1. 1 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Course overview: part 2 Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

2. 2 Complex systems course overview 9:00–9:10. Introduction and motivation. 9:10–9:25. Unintended consequences – mechanism, function, and purpose; introduction to NetLogo. 9:25–9:45. Emergence – the reductionist blind spot and levels of abstraction. 9:45–9:55. Modeling; thought externalization; how engineers and computer scientists think. 9:55–10:05. Break. 10:05–10:15. Evolution and evolutionary computing. 10:15–10:30. Innovation – exploratory behavior; initiative and integration; resource allocation. 10:30–10:45. Platforms – distributed control and systems of systems. 10:45–10:55. Groups – the wisdom of crowds. 10:55–11:00. Summary/conclusions – remember this if nothing else.

3. 3 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Evolution: how nature thinks Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

4. 4 Peppered moths: evolution in action Originally, the vast majority of peppered moths in Manchester, England had light coloration— which camouflaged them from predators since they blended into the light-colored trees. With the industrial revolution: –Pollution blackened the trees. –Light-colored moths died off. –Dark-colored moths flourished. With improved environmental standards, light- colored peppered moths have again become common.

5. 5 Try it out File > Models Library > Biology > Evolution > Peppered Moths Click Open

6. 6 The evolutionary process There is a population of elements. The elements are capable of making copies of themselves –perhaps with variants (mutations) and –perhaps by combining with other elements. The environment affects the likelihood of an element surviving and reproducing. This results in “evolution by natural (i.e., environmental) selection.” –Darwin likened it to breeding. The environment plays the role of the breeder.

7. 7 The nature of evolution Moth coloring confers survival value (fitness)—which depends on the environment. –Hence Darwin’s “natural selection,” i.e., environmental selection. –The environment selects the winners. There may be multiple “winners.” All one needs is a niche, not domination. Moth coloring confers survival value (fitness)—which depends on the environment. –Hence Darwin’s “natural selection,” i.e., environmental selection. –The environment selects the winners. There may be multiple “winners.” All one needs is a niche, not domination. Nature is not necessary “red in tooth and claw.” The moths and their colors don’t compete with each other directly. –There are no moth-on-moth battles. –Nor do the dark moths attempt to convince the light moths that it’s better to be dark — or vice versa. Moths (and their colors) are rivals, not adversaries. –It’s more like a race than a boxing match. –They are rivals with respect to their ability to survive and acquire resources from the environment. Nature is not necessary “red in tooth and claw.” The moths and their colors don’t compete with each other directly. –There are no moth-on-moth battles. –Nor do the dark moths attempt to convince the light moths that it’s better to be dark — or vice versa. Moths (and their colors) are rivals, not adversaries. –It’s more like a race than a boxing match. –They are rivals with respect to their ability to survive and acquire resources from the environment.

8. 8 Six time scales of evolution Social/economic/cultural systems evolve at medium speeds. –As rivals: a social system that does well for its members thrives and expands. –As adversaries: social systems sometimes compete for resources—land in the past; now other resources. Social/economic/cultural systems evolve at medium speeds. –As rivals: a social system that does well for its members thrives and expands. –As adversaries: social systems sometimes compete for resources—land in the past; now other resources. Markets are evolution speeded-up. –Coke and Pepsi are rivals for consumer dollars, not adversaries. –They don’t attempt to kill each other’s CEOs or to sabotage each other’s delivery trucks. Markets are evolution speeded-up. –Coke and Pepsi are rivals for consumer dollars, not adversaries. –They don’t attempt to kill each other’s CEOs or to sabotage each other’s delivery trucks. Warfare: often super fast evolution. –IED tactics and counter tactics. Warfare: often super fast evolution. –IED tactics and counter tactics. Biological evolution is generally slow. Thought: thinking through options is even faster. –Let one’s hypotheses die in one’s stead. —Karl Popper Thought: thinking through options is even faster. –Let one’s hypotheses die in one’s stead. —Karl Popper Simulation: computer modeling of evolutionary processes is faster yet.

9. 9 Application to engineering problems: Since it’s simulated it’s even faster than military evolution The Traveling Salesman Problem (TSP). Connect the cities with a tour that is a permutation of the cities. Starts and ends at the same city. Includes each city exactly once. In this case the problem is easy to solve by inspection. In general, it’s computationally explosive since there are (n-1)! possible tours. B B A A D D E E C C 20 13 12 14 12 7 9 4 24 The obvious tour will include the sequence ACED-54 (or its reverse). No diagonals. The question is where to put B: ABCED- 55, ACBED-57, or ACEBD-56? Why not n!

10. 10 An exchange (or reverse or mutation) solves this problem in one step. ACBED-57 → ABCED-55 Genetic algorithm approach Create a population of random tours. AEBCD-59, ACBED-57, ADCBE-59, ACDEB-71, … In this case there are only 4! = 24 possible tours. Could examine them all. Usually that’s not possible. Repeat until good enough or no improvement. But beware local optima. Select one or two tours as parents. −Ensure that better tours are more likely to be selected. Generate offspring using genetic operators to replace poorer elements. −Exchange two cities: ACDEB-71 → ACBED-57 −Reverse a subtour: ACBED-57 → AEBCD-59 −(Re)combine two tours: AEBCD-59 & ACBED-57 → AEDCB-71. Possibly mutate the result: ADCBE-59 → ACBDE-70 B B A A D D E E C C 20 13 12 14 12 7 9 4 24

11. 11 Try it out: TSP.jar After starting a run, double click in the display area to add a city or on a city to remove it. –New cities are added to the tour next to their nearest neighbor. Stop and restart for new random cities. –The number of new cities will be the same as the number of old cities. The differences between the current best and its predecessor are shown by link color. –New links are shown in green. –Removed links are in dashed magenta. No “geographical” heuristics are used. Just the structural ones shown on the previous slide.

