1. Evo Devo, Foresight, and Accelerating Change John Smart, President, ASF Association of Professional Futurists April 2006  Santa Fe, NM Slides: accelerating.org/slides.html

2. Los Angeles New York Palo Alto © 2006 Accelerating.org Presentation Outline 1. Assumption: An Accelerating, Infopomorphic Universe 2. Evo Devo: An Emerging Paradigm for Universal Change 3. Three Foresight Studies: Futures, Development, and Acceleration 4. Four Foresight Practices (and Domains): Predicting, Planning, Profiting, Innovating (Science, Society, Economics, Technology) 5. Five Foresight Systems: Individual, Social, Organizational, Global, Universal

3. Los Angeles New York Palo Alto © 2006 Accelerating.org Acceleration Studies Foundation ASF (Accelerating.org) is a nonprofit community of 3,000+ scientists, technologists, entrepreneurs, administrators, educators, analysts, humanists, and systems theorists discussing and dissecting accelerating change. We practice “developmental future studies,” that is, we seek to discover a set of persistent factors, stable trends, and convergent and highly probable scenarios for our common future, and to use this information now to improve our daily evolutionary choices. We suspect key macrohistorical trends include accelerating intelligence, immunity, and interdependence in our global sociotechnological systems, increasing technological autonomy, and the increasing intimacy of the human-machine, physical- digital interface.

4. Los Angeles New York Palo Alto © 2006 Accelerating.org Seeing the Extraordinary Present “There has never been a time more pregnant with possibilities.” — Gail Carr Feldman

5. Los Angeles New York Palo Alto © 2006 Accelerating.org We Have Two Options: Future Shock or Future Shaping “We need a pragmatic optimism, a can- do, change-aware attitude. A balance between innovation and preservation. Honest dialogs on persistent problems, tolerance of imperfect solutions. The ability to avoid both doomsaying and paralyzing adherence to the status quo.” ― David Brin

6. 1. Assumption: An Accelerating, Infopomorphic Universe

7. Los Angeles New York Palo Alto © 2006 Accelerating.org Systems Theory Systems Theorists Make Things Simple (sometimes too simple!) "Everything should be made as simple as possible, but not simpler." — Albert Einstein

8. Los Angeles New York Palo Alto © 2006 Accelerating.org From the Big Bang to Complex Stars: The Decelerating Phase of Universal Development

9. Los Angeles New York Palo Alto © 2006 Accelerating.org From Biogenesis to Intelligent Technology: The Accelerating Phase of Universal Development Carl Sagan’s “Cosmic Calendar” (Dragons of Eden, 1977) Each month is roughly 1 billion years.

10. Los Angeles New York Palo Alto © 2006 Accelerating.org A U-Shaped Curve of Change Big Bang Singularity 100,000 yrs ago: H. sap. sap. 1B yrs: Protogalaxies8B yrs: Earth 100,000 yrs: Matter 50 yrs ago: Machina silico 50 yrs: Scalar Field Scaffolds Developmental Singularity?

11. Los Angeles New York Palo Alto © 2006 Accelerating.org The MESTI Universe Matter, Energy, Space, Time  Information Increasingly Understood  Poorly Known MEST Compression/Density/Efficiency drives accelerating change.

12. Los Angeles New York Palo Alto © 2006 Accelerating.org Physics of a “MESTI” Universe Physical Driver: MEST Compression/Efficiency/Density Emergent Properties: Information Intelligence (World Models) Information Interdepence (Ethics) Information Immunity (Resiliency) Information Incompleteness (Search) An Interesting Speculation in Information Theory: Entropy = Negentropy Universal Energy Potential is Conserved.

