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1 Introduction to Complex Systems: How to think like nature 1998-2007. The Aerospace Corporation. All Rights Reserved. Course overview: two hours Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org A bit presumptuous? Besides, does nature really think? Slide 2 2 Complex systems course outline Morning 8:009:00. Unintended consequences mechanism, function, and purpose; introduction to NetLogo. 9:0010:30. Emergence the reductionist blind spot and levels of abstraction. 10:3010:45. Break. 10:4511:30. Agent-based modeling; thought externalization; how engineers and computer scientists think. Afternoon 12:301:30. Evolution and evolutionary computing. 1:302:15. Innovation exploratory behavior; initiative and integration; resource allocation. 2:152:30. Break. 2:303:15. Platforms distributed control and systems of systems. 3:154:15. Groups the wisdom of crowds. 4:154:30. Summary/conclusions remember this if nothing else. Slide 3 3 Complex systems course overview 9:009:15. Unintended consequences mechanism, function, and purpose; introduction to NetLogo. 9:159:30. Emergence the reductionist blind spot and levels of abstraction. 9:309:45. Agent-based modeling; thought externalization; how engineers and computer scientists think. 9:4510:00. Evolution and evolutionary computing. 10:0010:10. Break. 10:1010:25. Innovation exploratory behavior; initiative and integration; resource allocation. 10:2510:40. Platforms distributed control and systems of systems. 10:4010:55. Groups the wisdom of crowds. 10:5511:00. Summary/conclusions remember this if nothing else. Slide 4 4 Introduction to Complex Systems: How to think like nature 1998-2007. The Aerospace Corporation. All Rights Reserved. Unintended consequences: mechanism, function, and purpose Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org This segment introduces some basic concepts. Slide 5 5 A fable Once upon a time, a state in India had too many snakes. To solve this problem the government instituted an incentive- based program to encourage its citizens to kill snakes. It created the No Snake Left Alive program. Anyone who brings a dead snake into a field office of the Dead Snake Control Authority (DSCA) will be paid a generous Dead Snake Bounty (DSB). A year later the DSB budget was exhausted. DSCA had paid for a significant number of dead snakes. But there was no noticeable reduction in the number of snakes plaguing the good citizens of the state. What went wrong? Slide 6 6 The DSCA mechanism Catch, kill, and submit a dead snake. DSCA Receive money. Dead snake verifier Receive dead snake certificate. Submit certificate to DSCA. What would you do if this mechanism were available in your world? Start a snake farm. Slide 7 7 Moral: unintended consequences The preceding is an example of what is sometimes called an unintended consequence. It represents an entire category of (unintended and unexpected) phenomena in which a mechanism is installed in an environment, but then the mechanism is used/exploited in unanticipated ways. Once a mechanism is installed in the environment, it will be used for whatever purposes users can think to make of it which may not be that for which it was originally intended. The first lesson of complex systems thinking is that one must always be aware of the relationship between systems and their environments. Thats how nature works. Upcoming ideas: platforms, stigmergy. Slide 8 8 Parasites that control their hosts Dicrocoelium dendriticum causes host ants to climb grass blades where they are eaten by grazing animals, which is where D. dendriticum lives out its adult life. Toxoplasma gondii cause mice not to fear cats, which is where T. gondii reproduces. Spinochordodes tellinii causes host insects to jump into the water and drown, where S. tellinii grows to adulthood. May skip Slide 9 9 Locomotion in E. coli E. coli movements consist of short straight runs, each lasting a second or less, punctuated by briefer episodes of random tumbling. Each tumble reorients the cell and sets it off in a new direction. Cells that are moving up the gradient of an attractant tumble less frequently than cells wandering in a homogeneous medium or moving away from the source. In consequence, cells take longer runs toward the source and shorter ones away. Harold, Franklyn M. (2001) The Way of the Cell: Molecules, Organisms, and the Order of Life, Oxford University Press. Upcoming idea: exploratory behavior. Slide 10 10 Mechanism, function, and purpose* Mechanism: The physical processes within an entity. The chemical reactions built into E.coli that result in its flagella movements. The DSCA mechanism. Function: The effect of a mechanism on the environment and on the relationship between an entity and its environment. E. coli moves about. In particular, it moves up nutrient gradients. Snakes are killed and delivered; money is exchanged. Purpose: The (presumably positive) consequence for the entity of the change in its environment or its relationship with its environment. E. coli is better able to feed, which is necessary for its survival. Snake farming is encouraged? *Compare to Measures of Performance, Effectiveness, and Utility Wikipedia Commons Socrates Slide 11 11 NetLogo: lets try it File > Models Library > Biology > Ants Click Open In the full course, students would run NetLogo. Slide 12 12 population: number of ants diffusion-rate: rate at which the chemical (pheromone) spreads evaporation-rate: rate at which chemical evaporates Ant rules If you are not carrying food, Move up the chemical-scent gradient, if any. Pick up food, if any. Otherwise move randomly. If you are carrying food, move up the nest-scent gradient. When you reach the nest, deposit the food. In to look-for-food procedure, change orange to blue. After running once, play around with the population, diffusion-rate, and evaporation-rate. Simple ant foraging model Turns plotting on/off. Implemented chemically in real ants, by software in NetLogo. Can you get this picture, with paths to all food sources simultaneously? Slide 13 13 Two levels of emergence No individual chemical reaction inside the ants is responsible for making them follow the rules that describe their behavior. That the internal chemical reactions together do is an example of emergence. No individual rule and no individual ant is responsible for the ant colony gathering food. That the ants together bring about that result is a second level of emergence. Colony results Ant behaviors Ant chemistry Notice the similarity to layered