Organized complexity

1. Organized complexity

2. This week’s discussion • Papers: • Lazebnik, Y [2002]. "Can a biologist fix a radio?--Or, what I learned while studying apoptosis". Cancer Cell, 2(3):179-182. • Simon, H.A. [1962]. "The Architecture of Complexity". Proceedings of the American Philosophical Society, 106: pp. 467-482. • Klir, G.J. [2001]. Facets of systems Science. Springer. Chapters: 3, 8, and 11.

3. Systems movement • Roots: • Mathematics • Computer Technology • Systems Thinking • Cybernetics • Functional equivalence • Communication and information • Complexity • Interdisciplinary outlook • Bio-inspired mathematics and computing • Computing/Mechanism-inspired biology and social science • 1965: Society for the Advancement of General Systems Theory Kenneth Boulding Ludwig von Bertalanffy Ralph Gerard Anatol Rapoport

4. Warren Weaver’s classes of systems and problems • Organized simplicity • Classical mathematical tools • Calculus and differential equations • Problems with a very small number of components • With perfectly predicted behavior • Deterministic • Disorganized complexity • Statistical tools • Very large number of components • High degree of unpredictability • Randomness • Organized complexity • Sizable number of components which are interrelated into an organic whole • Study of organization • Systems where whole is more than sum of parts • Need for new mathematical and computational tools Disorganized complexity Organized Complexity Randomness Organized simplicity Weaver, W (1948) Science and Complexity, American Scientist, 36: 536 (1948). Complexity

5. Examples Disorganized complexity Organized Complexity Randomness Organized simplicity Complexity

6. From systems science to informatics • organized complexity • study of organization • “Whole is more than sum of parts” • Systemhood properties • Holism vs. Reductionism • Need for new mathematical and computational tools • Massive combinatorial searches • Problems that can only be tackled with computers • Computer as lab • Understanding function • Of wholes • Systems biology • Evolutionary thinking • Systems thinking • Emergence: How do elements combine to form new unities? Disorganized complexity Organized Complexity Randomness Organized simplicity Complexity

7. Models of organized complexity • Systemhood properties • Search for a language of generalized circuits • Isomorphy of concepts, laws and models • Minimize duplication of efforts across fields • Unity of science • Not mathematics. Kenneth Boulding: • “in a sense, because mathematics contains all theories it contains none; it is the language of theory, but it does not give us the content” • “body of systematic theoretical construction which will discuss general relationships of the empirical World”. • “somewhere between the specific that has no meaning and the general that has no content there must be, for each purpose an at each level of abstraction, an optimum degree of generality”. • Empirical and problem-driven • Other relevant areas • Cybernetics and Information theory (Shannon and Weaver) • Mathematical theories of control and generalized circuits • Optimal scheduling and resource allocation (operations research) Kenneth Boulding Ludwig von Bertalanffy Boulding's 1st Law: "Anything that exists is possible."

8. “Two-dimensional science” • Science in the post-industrial age • Industrial society • One-dimensional science • Organized simplicity and disorganized complexity • Thinghood-driven, reductionist • Information society • Two-dimensional science • Thinghood and systemhood • Integration of empirical science with general systems • Problem-driven, understanding function • Understanding of levels of generality • Historical sequence of societies: • Pre-industrial: “extractive” industries, manual labor, mining, low energy density • Industrial: large-scale production, machine technology • Information: computational technology and trades, information processing, services • Man -> machine -> computer George Klir

9. Stepping back a bit: information, what is it? Thing SIGN ICON

10. Stepping back a bit: information, what is it? “Information is that which reduces uncertainty”. (Claude Shannon) “Information is that which changes us”. (Gregory Bateson) “Information is a semantic chameleon”. (Rene Thom) The word information derives from the Latin informare in + formare = give form, shape, or character to. It is therefore to be the formative principle of, or to imbue with some specific character or quality. From: Von Bayer, H.C. [2004]. Information: The New Language of Science. Harvard University Press., Chapter 3, pp 20-21.\

11. Systems science: cross-disciplinary • For hundreds of years, the word information has been used to signify knowledge and related terms such as meaning, instruction, communication, representation, signs, symbols, etc. • “the action of informing; formation or molding of the mind or character, training, instruction, teaching; communication of instructive knowledge”. Oxford English Dictionary • Two of the most outstanding achievements of science in the XX century • Invention of Digital Computers and Information Technology • Birth of Molecular Biology • Resulted in the generation of vast amounts of data and information and new understandings of the concept of information itself • Modern science is unraveling the nature of information in numerous areas such as communication theory, biology, neuroscience, cognitive science, and education, among others. • Organization very tied to idea of information • Essential for systems approaches • Cf. Rosen’s comments on energy vs. communication

12. Information as representation • We often presume that such and such information is simply a factual representation of reality • but representation of reality to whom? • The act of representing something as a piece of knowledge demands the existence of a separation between the thing being represented and the representation of the thing for somebody – between the known and the knower. • This is a form of communication: • the representation of an object communicates the existence of the (known) object to the knower that recognizes the representation.