13. Los Angeles New York Palo Alto © 2006 Accelerating.org Eric Chaisson’s “Phi” (Φ): A Universal Moore’s Law Curve Free Energy Rate Density Substrate(ergs/second/gram) Galaxies 0.5 Stars 2 (counterintuitive) Planets (Early) 75 Plants 900 Animals/Genetics 20,000(10^4) Brains (Human) 150,000(10^5) Culture (Human) 500,000(10^5) Int. Comb. Engines(10^6) Jets (10^8) Pentium Chips (10^11) Source: Eric Chaisson, Cosmic Evolution, 2001 Ф time

14. Los Angeles New York Palo Alto © 2006 Accelerating.org The Infopomorphic Paradigm The universe is a physical-computational system. We exist for information theoretic reasons. We’re here to evolve and develop. To create, discover, and manage. To care, count, and act. To innovate, plan, profit, and predict

15. Los Angeles New York Palo Alto © 2006 Accelerating.org Cosmic Embryogenesis (in Three Easy Steps) Geosphere/Geogenesis (Chemical Substrate) Biosphere/Biogenesis (Biological-Genetic Substrate) Noosphere/Noogenesis (Memetic-Technologic Substrate) Le Phénomène Humain, 1955 Pierre Tielhard de Chardin (1881-1955) Jesuit Priest, Transhumanist, Developmental Systems Theorist

16. Los Angeles New York Palo Alto © 2006 Accelerating.org De Chardin on Acceleration: Technological “Cephalization” of Earth "No one can deny that a network (a world network) of economic and psychic affiliations is being woven at ever increasing speed which envelops and constantly penetrates more deeply within each of us. With every day that passes it becomes a little more impossible for us to act or think otherwise than collectively." “Finite Sphericity + Acceleration = Phase Transition”

17. Los Angeles New York Palo Alto © 2006 Accelerating.org Stock on ‘Metahumanity’: The Emerging Human-Machine Superorganism Metaman: The Merging of Humans and Machines into a Global Superorganism, 1994 Biologist William Wheeler, 1937: Termites, bees, and other social animals are “superorganisms.” Increasingly, they can’t be understood apart from the structures their genetics compel them to construct. Their developmental endpoint: an integrated cell/organism/supercolony.

18. 2. Evo Devo: An Emerging Paradigm for Universal Change

19. Los Angeles New York Palo Alto © 2006 Accelerating.org The Left and Right Hands of “Evolutionary Development” Complex Environmental Interaction Selection & Convergence “Convergent Selection” Emergence,Global Optima MEST-Compression Standard Attractors Development Replication & Variation “Natural Selection” Adaptive Radiation Chaos, Contingency Pseudo-Random Search Strange Attractors Evolution Right HandLeft Hand Well-Explored Phase Space OptimizationNew Computat’l Phase Space Opening

20. Los Angeles New York Palo Alto © 2006 Accelerating.org Cambrian Explosion Complex Environmental Interaction Selection/Emergence/ Phase Space Collapse/ MEST Collapse Development Adaptive Radiation/Chaos/ Pseudo-Random Search Evolution 570 mya. 35 body plans emerged immediately after. No new body plans since. Only new brain plans, built on top of the body plans (homeobox gene duplication). For more: Wallace Arthur, Jack Cohen, Simon Conway Morris, Rudolf Raff Invertebrates Vertebrates Bacteria Insects Multicellularity Discovered

21. Los Angeles New York Palo Alto © 2006 Accelerating.org Memetic Evolutionary Development Complex Interaction Selection, Convergence Convergent Selection MEST Compression Development Replication, Variation Natural Selection Pseudo-Random Search Evolution Variations on this ev. dev. model have been proposed for: Neural arboral pruning to develop brains (Edelman, Neural Darwinism, ‘88) Neural net connections to see patterns/make original thoughts (UCSD INS) Neural electrical activity to develop dominant thoughts (mosaics, fighting for grossly 2D cortical space) (Calvin, The Cerebral Code, 1996) Input to a neural network starts with chaos (rapid random signals), then creates emergent order (time-stable patterns), in both artificial and biological nets. Validity testing: Hybrid electronic/lobster neuron nets (UCSD INS )

22. Los Angeles New York Palo Alto © 2006 Accelerating.org Evolution vs. Development “The Twin’s Thumbprints” Consider two identical twins: ThumbprintsBrain wiring Evolution drives almost all the unique local patterns. Development creates the predictable global patterns.