12. 12 Genetic algorithms: parameter setting/tuning The number of variables is constant. –Both the TSP and the peppered moths examples illustrate genetic algorithms. Peppered moths: one parameter (color) to set. TSP: N variables. As a parameter setting problem think of each tour as consisting of N variables, each of which may contain any city number. The additional constraint is that no city may repeat. Often there are hundreds of variables (or more) or the search space is large and difficult to search for some other reason. There is no algorithmic way to find values that optimize (maximize/minimize) an objective function. Terrile et. al. (JPL), “Evolutionary Computation applied to the Tuning of MEMS gyroscopes,” GECCO, 2005. Abstract: We propose a tuning method for MEMS gyroscopes based on evolutionary computation to efficiently increase the sensitivity of MEMS gyroscopes through tuning and, furthermore, to find the optimally tuned configuration for this state of increased sensitivity. The tuning method was tested for the second generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of the frequency response of the MEMS device in open-loop operation.

13. 13 Genetic programming: design and analysis The number of variables (and the structure of the possible solution) is not fixed. Original goal was to generate software automatically. –Not very successful, but hence the name. Applied successfully to other design and analysis problems. –Circuit design –Lens design Bongard and Lipson (Cornel), “Automated reverse engineering of nonlinear dynamical systems,” PNAS, 2007. Abstract: Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. … “Symbolic regression”

14. 14 The Human-competitive awards: “Humies” Each year at the Genetic and Evolutionary Computing Conference (GECCO), prizes are awarded to systems that perform at human-competitive levels—including the previous two slides. –See http://www.genetic-programming.org/hc2005/main.html An automatically created result is considered “human-competitive” if it satisfies at least one of the eight criteria below. A.The result was patented as an invention in the past, is an improvement over a patented invention, or would qualify today as a patentable new invention. B.The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. C.The result is equal to or better than a result that was placed into a database or archive of results maintained by an internationally recognized panel of scientific experts. D.The result is publishable in its own right as a new scientific result — independent of the fact that the result was mechanically created. E.The result is equal to or better than the most recent human-created solution to a long-standing problem for which there has been a succession of increasingly better human-created solutions. F.The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. G.The result solves a problem of indisputable difficulty in its field. H.The result holds its own or wins a regulated competition involving human contestants (in the form of either live human players or human-written computer programs).

15. 15 Tom Lang: Genetic Algorithm for Constellation Optimization (GACO) Finds optimal constellation orbits using a genetic algorithm under multiple design constraints and with multiple sensor types. For low number of sats, GA arrangement is significantly better than Walker

16. 16 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Organizational innovation Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

17. 17 Innovative environments Net-centricity and the GIG Inspired by the web and the internet Goal: to bring the creativity of the web and the internet to the DoD What do innovative environments have in common? How can organizations become innovative? What do innovative environments have in common? How can organizations become innovative? Other innovative environments Market economies Biological evolution The scientific and technological research process

18. 18 The innovative process: exploratory behavior Innovation, including human creativity, is always the result of an evolutionary process. If I were to give an award for the single best idea anyone has ever had, I'd give it to Darwin, ahead of Newton and Einstein and everyone else. In a single stroke, the idea of evolution by natural selection unifies the realm of life, meaning, and purpose with the realm of space and time, cause and effect, mechanism and physical law. Daniel Dennett, Darwin's Dangerous Idea Generate new variants (e.g., ideas)—typically by combining and modifying existing ones. –This is a random process in nature. –But random or not isn’t the point. –The point is to generate lots of possibilities, to explore the landscape. (Select and) exploit the good ones –Allow/enable the good ones to flourish. The hard part! The easy part!

19. 19 Exploratory behavior in nature Evolution. E. Coli navigation. The immune system. Termite nest building. Ant and bee foraging. Building out the circulatory and nervous systems.

20. 20 Exploratory behavior: like water finding a way down hill Quite a challenge! We are very well defended. But we still get sick! If there is a way, some will inevitably find it. (Murphy's law?) The trick is to make the inevitability work for you, not against you. Microbes attempting to get into your body must first get past your skin and mucous membranes, which not only pose a physical barrier but are rich in scavenger cells and IgA antibodies. Next, they must elude a series of nonspecific defenses—and substances that attack all invaders regardless of the epitopes they carry. These include patrolling phagocytes, granulocytes, NK cells, and complement. Infectious agents that get past these nonspecific barriers must finally confront specific weapons tailored just for them. These include both antibodies and cytotoxic T cells. From a tutorial on the immune system from the National Cancer InstituteFrom a tutorial on the immune system from the National Cancer Institute.

21. 21 Exploratory behavior and asymmetric warfare It is the nature of complex systems and evolutionary processes that conflicts become asymmetric. No matter how well armored one is … there will always be chinks in the armor, … and something will inevitably find those chinks. The something that finds those chinks will by definition be asymmetric since it attacks the chinks and not the armor.

22. 22 Exploratory behavior: groups and individuals Exploratory behavior typically requires autonomous individuals to do the exploration. Much exploratory behavior is wasted effort. –Successful group exploratory behavior typically requires multiple, loosely coordinated, i.e., autonomous, individuals. One may hit the jackpot while the others drill dry holes.

23. 23 Exploratory behavior: groups and individuals For a group to benefit from the discoveries of individuals, there must be mechanisms that bring those discoveries back to the group and allow them to take root. –Mechanisms to internalize successful/promising discoveries must be built into a group’s process. –This frequently requires “creative destruction,” which may be more difficult to accept—especially if it’s your job that is being destroyed. –Markets are how we integrate creative destruction into society. Recall ant foraging and pheromone following. Joseph Schumpeter, Capitalism, Socialism, and Democracy It’s amazing how well we have tamed destruction. It’s now an accepted part of our normal processes.

24. 24 How does this apply to organizations? To ensure innovation: Sounds simple doesn’t it? Creation and trial Encourage the prolific generation and trial of new ideas. Establish the successful variants Allow new ideas to flourish or wither based on how well they do—rather than political reasons.