13. Information as representation • Signs are objects whose function is to be about other things • Objects whose function is reference rather than presence. • Do not deliver things but a sense or knowledge of things – a message. • Example: RoadSigns • Not a distant thing; but about distant things • For information to work • There has to be a system of signs • Recognizable by the relevant group of people (drivers!)

14. Information as relation • The central structure of information is a relation • among signs, objects or things, and agents capable of understanding (or decoding) the signs. • Agents are informed by a Signabout some Thing. sign thing agents

15. Information as relation • The information relation is a sign system • Semiotics is the discipline that studies sign systems sign thing agents

16. Playing with sign systems • Language and sign systems surround us • We are often not aware we use them • We notice them when an object oscillates between sign and thing • Reverts from reference to presence • Playing with reference in sign systems is common in Art “beware: Cliff” Or “beware: low gravity”?

17. Playing with sign systems • Symbols are used as pictorial objects to draw the picture of Kitty: presence • But within the silhouette of Kitty there is also a tale of cats: reference by John Hollander. Kitty, Black domestic shorthair

18. The name of the rose • Movie version of the Umberto Eco’s book • An old manuscript, the message, is literarily dangerous • Becomes literally poisonous • reference and presence become very intertwined indeed!

19. Play on reference • The accepted meaning of the symbols conflicts with the object • Highlights how arbitrary symbols are “This is not a pipe” The Key of Dreams, 1930, Rene Maggritte

20. When is an object a sign or a thing?

21. Semiotics and informatics • Semantics • the content or meaning of the Signof a Thing for an Agent • Relations between signs and objects for an agent • the study of meaning. • Syntax • the characteristics of signs and symbols devoid of meaning • Relations among signs such as their rules of operation, production, storage, and manipulation. • Pragmatics • the context of signs and repercussions of sign-systems in an environment • it studies how context influences the interpretation of signs and how well a signs-system represents some aspect of the environment information infromatics

22. (Peirce’s) Typology of Signs • Icons are direct representations of objects. • Similar to the thing they represent. • Pictorial road signs, scale models, computer icons. • A footprint on the sand is an icon of a foot. • Common in computer interface (watch the evil metaphore!)

23. (Peirce’s) Typology of Signs • Indices are indirect representations of objects, but necessarily related. • Smoke is an index of fire, the bell is an index of the tolling stroke • a footprint is an index of a person.

24. (Peirce’s) Typology of Signs • Symbols are arbitrary representations of objects • Require exclusively a social convention to be understood • Convention establishes a code, agreed by a group of agents, for understanding (decoding) the information contained in symbols. • Smoke is an index of fire, but if we agree on an appropriate code (e.g. Morse code) we can use smoke signals to communicate symbolically. • Internally consistent coding + indices: • ~ non-arbitrary symbols

25. (Peirce’s) Typology of Signs • Icons are direct representations of objects. • Similar to the thing they represent. • Pictorial road signs, scale models, computer icons. • A footprint on the sand is an icon of a foot. • Indices are indirect representations of objects, but necessarily related. • Smoke is an index of fire, the bell is an index of the tolling stroke • a footprint is an index of a person. • Symbols are arbitrary representations of objects • Require exclusively a social convention to be understood • Convention establishes a code, agreed by a group of agents, for understanding (decoding) the information contained in symbols. • Smoke is an index of fire, but if we agree on an appropriate code (e.g. Morse code) we can use smoke signals to communicate symbolically.

26. Assignment: Blackbox I • http://www.cs.indiana.edu/~jbollen/blackboxI501/BlackBox.html • Due on October 21, 2009 (16:00 EDT) • Deliver to OnCourse folder • Deliverable: report on your findings with regards to quadrants 2 and 3 • Describe observations, collect data from online applet • Formulate hypotheses with regards to underlying algorithm • Verify/falsify hypotheses by means of data analysis Quadrant 2 Quadrant 3

27. Discussion questions • Lazebnik, Y [2002]. "Can a biologist fix a radio?--Or, what I learned while studying apoptosis". Cancer Cell, 2(3):179-182. • Lazebnik seems to omit one important distinction between engineers and scientists. How does it affect his argument? • Simon, H.A. [1962]. "The Architecture of Complexity". Proceedings of the American Philosophical Society, 106: pp. 467-482. • Simon discusses the issue of hierarchic span. What kind of effect could the internet and modern communication systems have on the span/broadness of social systems? • Klir, G.J. [2001]. Facets of systems Science. Springer. Chapters: 3, 8, and 11. • Klir discusses Bremermann’s limit: can you think of ways of computing that would alter this limit?

28. Aleksander, I. [2002]. “Understanding Information Bit by Bit”. In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Granta, London. Rosvall, M and Bergstrom, C (2007) Maps of random walks on complex networks reveal community structure. PNAS January 29, 2008 vol. 105 no. 4 1118-1123 Next class