23. Los Angeles New York Palo Alto © 2006 Accelerating.org Marbles, Landscapes, and Basins (Complex Systems, Evolution, & Development) The marbles (systems) roll around on the landscape, each taking unpredictable (evolutionary) paths. But the paths predictably converge (development) on low points (MEST compression), the “attractors” at the bottom of each basin.

24. Los Angeles New York Palo Alto © 2006 Accelerating.org How Many Eyes Are Developmentally Optimal? Evolution tried this experiment. Development calculated an operational optimum. Some reptiles (e.g. Xantusia vigilis, and certain skinks) still have a parietal (“pineal”) vestigial third eye.

25. Los Angeles New York Palo Alto © 2006 Accelerating.org How Many Wheels are Developmentally Optimal on an Automobile? Examples: Wheel on Earth. Social computation device. Diffusion proportional to population density and diversity.

26. Los Angeles New York Palo Alto © 2006 Accelerating.org “Convergent Evolution”: Troodon and the Dinosauroid Hypothesis Dale Russell, 1982: Anthropoid forms as a standard attractor. A number of small dinosaurs (raptors and oviraptors) developed bipedalism, binocular vision, complex hands with opposable thumbs, and brain-to-body ratios equivalent to modern birds. They were intelligent pack-hunters of both large and small animals (including our mammalian precursors) both diurnally and nocturnally. They would likely have become the dominant planetary species due to their superior intelligence, hunting, and manipulation skills without the K-T event 65 million years ago.

27. Los Angeles New York Palo Alto © 2006 Accelerating.org Evolution and Development: Two Universal Systems Processes Each are pairs of a fundamental dichotomy, polar opposites, conflicting models for understanding universal change. The easy observation is that both processes have explanatory value in different contexts. The deepest question is when, where, and how they interrelate. Evolution Creativity Chance Randomness Variety/Many Possibilities Uniqueness Uncertainty Accident Bottom-up Divergent Differentiation Development Discovery Necessity Determinism Unity/One Constraints Sameness Predictability Design (self-organized or other) Top-Down Convergent Integration

28. Los Angeles New York Palo Alto © 2006 Accelerating.org Evo-Devo Provides Physical Reasons for Naturally Observed Polarities Evolution Creativity Novelty-Seeking Female “Right Brain” Democratic Freedom Experimentation Play Entropy Creation “Watch a Movie at 1am” “Sleep at 1pm” Development Discovery Truth-Seeking Male “Left Brain” Republican Justice Optimization Work Entropy Density Maximization “Sleep at 1am” “Watch a Movie at 1pm” We each have both of these qualities. Best use always depends on context. Use them both. Keep the balance!

29. Los Angeles New York Palo Alto © 2006 Accelerating.org Exercise Is computer hardware acceleration (Moore’s law) more evolution or development driven? Have historical advances been due more to human creativity or human discovery? What about software?

30. Los Angeles New York Palo Alto © 2006 Accelerating.org Ray Kurzweil: A Generalized Moore’s Law

31. Los Angeles New York Palo Alto © 2006 Accelerating.org Two Political Polarities: Innovation/Discovery vs. Mgmt/Sustainability Evo-Devo Theory Brings Process Balance to Political Dialogs on Innovation and Sustainability Developmental sustainability without generativity creates sterility, clonality, overdetermination, adaptive weakness (Maoism). Evolutionary generativity (innovation) without sustainability creates chaos, entropy, a destruction that is not naturally recycling/creative (Anarchocapitalism).