25. 25 Initial funding Prospect of failure ApprovalsEstablishment Biological evolution Capitalism in the small. Nature always experiments. Most are failures, which means death. (But no choice given.) None. Bottom-up resource allocation defines success. Entrepreneur Little needed for an Internet experiment. Perhaps some embarrassment, time, money; not much more. Few. Entrepreneur wants rewards. Bottom-up resource allocation. Bureaucracy Proposals, competition, forms, etc. When 100% Mission Success is the group goal, who wants a failure in his/her personnel file? Far too many. Managers have other priorities. Top-down resource allocation. New ideas aren’t the problem. Trying them out Innovation in various environments Getting good ideas established We save ourselves by spin-doctoring and benign neglect

26. 26 “Garages and laboratories, workbenches, and scribbled napkins are filled with brilliant ideas unmatched with determination, resources, and market sensibilities.” Jack Russo, Silicon Valley intellectual-property lawyer. In 1999, when Nathan Myhrvold left Microsoft (formerly CTO; brilliant, but he missed the importance of the web) he set himself an unusual goal. He wanted to see whether the kind of insight that leads to invention could be engineered. He formed a company called Intellectual Ventures. He raised hundreds of millions of dollars. He hired the smartest people he knew. It was not a venture-capital firm. –V–Venture capitalists fund existing insights. They let the magical process that generates new ideas take its course, and then they jump in. Myhrvold wanted to make insights—to come up with ideas, patent them, and then license them to interested companies. Malcolm Gladwell (May 12, 2008) “In the Air,” The New Yorker, http://www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell http://www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell Matt Richtel (March 30, 2008) “Edison...Wasn’t He the Guy Who Invented Everything?,” New York Times, http://www.nytimes.com/2008/03/30/weekinreview/30richtel.html http://www.nytimes.com/2008/03/30/weekinreview/30richtel.html

27. 27 Planned invention? When Myhrvold started out, his expectations were modest. Although he wanted insights like Alexander Graham Bell’s, Bell was clearly one in a million, a genius who went on to have ideas in an extraordinary number of areas—sound recording, flight, lasers, tetrahedral construction, and hydrofoil boats, to name a few. Invention has its own algorithm—some combination of genius, obsession, serendipity, and epiphany. How can you plan for that? The original expectation was that I.V. would file a hundred patents a year. It’s filing five hundred a year and has a backlog of three thousand ideas. It just licensed off a cluster of patents for $80,000,000. Its ideas are not trivial. –I–Improved jet engines –N–New techniques for making microchips –A–A way to custom-tailor the mesh “sleeve” used to repair aneurysms –A–Automatic, battery-powered glasses, with a tiny video camera that reads the image off the retina and adjusts the fluid-filled lenses accordingly, up to ten times a second. Why aren’t we doing this?

28. 28 Newton and Leibniz: calculus. No less than nine claimants: the telescope. At least six different inventors: the thermometer. Three mathematicians: invention of decimal fractions. Charles Darwin and Alfred Russel Wallace: evolution. Elisha Gray and Alexander Graham Bell: the telephone. John Napier, Henry Briggs, and Joost Bürgi: logarithms. Charles Cros and Louis Ducos du Hauron: color photography. Galileo, Scheiner, Fabricius, and Harriott: discovery of sunspots. Joseph Priestley and Carl Wilhelm Scheele: discovery of oxygen. Several individuals in England and in America: typewriting machines. Fulton, Jouffroy, Rumsey, Stevens, and Symmington: the steamboat. Édouard-Léon Scott de Martinville and Thomas Edison: the phonograph. Mayer, Joule, Thomson, Colding, and Helmholz: formulation of the conservation of energy. The history of science is full of ideas that several people had at the same time W. F. Ogburn & D. S. Thomas (March 1922) “Are inventions inevitable?” Political Science Quartly, 37, 83-98. Malcolm Gladwell (May 12, 2008) “In the Air,” The New Yorker, http://www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell http://www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell Matt Richtel (March 30, 2008) “Edison...Wasn’t He the Guy Who Invented Everything?,” New York Times, http://www.nytimes.com/2008/03/30/weekinreview/30richtel.html Invention does not require genius. Genius is just more efficient invention.

29. 29 Simplified model of successful innovative organizations Lower levels discover opportunities through exploratory behavior. –New initiatives often grow from the “edges,” where perception occurs. –Constrained by “rules of engagement,” which protect them from harm. –Must be possible for initiatives to originate at all levels—even the top. Higher/broader levels provide perspective, impose constraints, shape direction, and add or withhold resources as events develop. –They do not primarily issue commands. This is primarily a bottom-up model of resource allocation. –Decisions about increasingly significant commitments are made at increasingly higher/broader levels. If the entire organism/organization commits, becomes an organization/organism-level goal. The top-level strategy should be to stay healthy and build skills, resources, and capabilities that can be recruited/applied/committed when needed. Lower levels discover opportunities through exploratory behavior. –New initiatives often grow from the “edges,” where perception occurs. –Constrained by “rules of engagement,” which protect them from harm. –Must be possible for initiatives to originate at all levels—even the top. Higher/broader levels provide perspective, impose constraints, shape direction, and add or withhold resources as events develop. –They do not primarily issue commands. This is primarily a bottom-up model of resource allocation. –Decisions about increasingly significant commitments are made at increasingly higher/broader levels. If the entire organism/organization commits, becomes an organization/organism-level goal. The top-level strategy should be to stay healthy and build skills, resources, and capabilities that can be recruited/applied/committed when needed. Just what your mother always told you: eat right, exercise, get plenty of sleep, study hard, practice, and save money.

30. 30 Practical organizational innovation Hamel and Skarzynski: an innovation architecture. An innovation pipeline for managing and opportunities A core set of people trained in the processes of innovation A systematic process for generating and managing strategic insights The right evaluative criteria at every stage of the development process to prevent potentially valuable ideas from being killed off prematurely Ideas that are sufficiently radical to deliver breakthroughs Mechanisms for rapidly reallocating resources behind new opportunities Mechanisms to manage growth opportunities with different timescales and risk profiles Metrics to measure innovation performance Linkages between innovation and management compensation A self-sustaining enterprise capability and a tangible core value Prediction (20 years). To survive outside a protected environment an organization will need a successfully functioning innovation architecture. Corollary. Some organizations will focus on preserving their environments. Do we have an innovation architecture? Most companies want to make money. What metrics would we use?

