. Reflexivity

A theorist is outside the systemobserved

Scientists should build theoriesusing quantifiable variables

Theories do not alter the systemdescribed

Observers are part of thesystem observed

Scientists should use a varietyof descriptions of systems (e.g.,ideas, groups, events,variables)

Theories are a means to

change the system described

Stuart A. Umpleby

Adaptation/Reactivity/Regulation,

Self-organizationS lf f /R fl i it /R i


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Self-organization,Self-reference/Reflexivity/Recursiveness

Models of regulation, self-organization, and reflexivity can be

used in two ways Either to develop descriptions of some system (developinterdisciplinary models)

Or to guide efforts to influence some system

Stuart A. Umpleby

Overview of cybernetics


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Overview of cybernetics

The focus of attention within cybernetics has changed from engineeringto the biology of cognition to social systems

Ideas from cybernetics have been used in computer science, robotics,management, family therapy, philosophy of science, economics andpolitical science

Cybernetics has created theories of the nature of information,knowledge, adaptation, learning, self-organization, cognition,autonomy, and understanding

Stuart A. Umpleby


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Definitions of First and Second Order Cybernetics

The cybernetics of observing systems

The purpose of a modelerAutonomous systemsInteraction between observer and observedTheories of the interaction between ideas

and society

The cybernetics of observed

systemsThe purpose of a modelControlled systemsInteraction among the variables in

a systemTheories of social systems

Von Foerster

PaskVarelaUmpleby

Umpleby

Second Order CyberneticsFirst Order CyberneticsAuthor

Stuart A. Umpleby

A pragmatic view ofepistemology:

knowledge isconstructed to achieve

A biological view ofepistemology: how the

brain functions

A realist viewof epistemology:

knowledge is apicture of reality

The view ofepistemology

Social CyberneticsBiological CyberneticsEngineering Cybernetics


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100Three Versions of Cybernetics

By transforming conceptualsystems (throughpersuasion, notcoercion), we canchange society

If people accept constructivism,they will be more tolerant

Scientific knowledge canbe used to modifynatural processes tobenefit people

An importantconsequence

Ideas are accepted if theyserve the observerspurposes as a socialparticipant

Ideas about knowledge shouldbe rooted inneurophysiology.

Natural processes can beexplained byscientific theories

A key assumption

How people create,maintain, and changesocial systems throughlanguage and ideas

How an individual constructs areality

How the world worksWhat must beexplained

Explain the relationshipbetween the naturaland the social sciences

Include the observer within thedomain of science

Construct theories whichexplain observedphenomena

The puzzle to besolved

The biology of cognition vs.the observer as asocial participant

Realism vs. ConstructivismReality vs. scientifictheories

A key distinction

human purposes

Stuart A. Umpleby

The cybernetics of science


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The cybernetics of science

NORMAL SCIENCE

The correspondence Incommensurable

principle definitions

SCIENTIFIC REVOLUTION

Stuart A. Umpleby

The Correspondence Principle


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The Correspondence Principle

Proposed by Niels Bohr when developing the quantum theory

Any new theory should reduce to the old theory to which itcorresponds for those cases in which the old theory is known tohold

A new dimension is required

Stuart A. Umpleby

New philosophy of science


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p p y

An Application of the Correspondence Principle

Old philosophy of science

Amount of attention paid to

the observer

Stuart A. Umpleby

KLASSISKA VETENSKAPER I RELATION TILL ANDRAKUNSKAPSOMRDEN


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Kultur (religion,konst..)

Natural Sciences(Physics,Chemistry,Biology, )

Social Sciences(Economics,Sociology,

Anthropology, )

The Humanities(Philosophy, History,Linguistics )

Logic&

Mathematics

CROSS DISCIPLINARY RESEARCH FIELDS


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Our scheme represents the classical groups of sciences.

Modern sciences are stretching through several fields of our scheme.

Computer science e.g. includes the field of AI that has its roots inmathematical logic and mathematics but uses physics, chemistry andbiology and even has parts where medicine and psychology are very

important.

Examples: Environmental studies, Cognitive sciences, Cultural studies,

Policy sciences, Information sciences, Womens studies,

Molecular biology, Philosophy of Computing and Information,Bioinformatics, ..

CROSS DISCIPLINARY RESEARCH FIELDS


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Disciplinary change is a present day phenomenon.

The discovery of DNA in the 1970s was a cognitive revolution

which refigured traditional demarcations of physics, chemistry andbiology.

New fields of application arose.

New discoveries, tools, and approaches change the way that

research is conducted at empirical and methodological

levels.

SCIENCE, RESEARCH, DEVELOPMENT AND TECHNOLOGY


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Science

Research

Development

Technology

TECHNOLOGY EXPANDS OUR WAYSOF

THINKING ABOUT THINGS, EXPANDSOUR WAYS OF DOING THINGS.

