1. 1 Does nature really think? Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. Course overview: part 1 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. Overview – unintended consequences, mechanism, function, and purpose; levels of abstraction, emergence, introduction to NetLogo. 9:25–9:45. Emergence, levels of abstraction, and the reductionist blind spot. 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 – exploration and exploitation. 10:30–10:45. Platforms – distributed control and systems of systems. 10:45–10:55. Groups – how nature builds systems; the wisdom of crowds. 10:55–11:00. Summary/conclusions – remember this if nothing else. Lots of echoes and repeated themes from one section to another.

3. 3 Introduction to Complex Systems: How to think like nature  1998-2007. The Aerospace Corporation. All Rights Reserved. What “complex systems” means, and why you should care. Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org

4. 4 But if I had to define what a “complex system” is … –A collection of autonomous elements that interact both with each other and with their environment and that exhibits aggregate, ensemble, macro behaviors that none of the elements exhibit individually. But if I had to define what a “complex system” is … –A collection of autonomous elements that interact both with each other and with their environment and that exhibits aggregate, ensemble, macro behaviors that none of the elements exhibit individually. What we will be talking about. “Complex systems” refers to an anti-reductionist way of thinking that developed in the 1980s in Biology, Computer Science, Economics, Physics, and other fields. (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.” 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 building “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.

5. 5 A satellite in a geostationary orbit: one of the simplest possible “complex systems” But nothing is tying it down. No cable is holding it in place. Wikipedia commons period of the orbit = period of the earth’s rotation Typical of complex system mechanisms. Multiple independent or quasi-independent processes — which are not directly connected causally (agents) — interact within an environment to produce a result. Typical of complex system mechanisms. Multiple independent or quasi-independent processes — which are not directly connected causally (agents) — interact within an environment to produce a result. Fixed with respect to the earth as a reference frame. An “emergent” property What is the environment?

6. 6 Complex systems terms Emergence. A level of abstraction that can be described independently of its implementation. 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.

7. 7 Why should you care? Because our corporate leadership, our customers, and their contractors think it’s important. –Rumsfeld’s inspiration for transformation in the military grew out of this way of thinking. This field is not new. It’s at least 2 decades old. –The Command and Control Research Program (CCRP) in the Pentagon (Dave Alberts) is successfully promoting this style of thinking within the DoD. –Complex systems thinking is a generalization of and the foundation for net- centric thinking —and the way the world has changed as a result of the web. You should understand what they are talking about –So that you can explain it to them. Because it offers a powerful new way to think about how systems work. Because large systems—and especially systems of systems (another important buzz-word)—tend to be complex in the ways we will discuss. Because the ideas are interesting, important, and good for you. See next few slides

8. 8 General Hamel and Dr. Austin think it’s important General Michael Hamel (moderator) –“Where Commercial, Civil, and Military Space Intersect: Cooperation, Conflict, and Execution of the Mission” Plenary Session, AIAA Space 2007. Dr. Wanda Austin, –“Space System of Systems Engineering” USC Center for Systems and Software Engineering Convocation, October 2006.

9. 9 The process of planning, analyzing, organizing, and integrating the capability of a mix of existing and new systems into a system-of-system capability that is greater than the sum of the capabilities of the constituent parts. The process emphasizes the process of discovering, developing, and implementing standards that promote interoperability among systems developed via different sponsorship, management, and primary acquisition processes. * USAF SAB Report: System of Systems Engineering for Air Force Capability Development, July 2005 What is System of Systems Engineering?* Dr. Austin Emergence Platforms Many people know that something is missing from the way we look at systems traditionally. But most are groping to express just what it is. This course is intended to help sharpen the picture.