31. 31 Innovation in the military Our military is deliberately mission driven—where the missions are determined by civilian authority. We don’t want our military to take the initiative to find new missions for itself. What kinds of innovation does it make sense for the military to attempt? –Innovation that make it more effective at doing what it is charged to do. –It isn’t clear how success can be made self-defining in the same way as making money or reproducing is self-defining. Need a way to aggregate resources/success bottom-up. –Establish a military-specific innovation architecture. Our military is deliberately mission driven—where the missions are determined by civilian authority. We don’t want our military to take the initiative to find new missions for itself. What kinds of innovation does it make sense for the military to attempt? –Innovation that make it more effective at doing what it is charged to do. –It isn’t clear how success can be made self-defining in the same way as making money or reproducing is self-defining. Need a way to aggregate resources/success bottom-up. –Establish a military-specific innovation architecture.

32. 32 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Platforms: stigmergy, distributed control, and systems of systems Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

33. 33 The basic contrast Hierarchical control and functional decomposition Agent interaction and stigmergic/platform design Independent components, which perform their functions without interacting and produce results to be used at the next higher level. A tree structure. –Each node a potential single point of failure. Focus is on component functionality. Independent components, which interact both directly and indirectly through an environment (platform). The platform tends to be distributed. Focus on communication mechanisms. The more useful the platform, the more productive the system. –Telephone vs. Internet. Both are platforms. The Internet is more powerful.

34. 34 How would you gather wood chips into a pile? Probably not like this. File > Models Library > Biology > Termites Click Open

35. 35 Termite rules Wander about aimlessly (randomly) until you bump into a wood chip. –If you are not holding a wood chip Pick up the new chip. Move away from your current location. Go back to wandering about aimlessly. –If you are holding a wood chip Put down your chip in a nearby empty space. Move away from your current location. Go back to wandering about aimlessly. Net effect: wood chips are deposited near other wood chips, eventually forming a single pile. Wikipedia commons Run the program and watch what happens. Exercise: prove that this will always happen

36. 36 No horizontal communication. No dashed lines. (Is that good?) It’s not accurate as a communication or operational structure. It may represent how authority is delegated, and it may represent how responsibility is assigned, but it doesn’t represent how communication occurs or how organizations really work. Downward pointing arrows: commands. Upward pointing arrows: results/reports. Can be implemented with point-to-point communication links. Organizational/system structure: What’s wrong with this picture? Functional decomposition Recall Wanda Austin’s warning not to combine small stovepipes into large stovepipes. Functional decomposition Recall Wanda Austin’s warning not to combine small stovepipes into large stovepipes.

37. 37 What is a System of Systems? Small stovepipes to large stovepipes – NO Loosely coupled and tightly integrated – YES 37 Dr. Austin Platforms

38. 38 A somewhat more realistic picture The focus is on interaction among participants in the organization. David Sloan Wilson, Evolution for Everyone Everything is both an entity and a group.

39. 39 From point-to-point links to platforms Need more than fixed point-to- point communication channels The communication system (even if just a telephone system) is the start of net-centricity Must distinguish between communication structure and command hierarchy. Becomes reified as an additional component—not just a collection of interfaces. “ Platform ” But a network/platform may do nothing on its own. The fundamental question How will the organization use the network/platform? Enabling communication neither eliminates responsibility nor undermines command intent. As a common resource, how does it fit into the hierarchy? How is it governed?

40. 40 Layered architectures — not functional decomposition Presentation Session Transport Network Physical WWW (HTML) — browsers + servers Applications, e.g., email, IM, Wikipedia Asymmetric warfare Each layer is a platform that a)is built on the layers below it b)enables higher level layers to be built on top of it c)is vulnerable to disruption.

41. 41 How does Aerospace send mail to Aerospace? El Segundo mail routes (carts) Chantilly mail routes (carts) LAX ↔ IAD (commercial aircraft) Aerospace USPS (trucks) Sort & route Many interlinked processes. The various systems provide platforms for each other. Infrastructure A system of (many!) systems. Aerospace, USPS, commercial airlines, airports, traffic, road maintenance, … A system of (many!) systems. Aerospace, USPS, commercial airlines, airports, traffic, road maintenance, …

42. 42 Multi-sided software platforms Evans, Hagiu, and Schmalensee (2006) Invisible Engines: How Software Platforms Drive Innovation and Transform Industries, MIT Press. (freely downloadable) –Operating systems and game consoles. More examples: shopping centers, dating websites, TV channels, TV talk shows, Amazon resellers, telephone & telegraph systems. The web browser: web sites and surfers. Markets that connect disparate groups. –A stock exchange matches buyers and sellers. –A credit card system matches merchants and cardholders. Not your usual business model: buy; add value; sell. The value to each group increases as the size of the other group(s) grow. (Also known as network effect.)

43. 43 Platforms as refactorings A multi-sided platform may be understood as the standardization and factoring out (refactoring) of a hard part of an interaction and providing it as a service. The hard part is done by the platform.  USPS: sending & receiving materials.  Credit card: paying and being paid.  Dating service: finding the other party and making an initial contact.  Robert’s Rules of Order : the interaction protocol

44. 44 Standards as (ephemeral) platforms Since a platform is a level of abstraction, it can be characterized by a specification. The specification can then serve as the definition of the platform, e.g., HTML, SQL, …. Multiple vendors can be encouraged to compete to implement it. Defangs platform owners Favors platform users.