Herbert A. Simon

n

and

Crude

Com


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A

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Learning

a

Language

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andomness

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plexity

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7What Is Fundamental?9

89The Power of Theory8

75The Scientific Enterprise7

63Bacteria Developing Drug Resistance6

51A Child Learning a Language5

43Randomness4

23Information and Crude Complexity3

11Early Light2

3Prologue: An Encounter in the Jungle1

The Simple and the ComplexPart I

ixPreface

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The Quantum UniversePart II


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215Time's Arrows: Forward and Backward Time15

199Superstring Theory: Unification at Last?14

177Quarks and All That: The Standard Model13

167Quantum Mechanics and Flapdoodle12

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A Contemporary View of Quantum Mechanics:

Quantum Mechanics and the Classical

Approximation

11

123Simplicity and Randomness in the Quantum

Universe10

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Selection at Work in Biological Evolution and

Elsewhere16

Selection and FitnessPart III


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7Index

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7Afterword23

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5Transitions to a More Sustainable World22

32

9Diversities Under Threat21

Diversity and SustainabilityPart IV

30

7Machines That Learn or Simulate Learning20

29

1Adaptive and Maladaptive Schemata19

27

5Superstition and Skepticism18

26

1From Learning to Creative Thinking17

CLASSICAL SCIENCESHAVE SPECIFIC AREAS OF VALIDITY


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Scientific Worldview: the Structure of Matter


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DNA - Deoxyribonucleic Acid


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DNA is the primary chemical component ofchromosomes and the material of which genes are made

DNA BASE MOLECULE


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MOLECULE - ATOM


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ELEMENTARY PARTICLES AND FORCES


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POSTMODERNISM

From the mid 1970s to the late 1990s a cluster of anti-rationalist


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ideas became increasingly prevalent among academicsociologists in America, France and Britain.

Those ideas have formed following fields- Deconstructionism- Sociology of Scientific Knowledge (SSK)- Social Constructivism or- Science and Technology Studies (STS).

The umbrella term for above movements was Postmodernism.


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POSTMODERNISTS ANTI-SCIENTISM


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``The dictum that everything that people do is 'cultural' ... licenses the ideathat every cultural critic can meaningfully analyze even the most

intricate accomplishments of art and science. ... It is distinctly weird tolisten to pronouncements on the nature of mathematics from the lips ofsomeone who cannot tell you what a complex number is!''

Norman Levitt, from "The flight From Science and Reason," New York

Academy of Science. Quoted from p. 183 in the October 11, 1996Science)



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Modernism has provided the philosophical foundation for much of Westernculture since the Enlightenment. Modernism reaches its highest form in

science although this approach arguably influences all of Westernculture.

Inherent in modernism is the notion of an essence: the truth behind the

appearances we see around us. Science is about discovering theseessences as science slowly reveals the truth about the world aroundus. Postmodernist attacks on essentialism have taken aim at thismodernism version of essentialism.



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Third, postmodernists assert that because no interpretative frameworkcan be objectively shown to be true or false, the choice of interpretative

framework is purely relativist and subjective. We are in the world ofsubjective values. That is, anything goes.

This postmodern perspective provokes astonishment among those

working within a modernist framework. For the most part, such peoplemerely scoff at postmodernist attacks and believe no response isneededor possibleto what is seen as irrational anti-scientism.

Objectivity and Values


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Postmodernisms attack on modernism has undercut modernismspretension to scientific objectivity. Yet postmodernisms success is

actually very narrow and their devastating critique of modernism doesnot carry over to non-modernist perspectives on essences. Despite thegreat confidence of many postmodern thinkers, postmodernism makessense as a general critique only if you accept two flaws of logic.

Postmodernist values appear to be the following: people have a right to decide what to believe, oppression is bad, and diversity of interpretive frameworks is good.

An Alternative Resolution


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Both modernism and postmodernism subscribe to the false dilemmadiscussed above: we face a starkand necessarychoice betweenthe correspondence theory of truth and the subjectivist approach.

But in time, both in philosophy and politics, new ideas become old ideas; what wasonce challenging, becomes predictable and boring; and what once served to focusattention where it should be focused, later keeps discussion from considering newalternatives. This has now happened in the debate between the correspondence viewsof truth and subjectivist views.

Hilary Putnam Reason, Truth and History, Cambridge University Press, 1981, page x.


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The new discourse centers on problem and solution oriented

AFTER POSTMODERNISMS DEATH ...

Interdisciplinarity and Complexity


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The new discourse centers on problem- and solution-orientedresearch incorporating participatory approaches:

problem-oriented,

beyond disciplinarity,

practice-oriented,

participatory, and process-oriented.

Interdisciplinarity is necessitated by complexity The nature of

EFTER POSTMODERNISMENS DD ...

Interdisciplinarity and Complexity


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Interdisciplinarity is necessitated by complexity. The nature ofcomplex systems, provides a comprehensive rationale for

interdisciplinary study, unifies the apparently divergentapproaches, and offers guidance for criteria in each step of theintegrative process.

The ultimate objective of any interdisciplinary inquiry becomesunderstanding the portion of the world modeled by a particularcomplex system. (William Newell)

art of doing sciecnee2

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