10. 10 What is a System of Systems? Small stovepipes to large stovepipes – NO Loosely coupled and tightly integrated – YES 10 Dr. Austin Platforms Level of abstraction Functional decomposition

11. 11 Planning Complex Endeavors (April 2007) by David S. Alberts and Richard E. Hayes The Command and Control Research Program (CCRP) has the mission of improving DoD’s understanding of the national security implications of the Information Age. John G. Grimes, Assistant Secretary of Defense (NII) & Chief Information Officer Dr. Linton Wells, II, Principal Deputy Assistant Secretary of Defense (NII) Dr. David S. Alberts, Special Assistant to the ASD(NII) & Director of Research Alberts’ term for what a complex system does.

12. 12 From the forward by John G. Grimes As this latest book from the CCRP explains, we can no longer be content with building an “enterprise-wide” network that stops at the edges of our forces, nor with a set of information sources and channels that are purely military in nature. We need to be able to work with a large and diverse set of entities and information sources. We also need to develop new approaches to planning that are better suited for these coalition operations. The implications are significant for a CIO as it greatly expands the who, the what, and the how of information sharing and collaboration. It also requires a new way of thinking about effectiveness, increasing the emphasis we place on agility, which, as is explained in this book, is the necessary response to uncertainty and complexity. What is this “new way of thinking?” From Chapter 1. Introduction The economics of communications and information technologies has created enormous opportunities to leverage the power of information and collaboration cost effectively by adopting Power to the Edge principles and network-centric concepts. Fine to use these terms, but what do we really mean by them? Platforms Exploration and exploitation

13. 13 From Chapter 2. Key Concepts Complicated Systems Systems that have many moving parts or actors and are highly dynamic, that is, the elements of these systems constantly interact with and impact upon one another. However, the cause and effect relationships within a complicated situation are generally well understood, which allows planners to predict the consequences of specific actions with some confidence. I think this misses the point. Most systems and interactions are (eventually) “well understood.” Complicated systems are often fully entrained through one locus of control. Think Rube Goldberg device. I think this misses the point. Most systems and interactions are (eventually) “well understood.” Complicated systems are often fully entrained through one locus of control. Think Rube Goldberg device. Lots of gears and moving parts, but they all mesh—when it works properly.

14. 14 From Chapter 2. Key Concepts Complex Endeavors (Systems) Complex endeavors involve changes and behaviors that cannot be predicted in detail, although those behaviors and changes can be expected to form recognizable patterns. Complex endeavors are also characterized by circumstances in which relatively small differences in initial conditions or relatively small perturbations (seemingly tactical actions) are associated with very large changes in the resulting patterns of behavior and/or strategic outcomes. Some complex situations develop into complex adaptive systems (CAS), which tend to be robust—to persist over time and across a variety of circumstances. These are often observed in nature in the form of biological or ecological systems. However, while these systems are thought of as robust, they can be pushed out of balance even to the point of collapse through cascades of negatively reinforcing conditions and behaviors. Such perturbations are what ecologists fear when a habitat is reduced to an isolated geographic area or when invasive, nonnative species are introduced. Note biological reference I disagree—although sometimes the only way to predict it is to run (a model of) it. Both chaos and phase transitions

15. 15 The world in a grain of sand To see the world in a grain of sand, and heaven in a wild flower, to hold infinity in the palm of your hands, and eternity in an hour. –William Blake Do you understand what that means? A primary objective of this class is to say in as plain a way as possible what many people have been groping for when talking about complex systems. Do you understand what that means? It’s profound, but it uses poetry to make its point. Many of the ideas may seem like common sense. It’s just that we’ll be looking at them more closely.

16. 16 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.

17. 17 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? 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?

18. 18 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.

19. 19 Moral: unintended consequences A mechanism is installed in an environment. 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.

20. 20 Parasites that control their hosts Dicrocoelium dendriticum causes host ants to climb grass blades where they are eaten by grazing animals, where D. dendriticum lives out its adult life. Toxoplasma gondii causes host mice not to fear cats, where T. gondii reproduces. Spinochordodes tellinii causes host insects to jump into the water and drown, where S. tellinii grows to adulthood. Dicrocoelium dendriticum causes host ants to climb grass blades where they are eaten by grazing animals, where D. dendriticum lives out its adult life. Toxoplasma gondii causes host mice not to fear cats, where T. gondii reproduces. Spinochordodes tellinii causes host insects to jump into the water and drown, where S. tellinii grows to adulthood. It’s amazing how far exploitation of environmental mechanisms can go. (See platforms, later.)