45. 45 Platforms as infrastructure and environments Sometimes platforms define an environment. The free market economic system is defined primarily by two platforms. –The monetary and banking system. Factors out the economic notion of value. Allows value to be abstracted, stored, exchanged with minimal overhead. –The legal and judicial system. Factors out agreements (contracts) and enforcement mechanisms. Overhead not so minimal (lawyers) — but better than hiring your own “enforcers.” Used to rely more on reputation. Still do in eBay. Much too important to be controlled privately. In general, the set of platforms available in an environment is the environment’s infrastructure.

46. 46 An unusual platform-based design E. coli has genes to produce lactase, which digests lactose. But for efficiency sake it should produce lactase only when lactose is present. Imagine that you were asked to design a system that would produce a product only under certain conditions. How would you do it?

47. 47 A (quasi-top-down) functional analysis solution Lactose sensor How does one know one can build these pieces? What enables the interfaces? What holds it all together? The unasked questions Lactase production system Switch Off On Is it really top-down?

48. 48 E. coli lets lactose flip its own switch Lactose Repressor Lactose itself binds to the repressor, pulling it out of the way. lacYlacAlacX RNAP Repressor Three lac genes RNA polymerase can’t bind to DNA. Transcription blocked. lacYlacAlacX RNAP lac operon It’s often said that a first step in systems engineering is to agree on the system boundaries. What are the system boundaries in this case? It’s often said that a first step in systems engineering is to agree on the system boundaries. What are the system boundaries in this case? See movie http://pages.csam.montclair.edu/~smalley/LacOperon.mov.http://pages.csam.montclair.edu/~smalley/LacOperon.mov Lactose Repressor lacYlacAlacX RNA polymerase can now bind to DNA. Transcription enabled. The genes are expressed. RNAP Where’s the platform? The DNA → protein processing system. Like the Aerospace- delivers-mail example.

49. 49 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Organizations: when groups are better than individuals Innovation required individual autonomy. What do groups add? Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

50. 50 “Self-organizing” groups Craig Reynolds wrote the first flocking program two decades ago: http://www.red3d.com/cwr/boids. http://www.red3d.com/cwr/boids Here’s a good current interactive version: http://www.lalena.com/AI/Flock/ http://www.lalena.com/AI/Flock/ –Separation: Steer to avoid crowding birds of the same color. –Alignment: Steer towards the average heading of birds of the same color. –Cohesion: Steer to move toward the average position of birds of the same color.

51. 51 “Self-organizing” groups Debora Gordon on ant colonies Debora Gordon on ant coloniesThe bird, termite, and ant models illustrate emergence (and multi-scalarity). (See video Debora Gordon on ant colonies.)Debora Gordon on ant colonies In both cases, individual, local, low-level rules enabled “the group” to achieve “emergent” higher level results. –The birds flocked. –The wood chips were gathered into a single pile. –The food was brought to the nest. Exploratory behavior extends the perceptual reach of any individual. Emergence is successful group design.

52. 52 Breeding groups Chickens are fiercely competitive for food and water. Commercial birds are beak-trimmed to reduce cannibalization. Breeding individual chickens to yield more eggs compounds the problem. Chickens that produce more eggs are more competitive. Instead Muir bred chickens by groups. At the end of the experiment Muir's birds' mortality rate was 1/20 that of the control group. His chickens produced three percent more eggs per chicken and (because of the reduced mortality) 45% more eggs per group. Group (and more generally multi-level) selection is now accepted as valid. Traditional evolutionary theory says there is no such thing as group selection, only individual selection. Bill Muir (Purdue) demonstrated that was wrong. Wikipedia commons http://www.ansc.purdue.edu/faculty/muir_r.htm Groups are entities. You and I are both entities and cell colonies.

53. 53 But then groups found that coordination, specialization, and coordinated specialization enabled emergence. –Consider any multi-cellular organism, or any organism with multiple organs, or any society with any sort of specialization, or any social grouping with coordinated and/or specialized roles. –These groups exemplify real emergence. Entirely new capabilities appear. Wind instruments can play melodies. Piano and guitar can play chords as well. Why groups? Perhaps groups formed initially because they increased survival value. A team will generally beat an individual of approximately the same skill level. –This is not so much emergence as power in numbers. Why groups? Two steps.

54. 54 David Sloan Wilson on groups What holds for chickens holds for other groups as well: teams, military units, corporations, religious communities, cultures, tribes, countries. Successful groups are those that minimize within-group conflict and organize to succeed at between-group conflict. Groups with mechanisms for working together can often accomplish far more (emergence) than the sum of the individuals working separately. –Corporations, military organizations; reproduction; mitochondria and “us.” But if a group good is also an individual good (e.g., money, security), the group must have mechanisms to limit cheating (free-ridership). Group traits (although they are carried as rules by individuals) evolve because they benefit the group. (E.g., insect behavior.) These traits may be transmitted genetically (by DNA). They may also be transmitted culturally (by training/parenting/indoctrination/mentoring/…). –Human groups are much more complex because it’s not all built-in. Moral systems are interlocking sets of values, practices, institutions, and evolved psychological mechanisms that work together to suppress or regulate selfishness and make social life possible. —Jonathan Haidt We evolved to be pro-social within groups but xenophobic between groups. – Michael Shermer

55. 55 Experimental “games” Prisoner’s Dilemma. –One shot. Defect is the only rational strategy. –Iterated. Tit-for-tat: Cooperate initially and then copy the other guy. Pavlov: repeat on success; change on failure. (More robust.) Ultimatum Game. Proposer must offer to divide $100—e.g., from TAI. Responder either accepts the proposed division or rejects it—in which case neither gets anything. –Only rational strategy: proposer offers as little as possible; responder always accepts. –Real experiments (world-wide). Responder rejects unless offer ~1/3. –Some societies are different, e.g., where giving a gift means power. –What would you offer/accept? Try it. (Played anonymously. Write offer.) Try it table against table. Each table prepares an offer. CD C3/30/5 D5/01/1 A far-from-equilibrium system. New energy is supplied “for free.”