32. Los Angeles New York Palo Alto © 2006 Accelerating.org Eldredge and Gould (Biological Species) Pareto’s Law (“The 80/20 Rule”) (income distribution  technology, econ, politics) Rule of Thumb:20% Punctuation (Development) 80% Equilibrium (Evolution) Suggested Reading: For the 20%: Clay Christiansen, The Innovator's Dilemma For the 80%: Jason Jennings, Less is More Punctuated Equilibrium (in Biology, Technology, Economics, Politics…)

33. Los Angeles New York Palo Alto © 2006 Accelerating.org A Saturation Lesson: Biology vs. Technology How S Curves Get Old Resource limits in a niche Material Energetic Spatial Temporal Competitive limits in a niche Intelligence/Info-Processing No Known or Historical Limits to Information Acceleration 1. Our special universal structure permits each new computational substrate to be far more MEST resource-efficient than the last 2. The most complex local systems have no intellectual competition Result: No Apparent Limits to the Acceleration of Local Intelligence, Interdependence, and Immunity in New Substrates Over Time

34. 3. Three Foresight Studies: Futures, Development, and Acceleration

35. Los Angeles New York Palo Alto © 2006 Accelerating.org Three Fundamental Foresight Studies: Futures, Development, and Acceleration Futures Studies – “Possible” change (scenarios, alternatives) Development Studies – “Irreversible” change (emergences, phase changes) Acceleration Studies – “Accelerating” change (exponential growth, positive feedback, self-catalyzing, autonomous) All three are evo-devo compliant models of accelerating change.

36. Los Angeles New York Palo Alto © 2006 Accelerating.org Development Studies I: Irreversible and Progressively Emergent Historical Examples (Discuss): The Wheel Electricity Democracy Emancipation Future Scenarios (Discuss): Public Transparency / End of Anonymity The Conversational User Interface The Metaverse The Valuecosm

37. Los Angeles New York Palo Alto © 2006 Accelerating.org Development Studies II: Irreversible and Cyclically Emergent Historical Examples (Discuss): Individuation vs. Conformity (Pendulum) Plutocracy vs. Democracy (Pendulum) Materialism, Idealism, Conflict Resolution (Cycle) Quaternary Generations (Cycle) Guns (Japanese and Chinese history. Nonlethals today.) Warfare (Archaic Age rise, Empires Age peak, 21st Century rise and fall)

38. Los Angeles New York Palo Alto © 2006 Accelerating.org Development Studies III: The S Curve (Logistic Growth) Four Classic Phases: Emergence, Growth, Maturing, Saturation Fifth Developmental Phase: Senescence/Death (and Replacement)

39. Los Angeles New York Palo Alto © 2006 Accelerating.org Exercise: Identify the Logistic Phase Current Year if Date Not Given: Air Transportation World Population (1960) World Population (2000) MOS Computing Price/Performance Copper Twisted Pair Communication Price/Performance Novel Rock Songs Internet Users Bacterial Growth on introduction to new media Rabbit Population Growth on introduction to Australia Ocean Pollution Global Energy Intensity (Gigajoules/capita used annually) Global CO2 Production Global Digital Divide (Between 1st and Emerging World) Global Education Divide Global Economic Divide Global “Power” Divide

40. Los Angeles New York Palo Alto © 2006 Accelerating.org Acceleration Studies: Something Curious Is Going On Unexplained. (Don’t look for this in your physics or information theory texts…)

41. Los Angeles New York Palo Alto © 2006 Accelerating.org Classic Predictable Accelerations: Moore’s Law Moore’s Law derives from two predictions in 1965 and 1975 by Gordon Moore, co-founder of Intel, (and named by Carver Mead) that computer chips (processors, memory, etc.) double their complexity every 12-24 months at near constant unit cost. This means that every 15 years, on average, a large number of technological capacities (memory, input, output, processing) grow by 1000X (Ten doublings: 2,4,8…. 1024). Emergence! There are several abstractions of Moore’s Law, due to miniaturization of transistor density in two dimensions, increasing speed (signals have less distance to travel) computational power (speed × density).

