1. 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?

2. 2 What we will be talking about. The term complex systems refers to a broad range of disciplines and ways of thinking. (The term complexity is also used this way.) –It is not intended to refer to a particular category of systems, which are presumably distinguished from other systems that aren’t complex. But if I had to define what a complex system is … –A system of autonomous elements that interact both with each other and with their environment and that exhibits macro behaviors that none of the elements exhibit individually. System: a construct or collection of different elements that together produce results not obtainable by the elements alone. Isn’t that true of all systems? We are in the business of producing complex systems. 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.

3. 3 Why should you care? Because our customers and their contractors think it’s important. –You should understand what they are talking about. –Rumsfeld’s inspiration for transformation in the military grew out of this way of thinking. This field is not new. It is at least 2 decades old. –The Command and Control Research Program in the Pentagon (Dave Alberts) is successfully promoting this style of thinking within the DoD. –Net-centricity—and the way the world has changed as a result of the web—illustrates this way of thinking. Because it gives you a powerful new way to think about how systems work. Because large systems—and especially systems of systems—tend to be complex in the ways we will discuss. –Because systems-of-systems are considered important by our customer. Because the ideas are interesting, important, and good for you.

4. 4 Arcadia, by Tom Stoppard It makes me so happy. To be at the beginning again, knowing almost nothing.... A door like this has cracked open five or six times since we got up on our hind legs. It's the best possible time to be alive, when almost everything you thought you knew is wrong.

5. 5 Complex systems course outline Morning 8:00–9:00. Unintended consequences – mechanism, function, and purpose; introduction to NetLogo. 9:00–10:30. Emergence – the reductionist blind spot and levels of abstraction. 10:30–10:45. Break. 10:45–11:30. Agent-based modeling; thought externalization; how engineers and computer scientists think. Afternoon 12:30–1:30. Evolution and evolutionary computing. 1:30–2:15. Innovation – exploratory behavior; initiative and integration; resource allocation. 2:15–2:30. Break. 2:30–3:15. Platforms – distributed control and systems of systems. 3:15–4:15. Groups – the wisdom of crowds. 4:15–4:30. Summary/conclusions – remember this if nothing else.

6. 6 Complex systems course overview 9:00–9:15. Unintended consequences – mechanism, function, and purpose; introduction to NetLogo. 9:15–9:30. Emergence – the reductionist blind spot and levels of abstraction. 9:30–9:45. Agent-based modeling; thought externalization; how engineers and computer scientists think. 9:45–10:00. Evolution and evolutionary computing. 10:00–10:10. Break. 10:10–10:25. Innovation – exploratory behavior; initiative and integration; resource allocation. 10:25–10:40. Platforms – distributed control and systems of systems. 10:40–10:55. Groups – the wisdom of crowds. 10:55–11:00. Summary/conclusions – remember this if nothing else.

7. 7 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.

8. 8 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?

9. 9 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.

10. 10 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. That’s how nature works. Upcoming ideas: platforms, stigmergy.

11. 11 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

12. 12 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.

13. 13 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

14. 14 NetLogo: let’s try it File > Models Library > Biology > Ants Click Open In the full course, students would run NetLogo.

15. 15 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?

16. 16 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 we’ll see later, each layer is a level of abstraction

17. 17 Complex systems terms Emergence. A level of abstraction that can be described independently of its implementation. –Examples include the movement of 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.

18. 18 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Emergence: what’s right and what’s wrong with reductionism Presumptuous again? Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

19. 19 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

20. 20 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. It’s a fine-sounding word, and brings to mind southwestern creation myths in an oddly apt way.

21. 21 Erwin Schrödinger “[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 Schrödinger, 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.

22. 22 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

23. 23 The Game of Life Click Open File > Models Library > Computer Science > Cellular Automata > Life In the full course, students would run NetLogo.

24. 24 Gliders are causally powerless. –A glider does not change how the rules operate or which cells will be switched on and off. A glider doesn’t “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 don’t “notice” gliders — any more than gliders “notice” cells. But … –One can write down equations that characterize glider motion and predict whether—and if so when—a glider will “turn on” a particular cell. –What is the status of those equations? Are they higher level laws? Gliders Like shadows, they don’t “do” anything. The rules are the only “forces!”

25. 25 Amazing as they are, gliders are also trivial. –Once we know how to produce a glider, it’s 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.

26. 26 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, not derivable from the GoL rules. Downward causation entailment

27. 27 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—its way of being in the world—makes it ontologically independent. A level of abstraction is causally reducible to its implementation. Its independent specification—its way of being in the world—makes it ontologically independent. Examples The collection of Game of Life patterns. –One can catalog the patterns and their interactions without ever talking about Game of Life rules A Game of Life Turing Machine. Turing described it independently of any implementation.

28. 28 The reductionist blind spot Darwin and Wallace’s theory of evolution by natural selection is expressed in terms of –entities –their properties –how suitable the properties of the entities are for the environment –populations –reproduction –etc. These concepts are a level of abstraction. –The theory of evolution is about entities at that level of abstraction. Let’s assume that it’s (theoretically) possible to trace how any state of the world—including the biological organisms in it—came about by tracking elementary particles Even so, it is not possible to express the theory of evolution in terms of elementary particles. Reducing everything to the level of physics, i.e., naïve reductionism, results in a blind spot regarding higher level entities and the laws that govern them. Darwin and Wallace’s theory of evolution by natural selection is expressed in terms of –entities –their properties –how suitable the properties of the entities are for the environment –populations –reproduction –etc. These concepts are a level of abstraction. –The theory of evolution is about entities at that level of abstraction. Let’s assume that it’s (theoretically) possible to trace how any state of the world—including the biological organisms in it—came about by tracking elementary particles Even so, it is not possible to express the theory of evolution in terms of elementary particles. Reducing everything to the level of physics, i.e., naïve reductionism, results in a blind spot regarding higher level entities and the laws that govern them.