56. 56 Homo economicus vs. strong reciprocity Homo economicus: individual selection Agents care only about the outcome of an economic interaction and not about the process through which this outcome is attained (e.g., bargaining, coercion, chance, voluntary transfer). Agents care only about what they personally gain and lose through an interaction and not what other agents gain or lose (or the nature of these other agents’ intentions). Except for sacrifice on behalf of kin, what appears to be altruism (personal sacrifice on behalf of others) is really just long-run material self-interest. Ethics, morality, human conduct, and the human psyche are to be understood only if societies are seen as collections of individuals seeking their own self-interest. Moral Sentiments and Material Interests: The Foundations of Cooperation in Economic Life Herbert Gintis, Samuel Bowles, Robert T. Boyd, and Ernst Fehr (eds), MIT Press, 2005.

57. 57 Homo economicus vs. strong reciprocity Strong reciprocity: group selection A predisposition to cooperate with others, and to punish (at personal cost, if necessary) those who violate the norms of cooperation –even when it is implausible to expect that these costs will be recovered at a later date. Strong reciprocators are both conditional cooperators They behave altruistically as long as others are doing so as well. and altruistic punishers They apply sanctions to those who behave unfairly even at a cost to themselves. Socialization: norm internalization. There's no such thing in biology, economics, political science, or anthropology. Humans can want things even when they are costly to ourselves because we were socialized to want them to be fair, to share, to help your group, to be patriotic, to be honest, to be trustworthy, to be cheerful.

58. 58 Wise crowds: more than the sum of their parts Web wise crowd platforms Wikis Mailing lists Chat rooms Prediction markets Condorcet Jury Theorem (18 th century) example Five people (a small crowd). Each person has a 75% chance of being right. Probability that the majority will be right: ~90% With 10 people: ~98%. Simple if you think about it. Traditional wise crowds Teams Juries Democratic voting

59. 59 Wise crowd criteria Diverse: different skills and information brought to the table. Decentralized and with independent participants: No one at the top dictates the crowd's answer. Each person is free to speak his/her own mind and make own decision. Distillation mechanism: to extract the essence of the crowd's wisdom. Participant autonomy. James Surowiecki, The Wisdom of Crowds

60. 60 Example from The Difference Which person from the following list was not a member of the Monkees (a 1960s pop band)? (A) Peter Tork (B) Davy Jones (C) Roger Noll (D) Michael Nesmith Imagine a crowd of 100 people with knowledge distributed as follows: 7 know all 3 of the Monkees 10 know 2 of the Monkees 15 know 1 of the Monkees 68 have no clue In other words, less than 10 percent of the crowd knows the answer, and over two- thirds are culturally deprived of any Monkees knowledge. We assume individuals without the answer vote randomly. The Condorcet Jury Theorem, then, doesn’t apply because only a small minority knows the answer. Still, the crowd will have no problem getting the right answer. The 7 who know all the Monkees vote for X; 5 of the 10 who know 2 of the Monkees will vote for X; 5 of the 15 who know 1 of the Monkees will vote for X; and 17 of the 68 clueless will vote for Noll. So X will garner 34 votes, versus 22 votes for each of the other choices. Diverse groups of problem solvers outperformed the groups of the best individuals at solving problems. The diverse groups got stuck less often than the smart individuals, who tended to think similarly.

61. 61 A wise crowd as assistant and companion

62. 62 Distillation mechanism: prediction markets Statement Statement Statement issued by 25 world-famous academics. May 2007. Including: Kenneth Arrow, Daniel Kahneman, Thomas Schelling, Robert Shiller, Cass Sunstein. Abstract: Prediction markets are markets for contracts that yield payments based on the outcome of an uncertain future event, such as a presidential election. Using these markets as forecasting tools could substantially improve decision making in the private and public sectors. We argue that U.S. regulators should lower barriers to the creation and design of prediction markets by creating a safe harbor for certain types of small stakes markets. We believe our proposed change has the potential to stimulate innovation in the design and use of prediction markets throughout the economy, and in the process to provide information that will benefit the private sector and government alike.

63. 63 Often Beats Alternatives Vs. Public Opinion –I.E.M. beat presidential election polls 451/596 (Berg et al ‘01) –Re NFL, beat ave., rank 7 vs. 39 of 1947 (Pennock et al ’04) Vs. Public Experts –Racetrack odds beat weighed track experts (Figlewski ‘79) If anything, track odds weigh experts too much! –OJ futures improve weather forecast (Roll ‘84) –Stocks beat Challenger panel (Maloney & Mulherin ‘03) –Gas demand markets beat experts (Spencer ‘04) –Econ stat markets beat experts 2/3 (Wolfers & Zitzewitz ‘04) Vs. Private Experts –HP market beat official forecast 6/8 (Plott ‘00) –Eli Lily markets beat official 6/9 (Servan-Schreiber ’05) –Microsoft project markets beat managers (Proebsting ’05) from Robin HansonRobin Hanson

64. 64 Prediction markets Contracts: Intrade (Ireland-based): real money or play money.real moneyplay money Panos Ipeirotis But, there is evidence that prediction markets are not efficient.prediction markets are not efficient Slate’s Election Market Page Other Intrade contracts: Current Events > Google Lunar X PrizeGoogle Lunar X Prize Split off from TradeSports Land a privately funded robotic rover on the Moon that is capable of completing several mission objectives, including roaming the lunar surface for at least 500 meters and sending video, images and data back to the Earth.

65. 65 Concerns and Myths Self-defeating prophecies Decision selection bias Price manipulation Rich more “votes” Inform “enemies” Share less info Combinatorics Risk distortion Moral hazard Alarm public Embezzle Bubbles Bozos Lies Crowds don’t always beat experts. People will not work for trinkets. High accuracy is not assured. from Robin HansonRobin Hanson

66. 66 Other distillation mechanisms: making the crowd’s “wisdom” “actionable” Elections, polls, etc. Traditional. Many possible processes, e.g., transferrable ballots, etc. –Expression of preferences. –Many online options (and more options).options Collaboration: wikis and other collaboration tools (shared spaces), mailing lists, chat rooms, etc. –Explicit: Generation of new “work products.” Here’s a (long!) list of collaborative work environments.collaborative work environments –Implicit: Google’s page rank, “reputations” (e.g., eBay), “recommendation engines” (e.g., Amazon) A hard problem. Yet evolution and markets do it automatically.