21. 21 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. 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. Harold, Franklyn M. (2001) The Way of the Cell: Molecules, Organisms, and the Order of Life, Oxford University Press. Exploitation Exploration Gain benefit

22. 22 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. (But Nature is not teleological.) –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

23. 23 NetLogo: let’s try it File > Models Library > Biology > Ants Click Open In the full course, students would run NetLogo.

24. 24 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 Presentation Session Transport Network Physical WWW (HTML) — browsers + servers Applications, e.g., email, IM, Wikipedia Each layer is a level of abstraction Notice the similarity to layered communication protocols

25. 25 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? Russ Abbott Sr. Engr. Spec. 310-336-1398 Russ.Abbott@Aero.org Philosophical, but with a practical corollary at the end.

26. 26 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 Emergence: Contemporary Readings in Philosophy and Science Mark A. Bedau and Paul Humphreys (Eds.), MIT Press, April 2008.

27. 27 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.

28. 28 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. 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)

29. 29 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 explained in terms of levels of abstraction. My answer Emergence

30. 30 The Game of Life Click Open File > Models Library > Computer Science > Cellular Automata > LifeLife In the full course, students would run NetLogo.

31. 31 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. 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!”

32. 32 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 as a Programming Platform A second level of emergence. Emergence is not particularly mysterious.

33. 33 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. –Yet the theory of computation is not derivable from GoL rules. One can use glider “velocity” laws to draw conclusions (make predictions) about which cells will be turned on and when that will happen. (Also downward entailment.) Downward causation entailment GoL gliders and Turing Machines are causally reducible but ontologically real. –You can reduce them away without changing how a GoL run will proceed. –Yet they obey higher level laws, not derivable from the GoL rules. GoL gliders and Turing Machines are causally reducible but ontologically real. –You can reduce them away without changing how a GoL run will proceed. –Yet they obey higher level laws, not derivable from the GoL rules.

34. 34 Level of abstraction: the reductionist blind spot 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. –You can look at the implementation to see how it works. A level of abstraction is causally reducible to its implementation. –You can look at the implementation to see how it works. Its independent specification—its properties and way of being in the world—makes it ontologically real. –How it interacts with the world is based on its specification and is independent of its implementation. –It can’t be reduced away without losing something Its independent specification—its properties and way of being in the world—makes it ontologically real. –How it interacts with the world is based on its specification and is independent of its implementation. –It can’t be reduced away without losing something A concept computer science has contributed to the world.

35. 35 Practical corollary: feasibility ranges Physical 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.)

36. 36 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

37. 37 Modeling problems: the difficulty of looking downward It is not possible 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. –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.

38. 38 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

39. 39 Turning dreams into reality Computer Scientists and Engineers both turn dreams (ideas) into reality—systems that operate in the world. But we do it in very different ways.

40. 40 Intellectual leverage in Computer Science: executable externalized thought Computer languages enable executable externalized thought— different from (nearly) all other forms of externalized thought throughout history! –Software is both intentional—has meaning—and executable. –All other forms of externalized thought (except music) 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.

41. 41 Engineering is both cursed and blessed by its attachment to physicality. –There is no reliable floor in the material world. 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 don’t create ontologically independent entities.

42. 42 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, but we may have a hard time saying what it is—emergence. –When designing systems, Computer scientists start with the bit and build 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 down to the physics—using functional decomposition and successive approximation. Engineering is (proudly) applied physics.

43. 43 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 – exploration and exploitation. 10:30–10:45. Platforms – distributed control and systems of systems. 10:45–10:55. Groups – how nature builds systems; the wisdom of crowds. 10:55–11:00. Summary/conclusions – remember this if nothing else.

44. 44 Backups

45. 45 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.

46. 46 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.