42. Los Angeles New York Palo Alto © 2006 Accelerating.org Transistor Doublings (2 years) Courtesy of Ray Kurzweil and KurzweilAI.net

43. Los Angeles New York Palo Alto © 2006 Accelerating.org Processor Performance (1.8 years) Courtesy of Ray Kurzweil and KurzweilAI.net

44. Los Angeles New York Palo Alto © 2006 Accelerating.org DRAM Miniaturization (5.4 years) Courtesy of Ray Kurzweil and KurzweilAI.net

45. Los Angeles New York Palo Alto © 2006 Accelerating.org Dickerson’s Law: Solved Protein Structures as a Moore’s-Dependent Process Richard Dickerson, 1978, Cal Tech: Protein crystal structure solutions grow according to n=exp(0.19y1960) Dickerson’s law predicted 14,201 solved crystal structures by 2002. The actual number (in online Protein Data Bank (PDB)) was 14,250. Just 49 more. Macroscopically, the curve has been quite consistent.

46. Los Angeles New York Palo Alto © 2006 Accelerating.org Many Tech Capacity Growth Rates Are Independent of Socioeconomic Cycles There are many natural cycles: Plutocracy-Democracy, Boom-Bust, Conflict-Peace… Ray Kurzweil first noted that a generalized, century-long Moore’s Law was unaffected by the U.S. Great Depression of the 1930’s. Conclusion: Human-discovered, Not human-created complexity here. Not many intellectual or physical resources are required to keep us on the accelerating developmental trajectory. Age of Spiritual Machines, 1999 “MEST compression is a rigged game.”

47. Los Angeles New York Palo Alto © 2006 Accelerating.org IT’s Exponential Economics Courtesy of Ray Kurzweil and KurzweilAI.net

48. Los Angeles New York Palo Alto © 2006 Accelerating.org Macrohistorical Singularity Books The Evolutionary Trajectory, 1998 Singularity 2130 ±20 years Trees of Evolution, 2000 Singularity 2080 ±30 years

49. Los Angeles New York Palo Alto © 2006 Accelerating.org Macrohistorical Singularity Books Why Stock Markets Crash, 2003 Singularity 2050 ±10 years The Singularity is Near, 2005 Singularity 2050 ±20 years

50. Los Angeles New York Palo Alto © 2006 Accelerating.org Henry Adams, 1909: The First “Singularity Theorist” The final Ethereal Phase would last only about four years, and thereafter "bring Thought to the limit of its possibilities." Wild speculation or computational reality? Still too early to tell, at present.

51. Los Angeles New York Palo Alto © 2006 Accelerating.org Acceleration Studies: Our Historical Understanding of Accelerating Change In 1904, we seemed nearly ready to see intrinsically accelerating progress. Then came mechanized warfare (WW I, 1914-18, WW II, 1939-45), Communist oppression (60 million deaths). 20 th century political deaths of 170+ million showed the limitations of human- engineered accelerating progress models. Today the idea of accelerating progress remains in the cultural minority, even in first world populations. It is viewed with interest but also deep suspicion by a populace traumatized by technological extremes, global divides, and economic fluctuation. Zbigniew Brzezinski, Out of Control, 1993

52. Los Angeles New York Palo Alto © 2006 Accelerating.org The Technological Singularity Hypothesis Each unique physical- computational substrate appears to have its own “capability curve.” The information inherent in these substrates is apparently not made obsolete, but is instead incorporated into the developmental architecture of the next emergent system.

53. Los Angeles New York Palo Alto © 2006 Accelerating.org The Developmental Spiral Homo Habilis Age2,000,000 yrs ago Homo Sapiens Age 100,000 yrs Tribal/Cro-Magnon Age40,000 yrs Agricultural Age7,000 yrs Empires Age2,500 yrs Scientific Age 380 yrs (1500-1770) Industrial Age180 yrs (1770-1950) Information Age70 yrs (1950-2020) Symbiotic Age30 yrs (2020-2050) Autonomy Age10 yrs (2050-2060) Tech Singularity ≈ 2060

54. Los Angeles New York Palo Alto © 2006 Accelerating.org “NBICS”: 5 Choices for Strategic Technological Development Nanotech (micro and nanoscale technology) Biotech (biotechnology, health care) Infotech (computing and comm. technology) Cognotech (brain sciences, human factors) Sociotech (remaining technology applications) It is easy to spend lots of R&D or marketing money on a still-early technology in any field. Infotech examples: A.I., multimedia, internet, wireless It is almost as easy to spend disproportionate amounts on older, less centrally accelerating technologies. Every technology has the right time and place for innovation and diffusion. First mover and second mover advantages.