67. 67 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Remember this Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

68. 68 Complex systems Multiple interacting elements can create a new entity, which has new properties. –That’s emergence. –Everything is both an entity and a group. Geostationary satellite. You and I are both entities and cell colonies. Interaction occurs through the environment. –When the environment provides useful functionality to facilitate that interaction, it’s a platform. Evolutionary processes are unavoidable and are also the source of all creativity. –Their essence is exploratory behavior and establishing good results. –Innovation can be planned for. Human groups are especially powerful. –We can learn to function in groups because we evolved to function in groups. –It’s still a problem of how best to distill a group’s wisdom as action. A tiny dew drop can reflect the entire moon.

69. 69 Backups

70. 70 Market mechanisms Intrade uses a continual double auction—bid and asked prices. (Like stocks.)Intrade –Requires high liquidity or a market maker. –Aggregates information in price; can buy or sell any time. Pari-Mutual. Losing bets distributed to winning betters. (Like horse racing). –Requires neither liquidity nor a market maker. –Aggregates information as odds. Can’t trade. Prices don’t vary. No profit in being right early. Best strategy is to wait until the last minute. But that reduces the amount of information supplied to the pool. – kahst.kahst Market Scoring Rules (Robin Hanson) and Dynamic Pari-Mutuel Market (David M. Pennock & Mike Dooley).Dynamic Pari-Mutuel Market –Combines pari-mutuel with continual double auction. –Benefit for being right early. –MSR: Inkling, Qmarkets; DPM: Yahoo! Tech Buzz Game.InklingQmarketsYahoo! Tech Buzz Game List of markets: MidasOracle.org.MidasOracle.org

71. 71 Stem cells instead of cancer Organisms are just a bunch of cells. If you understand the conditions under which they cooperate, you can understand the conditions under which cooperation breaks down. Cancer is a breakdown of cooperation. When cells reach the point where they divide constantly, they are cancer cells. Instead multi- cellular organisms use a seemingly inefficient process to replace lost cells. An organ such as the skin calls upon skin-specific stem cells to produce intermediate cells that in turn produce skin cells. Although great at their job, the new skin cells are evolutionary dead ends. They cannot reproduce. Losing the ability to reproduce was part of the evolutionary path single-celled organisms had to take to become multi-cellular. What was in it for the single cells? They got to be part of something more powerful. Something that was hard to eat and good at eating other things. John W. Pepper, University of Arizona Animal Cell Differentiation Patterns Suppress Somatic EvolutionAnimal Cell Differentiation Patterns Suppress Somatic Evolution, PLoS Computational Biology Vol. 3, No. 12, (12/2007) If cells reproduce by simply making carbon-copies of themselves, their descendants are more likely to accumulate mutations. Suppressing mutations that might fuel uncontrolled growth of cells would be particularly important for larger organisms that had long lives

72. 72 Group/system-level emergence Debora Gordon on ant colonies Debora Gordon on ant coloniesBoth the termite and ant models illustrate emergence (and multi- scalarity). (See video Debora Gordon on ant colonies.)Debora Gordon on ant colonies In both cases, individual, local, low-level rules and interactions produce “emergent” higher level results. –The wood chips were gathered into a single pile. –The food was brought to the nest. Emergence in ant and termite colonies may seem different from emergence in E. coli following a nutrient gradient because we see ant and termite colonies as groups of agents and E. coli as a single entity. But emergence as a phenomenon is the same. In both cases we can explain the design of the system, i.e., how the system works. In the ant/termite examples, the colony is the system. In the case of E. coli, the organism is the system. In Evolution for Everyone, David Sloan Wilson argues that all biological and social elements are best understood as both groups and entities. You and I are each (a) entities and (b) cell colonies. In Evolution for Everyone, David Sloan Wilson argues that all biological and social elements are best understood as both groups and entities. You and I are each (a) entities and (b) cell colonies.

73. 73 The Public Goods Game Contributions to a common pot grow—via emergence. The result is divided among everyone, even free-riders. Free riders do better than cooperators/contributors. But then cooperation (and public goods) will vanish. Punishment is important in sustaining cooperation. But how can punishment emerge if it is costly? Categories of players Loners do not participate; they neither contribute nor benefit. Defectors do not contribute but benefit. Cooperators contribute and benefit but do not punish. Punishers are contributors who also (pay to) punish defectors and simple cooperators—to prevent simple cooperators from free-riding on punishers. Which category dominates depends on modeling assumptions. Hannelore Brandt, Christoph Hauert, and Karl Sigmund, “Punishing and abstaining for public goods,” PNAS, Jan 10, 2006. http://www.pnas.org/cgi/reprint/103/2/495Karl Sigmundhttp://www.pnas.org/cgi/reprint/103/2/495 Games of Life

74. 74 We’re smart because we are “programmable,” i.e., able to learn—both information and norms As humans we’re successful because we’re smart. We’re smart because we operate in complex groups. We can operate in complex groups because we have strong reciprocity. We both share and are willing to punish non-sharers. Take bees. You always think of the hive as the big social collective. Not true. Workers often try to lay eggs, even though only the queen is supposed to lay eggs. If workers lay eggs, other workers run around, eat the eggs, and then punish the workers that laid the eggs. Wherever you find cooperation, you’ll also find punishment. Think of your own body. Each cell has its own self-interest to multiply. Why don’t they go berserk (cancer)? How do you get cells to cooperate? You punish cells that don’t cooperate. Socialization: norm internalization. There's no such thing in biology, economics, political science, or anthropology. Humans can want things even when they are costly to ourselves because we were socialized to want them to be fair, to share, to help your group, to be patriotic, to be honest, to be trustworthy, to be cheerful. What does it mean to say that we can learn? The word may sound cold and robotic, but it means that we are “programmable,” i.e., capable of internalizing new skills and ideas. Socialization is a form of learning. Clearly fundamental. How are we autonomous? Herbert Gintis Next slide

75. 75 Open source platforms If your business depended on a platform, would you want that platform owned (and controlled) by some other business? Open source software. Two categories: money driven and other. Iansiti and Richards, http://www.hbs.edu/research/pdf/07-028.pdf Money-driven tend to be multi-sided platforms. Top two in terms of corporate support: Linux and Firefox.

