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1 Introduction to Complex Systems: How to think like nature Russ Abbott Sr. Engr. Spec. Rotn to CCAE 310-336-1398  1998-2007. The.

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Presentation on theme: "1 Introduction to Complex Systems: How to think like nature Russ Abbott Sr. Engr. Spec. Rotn to CCAE 310-336-1398  1998-2007. The."— Presentation transcript:

1 1 Introduction to Complex Systems: How to think like nature Russ Abbott Sr. Engr. Spec. Rotn to CCAE 310-336-1398 Russ.Abbott@Aero.org  1998-2007. The Aerospace Corporation. All Rights Reserved. Organizations: resolving the tension between autonomy and emergence What individuals can do that groups can’t What groups can do that individuals can’t

2 2 Flocking 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/ A soccer game based on “forces.”soccer game based on “forces.” –Download, execute. –After it starts, click Console tab and reduce speed to 0.025.

3 3 Group/system-level emergence Both the termite and ant models illustrate emergence (and multi- scalarity). 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.Evolution for EveryoneDavid Sloan Wilson 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.Evolution for EveryoneDavid Sloan Wilson You and I are each (a) entities and (b) cell colonies.

4 4 Breeding groups/teams/systems 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. Traditional evolutionary theory says there is no such thing as group selection, only individual selection. Bill Muir (Purdue) demonstrated that was wrong. Evolutionary processes are fundamental to complex systems Wikipedia commons http://www.ansc.purdue.edu/faculty/muir_r.htm

5 5 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, etc. Reproduction and child rearing. 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.) Group (and more generally multi-level) selection now accepted as valid. 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 HaidtJonathan Haidt 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 HaidtJonathan Haidt We evolved to be pro-social within groups but xenophobic between groups. – Michael ShermerMichael Shermer

6 6 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

7 7 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. In basketball, a team will beat an individual (of approximately the same skill level). –This is not so much emergence as power in numbers. Why groups? Two steps.

8 8 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

9 9 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. 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. 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. Good subject for mirror neuron experiments

10 10 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. -Version 1. The winning table is the one with the greatest total. -Version 2. A table survives if it winds up with at least $50. CD C3/30/5 D5/01/1 A far from equilibrium system. New energy is supplied “for free.”

11 11 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

12 12 Wise crowds: more than the sum of their parts Web wise crowd platforms Wikis Mailing lists Chat rooms Prediction markets (James Surowiecki, The Wisdom of Crowds) (Scott Page, The Difference) 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 free to speak his/her own mind and make own decision. Distillation mechanism: to extract the essence of the crowd's wisdom. 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 Participant autonomy. Emergence. Second slide ahead

13 13 A wise crowd as assistant and companion

14 14 Distillation: 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) Knowledge extraction: prediction markets.

15 15 Prediction markets 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. StatementStatement issued by 25 world-famous academics. May 2007. Including: Kenneth Arrow, Daniel Kahneman, Thomas Schelling, Robert Shiller, Cass Sunstein.

16 16 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

17 17 Market mechanisms Intrade uses a continual double (bid and asked) auction. (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 CDA. –Benefit for being right early. –MSR: Inkling, Qmarkets; DPM: Yahoo! Tech Buzz Game.InklingQmarketsYahoo! Tech Buzz Game List of markets: MidasOracle.org.MidasOracle.org

18 18 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.

19 19 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

20 20 Exploratory behavior: 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.

21 21 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.

22 22 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 die-off produce webbing in duck feet and bat wings but not in human fingers. Military strategy of “probing for weakness.” Ant and bee foraging. 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

23 23 Innovative environments The Internet The inspiration for net-centricity and the GIG Goal: to bring the creativity of the internet to the DoD What do innovative environments have in common? What do innovative environments have in common? Other innovative environments The scientific and technological research process The market economy Biological evolution

24 24 Innovative environments Innovation is always the result of an evolutionary process. Randomly generate new variants—by combining and modifying existing ones. Select the good ones. (Daniel Dennett, Darwin's Dangerous Idea) Requires mechanisms: For creating stable and persistent design representations so that they can serve as the basis for new possibilities. For combining and modifying designs. For selecting and establishing better ones.

25 25 Designs in various environments Recorded asCreated by How instantiated Established InternetSoftware Programmers who know the techniques Self-instantiatingBy users Scientific knowledge Publications Scientists who know the literature The publication is the instantiation By peer review Market economy Trade secrets Product developers who know the tricks Entrepreneurial manufacturing By consumers Biological evolution DNA Combination and mutation Reproduction Whether it finds a niche Entities: nature’s memes Implicit designs Construction, combination and mutation Implementation of a level of abstraction Whether it finds a niche All bottom-up

26 26 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. Establishing successful variants Allow new ideas to flourish or wither based on how well they do.

27 27 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

28 28 How groups benefit from individual autonomy Exploratory behavior typically requires autonomous individuals. But much exploratory behavior is wasted effort. Success generally depends on more than a single lone inventor. –Successful 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 into the group and allow them to take root. –Establishment is often built into a group’s process. –At the evolutionary level—including our hyper-evolutionary global society—this frequently requires “creative destruction,” which is often far more difficult to accept. Exploratory behavior typically requires autonomous individuals. But much exploratory behavior is wasted effort. Success generally depends on more than a single lone inventor. –Successful 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 into the group and allow them to take root. –Establishment is often built into a group’s process. –At the evolutionary level—including our hyper-evolutionary global society—this frequently requires “creative destruction,” which is often far more difficult to accept. Markets are how we integrate creative destruction into society. Ant foraging; building out the circulatory system. Schumpeter

29 29 Implications for C2 There is no “commander’s intent” in nature or in the market. But there is something like (commander’s) intent in organisms. How do successful organisms work? A simplified model. –Lower levels discover opportunities through exploratory behavior. Constrained by “rules of engagement,” which protect them from harm. Initiatives often grow from the “edges,” where perception occurs. –Higher levels provide perspective and impose constraints. They do not primarily issue commands. Additional resources recruited as success builds—if it does. But lots of opportunities to withhold support or shape direction. This is a bottom-up model of resource allocation. Decisions about increasingly significant commitments made at increasingly higher levels. If entire organism commits, becomes “commanders intent.” To implement this model one should stay healthy and build skills and capabilities, which can be recruited/applied/committed when relevant. Different from starting with limited and narrowly focused top-level missions, goals, and objectives. Top-level mission is to survive, to build skills, and to ensure an environment within which this process can proceed and the organism can thrive. There is no “commander’s intent” in nature or in the market. But there is something like (commander’s) intent in organisms. How do successful organisms work? A simplified model. –Lower levels discover opportunities through exploratory behavior. Constrained by “rules of engagement,” which protect them from harm. Initiatives often grow from the “edges,” where perception occurs. –Higher levels provide perspective and impose constraints. They do not primarily issue commands. Additional resources recruited as success builds—if it does. But lots of opportunities to withhold support or shape direction. This is a bottom-up model of resource allocation. Decisions about increasingly significant commitments made at increasingly higher levels. If entire organism commits, becomes “commanders intent.” To implement this model one should stay healthy and build skills and capabilities, which can be recruited/applied/committed when relevant. Different from starting with limited and narrowly focused top-level missions, goals, and objectives. Top-level mission is to survive, to build skills, and to ensure an environment within which this process can proceed and the organism can thrive.


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