29. 29 How are levels of abstraction built? By adding persistent constraints to what exists. –Constraints “break symmetry” by ruling out possible future states. –Should be able to relate this to symmetry breaking more generally. Easy in software. –Software constrains a computer to operate in a certain way. –Software (or a pattern set on a Game of Life grid) “breaks the symmetry” of possible sequences of future states. How does nature build levels of abstraction? Two ways. –Energy wells produce static entities. Atoms, molecules, solar systems, … –Activity patterns use imported energy to produce dynamic entities. The constraint is imposed by the processes that the dynamic entity employs to maintain its structure. Biological entities, social entities, hurricanes. A constrained system operates differently (has additional laws— the constraints) from one that isn’t constrained. I’m showing this slide to invite anyone who is interested to work on this with me. Isn’t this just common sense? Ice cubes act differently from water and water molecules.

30. 30 Practical corollary: feasibility ranges Levels of abstraction are implemented only within feasibility ranges. When the feasibility range is exceeded a phase transition generally occurs. Require contractors 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.)

31. 31 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Modeling, the externalization of thought, and how engineers and computer scientists think Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

32. 32 Modeling problems: the difficulty of looking downward Strict reductionism implies that it is impossible to find a non-arbitrary base level for models. –What are we leaving out that might matter? Use Morse code to transmit messages on encrypted lines. No good models of biological arms races. –Combatants exploit and/or disrupt or otherwise foil each other’s epiphenomena. Insects vs. plants: bark, bark boring, toxin, anti-toxin, …. Geckos use the Van der Waals “force” to climb. Models of computer security or terrorism will always be incomplete. Can only model unimaginative enemies. Epiphenomenal Nature is not segmented into a strictly layered hierarchy.

33. 33 Don’t know how to build models that can notice emergent phenomena and characterize their interactions. We don’t know what we aren’t noticing. –We/they can use our commercial airline system to deliver mail/bombs. Model gravity as an agent-based system. –Ask system to find equation of earth’s orbit. –Once told what to look for, system can find ellipse. (GP) –But it won’t notice the yearly cycle of the seasons — even though it is similarly emergent. Modeling problems: the difficulty of looking upward Models of computer security or terrorism will always be incomplete. Can only model unimaginative enemies. Exploit an existing process

34. 34 Intellectual leverage in Computer Science: executable externalized thought Computer languages enable executable externalized thought— different from all other forms of externalized thought throughout history! –There is nothing comparable in engineering—or any other field. –All other forms of externalized thought require a human being to interpret them. The bit provides a floor that is both symbolic and real. –Bits are: symbolic, physically real, and atomic. –Bits don’t have error bars. –Can build (ontologically real) levels of abstraction above them. But the bit limits realistic modeling. –E.g., no good models of evolutionary arms races and many other multi-scale (biological) phenomena. No justifiable floor. –Challenge: build a computer modeling framework that supports dynamically varying floors.

35. 35 Engineering is both cursed and blessed by its attachment to physicality. –There is no reliable floor. “Engineering systems often fail … because of [unanticipated interactions among well designed components, e.g. acoustic coupling] that could not be identified in isolation from the operation of the full systems.” National Academy of Engineering, Design in the New Millennium, 2000. –But if a problem appears, engineers (like scientists) can dig down to a lower level to solve it. Intellectual leverage in Engineering: mathematical modeling Engineering gains intellectual leverage through mathematical modeling and functional decomposition. –Models approximate an underlying reality (physics). –Models are judged by the width of their error bars. –Models don’t create ontologically independent entities.

36. 36 Engineers and computer scientists are different — almost as different as Venus and Mars Computer scientists live in a world of abstractions. –Physics has very little to do with computer science worlds. –For computer scientists, there is more than physics, i.e., emergence—but may have had a hard time saying what it is. –When designing systems, Computer scientists start with the bit and build it up to the idea—using levels of abstraction. Computer science is (cautiously) applied philosophy. Computer scientists live in a world of abstractions. –Physics has very little to do with computer science worlds. –For computer scientists, there is more than physics, i.e., emergence—but may have had a hard time saying what it is. –When designing systems, Computer scientists start with the bit and build it up to the idea—using levels of abstraction. Computer science is (cautiously) applied philosophy. Engineers are grounded in physics. –Ultimately there is nothing besides physics. –Even though engineers build things that have very different (emergent) properties from their components, engineers tend to think at the level of physics. –When designing systems, engineers start with an idea and build it down to the physics—using functional decomposition. Engineering is (proudly) applied physics. Engineers are grounded in physics. –Ultimately there is nothing besides physics. –Even though engineers build things that have very different (emergent) properties from their components, engineers tend to think at the level of physics. –When designing systems, engineers start with an idea and build it down to the physics—using functional decomposition. Engineering is (proudly) applied physics.