76. 76 Platform operational structure User Implementation Robert’s Rules of Order Telephone network Market economy InternetUSPS Presiding officer/parliamentarian Switching circuits Banks, courts, … TCP/IP Mail delivery processes The interests of platform owners (maximize profit) are not always the same as those of platform users. Some platforms are so important we don’t want the owner’s interests to take priority. Publicly owned and operated (or highly regulated) multi-sided platforms: –USPS. (So important it’s in the constitution.) –Telephone system.

77. 77 Natural language: the crown jewel of multi-sided platforms. Open at the top: virtually unlimited creativity. Open at the bottom: we each build our own implementations. Continually (but slowly) changing. –It’s a standard. We have dictionaries and grammar books for “standard English.” We all implement our versions of the standard. –It’s open source. It is continually undergoing modification — but slowly enough so that most of us can keep up.

78. 78 Infrastructure platforms The interests of platform owners (maximize profit) are not always the same as those of platform users. Some platforms are so important we don’t want the owner’s interests to take priority. Publicly owned and operated (or highly regulated) multi-sided platforms: –USPS. (So important it’s in the constitution.) –Telephone system. –FDA — an artificial platform imposed between seller and buyer.

79. 79 Autonomy, hierarchy, and common resources People who interact on the web are autonomous. Website developers are autonomous. Systems (such as the militaries of the NATO countries) are autonomous. These are all systems of systems. How do they communicate? Through common resources, that serve as their environment. Contrast this with the top-down structure of most military and business organizations. They often have internal communication channels.

80. 80 Complexity typically involves multiple autonomous components Poincare showed a century ago that the three-body gravitational problem has no general solution. Distributed software is the most difficult to write. –Making effective use of multi-core computers will be the computing challenge of the next decade. Stigmergy: an (often implicit) organizational strategy in which system components communicate by modifying their environment. E.g., ants, termites, and systems that interact through a database. All this contrasts with monolithic top-down control. Wikipedia commons When multiple autonomous entities interact within an environment, one often gets emergent results. Systems of that sort tend to be called complex. } Imagine having to program the Game of Life. Derogatory terms in the complex systems world

81. 81 It’s all platforms Telephone system Voicemail Television infrastructure Television channel Talk show Internet Email Interest groups WWW WikipediaGoogle mapsCraig’s listeBay store Mashups SOA framework Service TV show Telemarketing Service Television channel Talk show Politics Civil society Courts: dispute resolutionMoney and banking system Free market economic system Other infrastructure elements Product Service … Movie marketingPolitics

82. 82 Two- (and multi-) sided markets Markets that connect disparate groups. –A stock exchange matches buyers and sellers. –A credit card system matches merchants and cardholders. Not your usual business model: buy; add value; sell. The value to each group increases as the size of the other group(s) grow. (Also known as network effect.) By providing services to two groups of users one can sell access to one group to the other. –A TV station matches creators of programming and viewers, access to which it then sells to advertisers. Google! Sometimes both groups can be charged — some credit cards, classified ads. –Often the seller pays (eBay). –In The Recycler ads are free; the buyers pay. 2001Institut d'Economie Industrielle (IDEI). Jean-Charles Rochet and Jean Tirole (2001) Institut d'Economie Industrielle (IDEI).

83. 83 Platforms as refactorings Think about the abstractions (data types and operations on objects of those types) platforms offer and what implements the abstractions. –Emergence is a level of abstraction. Databases: store, modify, and retrieve tabular data — or linked text as in Wikipedia. –Each platform is a single shared implementation. File extensions for shared files, e.g.,.doc,.xl. –Each user has his/her own implementation.

84. 84 Level of abstraction Example: PowerPoint classes and operations: –Slide: insert objects (such as text boxes); position, align, and distribute objects; animate objects; –Master Slide: associate with slide collection –Slide collection (file): arrange slide order –Text box: insert/edit text; change background and outline color; Sometimes called a conceptual model What the system consists of. What those things can do. How they can be operated on. A collection of classes (categories) and operations that can be performed on instances of those classes.

85. 85 File extensions for shared files (.doc,.xl) Each user has his/her own implementation. User Implementation Xyz.doc Representation of internal state for this transaction. E.g., MS Word Requires import/export capability

86. 86

87. 87 Reading the code: the Procedures tab to go search-for-chip find-new-pile put-down-chip end to search-for-chip ifelse pcolor = yellow [ set pcolor black set color orange fd 20 ] [ wiggle search-for-chip ] end to find-new-pile if pcolor != yellow [ wiggle find-new-pile ] end to put-down-chip ifelse pcolor = black [ set pcolor yellow set color white get-away ] [ rt random 360 fd 1 put-down-chip ] end to get-away rt random 360 fd 20 if pcolor != black [ get-away ] end to wiggle fd 1 rt random 50 lt random 50 end

88. 88 Platform service provider Governance: platforms are not like most businesses Not a typical business product or service. Does not combine components from suppliers to make and sell a product for consumers. Enables interaction. Value depends on breadth of use. –Often called a network effect. Examples Internet – WWW – GIG. A credit card service. A shopping center. A dating service. Whoever owns/runs/controls it, has users at their mercy. Platform Governance of common resources becomes a central issue. Multi-sided platform A means, mechanism, or set of conventions that structure and enable interaction among parties. Owner’s and users’ priorities may not be compatible.