communication protocols Presentation Session Transport Network Physical WWW (HTML) browsers + servers Applications, e.g., email, IM, Wikipedia As well see later, each layer is a level of abstraction Slide 14 14 Complex systems terms Emergence. A level of abstraction that can be described independently of its implementation. Examples include the movement E. coli and ants through space toward a food source, which can be described independently of how it is brought about. Multi-scalar. Applicable to systems that are understood on multiple levels simultaneously, especially when a lower level implements the emergence of some functionality at a higher level. E. coli motion and ant foraging are both examples of multi-scalar systems. System: a construct or collection of different elements that together produce results not obtainable by the elements alone. Isnt that true of all systems? We are in the business of producing emergence System: a construct or collection of different elements that together produce results not obtainable by the elements alone. Eberhardt Rechtin Systems Architecting of Organizations: Why Eagles Can't Swim, CRC, 1999. System: a construct or collection of different elements that together produce results not obtainable by the elements alone. Eberhardt Rechtin Systems Architecting of Organizations: Why Eagles Can't Swim, CRC, 1999. Slide 15 15 Introduction to Complex Systems: How to think like nature 1998-2007. The Aerospace Corporation. All Rights Reserved. Emergence: whats right and whats wrong with reductionism Presumptuous again? Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org Slide 16 16 How macroscopic behavior arises from microscopic behavior. Emergent entities (properties or substances) arise out of more fundamental entities and yet are novel or irreducible with respect to them. Stanford Encyclopedia of Philosophy http://plato.stanford.edu/entries/properties-emergent/ Emergence: the holy grail of complex systems The scare quotes identify problematic areas. Plato Slide 17 17 Cosma Shalizi http://cscs.umich.edu/~crshalizi/reviews/holland-on-emergence/ Someplace where quantum field theory meets general relativity and atoms and void merge into one another, we may take the rules of the game to be given. But the rest of the observable, exploitable order in the universe benzene molecules, PV = nRT, snowflakes, cyclonic storms, kittens, cats, young love, middle-aged remorse, financial euphoria accompanied with acute gullibility, prevaricating candidates for public office, tapeworms, jet-lag, and unfolding cherry blossoms Where do all these regularities come from? Call this emergence if you like. Its a fine-sounding word, and brings to mind southwestern creation myths in an oddly apt way. Slide 18 18 Erwin Schrdinger [L]iving matter, while not eluding the laws of physics is likely to involve other laws, [which] will form just as integral a part of [its] science. Erwin Schrdinger, What is Life?, 1944. Steven Weinberg Why is there anything except physics? Jerry Fodor John Holland The ability to reduce everything to simple fundamental laws [does not imply] the ability to start from those laws and reconstruct the universe. [We] must all start with reductionism, which I fully accept. More is Different (Science, 1972) Philip Anderson The ultimate reductionist. Slide 19 19 Are there autonomous higher level laws of nature? The fundamental dilemma of science How can that be if everything can be reduced to the fundamental laws of physics? The functionalist claim The reductionist position It can all be understood as levels of abstraction. My answer Slide 20 20 The Game of Life Click Open File > Models Library > Computer Science > Cellular Automata > Life In the full course, students would run NetLogo. Slide 21 21 Gliders are causally powerless. A glider does not change how the rules operate or which cells will be switched on and off. A glider doesnt go to an cell and turn it on. A Game of Life run will proceed in exactly the same way whether one notices the gliders or not. A very reductionist stance. Cells dont notice gliders any more than gliders notice cells. But One can write down equations that characterize glider motion and predict whetherand if so whena glider will turn on a particular cell. What is the status of those equations? Are they higher level laws? Gliders Like shadows, they dont do anything. The rules are the only forces! Slide 22 22 Amazing as they are, gliders are also trivial. Once we know how to produce a glider, its simple to make them. Can build a library of Game of Life patterns and their interaction APIs. By suitably arranging these patterns, one can simulate a Turing Machine. Paul Rendell. http://rendell.server.org.uk/gol/tmdetails.htm Game of Life Programming Platform A second level of emergence. Emergence is not particularly mysterious. Slide 23 23 Downward causation The unsolvability of the TM halting problem entails the unsolvability of the GoL halting problem. How strange! We can conclude something about the GoL because we know something about Turing Machines. Earlier, we dismissed the notion that a glider may be said to go to a cell and turn it on. Because of downward entailment, there is hope for talk like this. One can write glider velocity laws and then use those laws to draw conclusions (make predictions) about which cells will be turned on and when that will happen. GoL gliders and Turing Machines are causally reducible yet ontologically real. They obey higher level laws, which are not derivable from the GoL rules. Downward causation entailment Slide 24 24 Level of abstraction A collection of concepts and relationships that can be described independently of its implementation. Every computer application creates one. A collection of concepts and relationships that can be described independently of its implementation. Every computer application creates one. A level of abstraction is causally reducible to its implementation. Its independent specification makes it ontologically real. A level of abstraction is causally reducible to its implementation. Its independent specification makes it ontologically real. Slide 25 25 Practical corollary: feasibility ranges Entities are implemented only within feasibility ranges. When the feasibility range is exceeded a phase transition generally occurs. Contractors should be required to identify the feasibility range within which the implementation will succeed and describe the steps taken to ensure that those feasibility ranges are honored and what happens if they are not. (Think O-rings.)