55. Los Angeles New York Palo Alto © 2006 Accelerating.org “Unreasonable” Effectiveness and Efficiency of Science and the Microcosm: Wigner and Mead The Unreasonable Effectiveness of Mathematics in the Natural Sciences, Nobel Laureate Eugene Wigner, 1960 After Wigner and Freeman Dyson’s work in 1951, on symmetries and simple universalities in mathematical physics. Commentary on the “Unreasonable Efficiency of Physics in the Microcosm,” VSLI Pioneer Carver Mead, c. 1980. F=ma E=mc 2 F=-(Gm 1 m 2 )/r 2 W=(1/2mv 2 ) In 1968, Mead predicted we would create much smaller (to 0.15 micron) multi-million chip transistors that would run far faster and more efficiently. He later generalized this observation to a number of other devices.

56. Los Angeles New York Palo Alto © 2006 Accelerating.org Understanding the Lever of Nano and ICT “The good opinion of mankind, like the lever of Archimedes, with the given fulcrum [representative democracy], moves the world.” (Thomas Jefferson, 1814) The lever of accelerating information and communications technologies (in outer space) with the fulcrum of physics (in inner space) increasingly moves the world. (Carver Mead, Seth Lloyd, George Gilder…) "Give me a lever, a fulcrum, and place to stand and I will move the world." Archimedes of Syracuse (287-212 BC), quoted by Pappus of Alexandria, Synagoge, c. 340 AD

57. Los Angeles New York Palo Alto © 2006 Accelerating.org Example: Holey Optical Fibers Above: SEM image of a photonic crystal fiber. Note periodic array of air holes. The central defect (missing hole in the middle) acts as the fiber's core. The fiber is about 40 microns across. This conversion system is a million times (10 6 ) more energy efficient than all previous converters. These are the kinds of jaw-dropping efficiency advances that continue to drive the ICT and networking revolutions. Such advances are due even more to human discovery (in physical microspace) than to human creativity, which is why they have accelerated throughout the 20th century, even as we remain uncertain exactly why they continue to occur. Lasers today can made cheaply only in some areas of the EM spectrum, not including, for example, UV laser light for cancer detection and tissue analysis. It was discovered in 2004 that a hollow optical fiber filled with hydrogen gas, a device known as a "photonic crystal," can convert cheap laser light to the wavelengths previously unavailable.

58. Los Angeles New York Palo Alto © 2006 Accelerating.org Accelerating Ephemeralization and the Increasingly Weightless Economy In 1981 (Critical Path), Fuller called ephemeralization, "the invisible chemical, metallurgical, and electronic production of ever-more-efficient and satisfyingly effective performance with the investment of ever-less weight and volume of materials per unit function formed or performed". In Synergetics 2, 1983, he called it "the principle of doing ever more with ever less weight, time and energy per each given level of functional performance” This trend has also been called “virtualization,” “weightlessness,” and Matter, Energy, Space, Time (MEST) compression, efficiency, or density. In 1938 (Nine Chains to the Moon), poet and polymath Buckminster Fuller coined "Ephemeralization,” positing that in nature, "all progressions are from material to abstract" and "eventually hit the electrical stage. " (e.g., sending virtual bits to do physical work) Due to principles like superposition, entanglement, negative waves, and tunneling, the world of the quantum (electron, photon, etc.) appears even more ephemeral than the world of collective electricity.

