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 is “evolution by natural (i.e., environmental) selection.” –Darwin likened it to breeding. The environment plays the rules 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. 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. 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 The nature of evolution. Four time scales Nature is not “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. Nature is not “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. Social/economic 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 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 do not 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 do not 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.

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. Innovation: generalized evolution and environmentally based resource allocation. 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 How do they find the open pathways? It’s not “invaders” vs. “defenders.” Through (evolutionary) exploratory behavior, if there is a way, some will inevitably find it. How do they find the open pathways? It’s not “invaders” vs. “defenders.” Through (evolutionary) exploratory behavior, if there is a way, some will inevitably find it. Quite a challenge! We are very well defended. But we still get sick! Some “invaders” will make it past these defenses. The problem is not even that some get through, it’s that they exploit their success. Innovative organizations make that inevitability work in their favor. Innovation is the (disruptive) invader not the defender. 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: recall evolutionary processes How can the human genome, with fewer than 25,000 genes –fill in all the details of the circulatory and nervous systems? –produce a brain with trillions of cells and synaptic connections? Cell growth followed by species-specific die-off produces webbing in duck feet and bat wings but not in human fingers. Military strategy of “probing for weakness.” Ant and bee foraging: group is a perceptual organ for individuals. Scientific research. Corporate strategy of seeking (or creating) marketing niches. The general mechanism is: Prolifically generate a wide range of possibilities Establish connections to new sources of value in the environment. The general mechanism is: Prolifically generate a wide range of possibilities Establish connections to new sources of value in the environment. Mechanism generation Function explore Purpose use result Bottom up

22. 22 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.

23. 23 Exploratory behavior in humans and society As processes carried out by individuals –Individual mental trial and error exploration. –Modeling and simulation. As processes carried out by groups –Market economies. (A speeded up form of evolution.) –The scientific and technological research process. These give us enormous leverage and speed-up over traditional evolution. It’s better to let one’s hypotheses die in one’s stead. —Karl Popper, source uncertain. It’s better to let one’s hypotheses die in one’s stead. —Karl Popper, source uncertain.

24. 24 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. 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 is now an accepted part of our normal processes.

25. 25 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.

26. 26 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

27. 27 “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

28. 28 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. Malcolm Gladwell (May 12, 2008) “In the Air,” The New Yorker, http://www.newyorker.com/reporting/2008/05/12/080512fa_fact_gladwell

29. 29 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 efficient invention.

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. Within 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.

31. 31 Simplified model of successful organisms/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 entity-level goal. The top-level organism/organizational goal should be to stay healthy and to 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 entity-level goal. The top-level organism/organizational goal should be to stay healthy and to 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.

32. 32 C2: 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 initiatives does it make sense for the military to take? –Initiatives that further already agreed upon missions—or derived missions. I.e., internal initiatives 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 initiatives does it make sense for the military to take? –Initiatives that further already agreed upon missions—or derived missions. I.e., internal initiatives 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.