59. Los Angeles New York Palo Alto © 2006 Accelerating.org Tech Roadmappers Carefully Watch Efficiency/Cost/Capacity Curves! Toshiba Li-Ion NanobatteryWhat Might This Enable? 80% recharge in 60 seconds 99% duty after 1,000 cycles Reliable at temp extremes Cost competitive New consumer wearable and mobile electronics Military apps Plug-in hybrids at home and filling stations (“90% of an electric vehicle economy”) “The future’s already here. It’s just not evenly distributed yet.” ― William Gibson

60. Los Angeles New York Palo Alto © 2006 Accelerating.org An Electric Future: Natural Gas, Nanobatteries, and Plug-In Hybrid Electric Vehicles Nanobatteries can make electric car recharging as fast as gas tank filling, and tomorrow's power grids will be much more decentralized than today's gasoline stations, supporting even greater city densities. “Driving Toward an Electric Future,” John Smart, 2006 Natural gas, already 20% of US energy consumption, is the fastest growing and most efficient component. Nanobatteries recharge 80% in 60 seconds, keep 99% of their duty after 1,000 cycles. 180+ mpg Prius. 34 miles on battery only.

61. Los Angeles New York Palo Alto © 2006 Accelerating.org Understanding Process Automation Perhaps 80% of today's First World paycheck is paid for by automation (“tech we tend”). Robert Solow, 1987 Nobel in Economics (Solow Productivity Paradox, Theory of Economic Growth) “7/8 comes from technical progress.” Human contribution (20%?) to a First World job is Social Value of Employment + Creativity + Education Developing countries are next in line (sooner or later). Continual education and grants (“taxing the machines”) are the final job descriptions for all human beings. Termite Mound

62. Los Angeles New York Palo Alto © 2006 Accelerating.org Oil Refinery (Multi-Acre Automatic Factory) Tyler, Texas, 1964. 360 acres. Run by three operators, each needing only a high school education. The 1972 version eliminated the three operators.

63. Los Angeles New York Palo Alto © 2006 Accelerating.org Automation and Job Disruption Between 1995 and 2002 the world’s 20 largest economies lost 22 million industrial jobs. This is the shift from a Manufacturing to a Service Economy. America lost about 2 million industrial jobs, mostly to China. China lost 15 million ind. jobs, mostly to machines. (Fortune) Despite the shrinking of America's industrial work force, the country's overall industrial output increased by 50% since 1992. (Economist) “Robots are replacing humans or are greatly enhancing human performance in mining, manufacture, and agriculture. Huge areas of clerical work are also being automated. Standardized repetitive work is being taken over by electronic systems. The key to America's continued prosperity depends on shifting to ever more productive and diverse services. And the good news is jobs here are often better paying and far more interesting than those we knew on the farms and the assembly line.”  Tsvi Bisk "The Misery of Manufacturing," The Economist. Sept. 27, 2003 "Worrying About Jobs Isn't Productive," Fortune Magazine. Nov. 10, 2003 “The Future of Making a Living,” Tsvi Bisk, 2003

64. Los Angeles New York Palo Alto © 2006 Accelerating.org World Economic Performance GDP Per Capita in Western Europe, 1000 – 1999 A.D. This curve looks quite smooth on a macroscopic scale. Notice the “knee of the curve” occurs at the industrial revolution, circa 1850.

65. Los Angeles New York Palo Alto © 2006 Accelerating.org Our 2002 service to manufacturing labor ratio, 110 million service to 21 million goods workers, is 4.2:1 Automation and the Service Society

66. Los Angeles New York Palo Alto © 2006 Accelerating.org The Voluntary Future Lifetime hours trends:188019952040 Total Available (after eating, sleeping, etc.) 225,900298,500321,900 Worked to earn a living182,100122,40075,900 Balance for Leisure and Voluntary Work 43,800176,100246,000 Prediction: Great increase in voluntary activities. Culture, entertainment, travel, education, wellness, nonprofit service, humanitarian and development work, the arts, etc. Source: The Fourth Great Awakening and the Future of Egalitarianism, 2000, Robert Fogel (Nobel-prize-winning economist, founder of the field of cliometrics, the study of economic history using statistical and mathematical models)

67. Los Angeles New York Palo Alto © 2006 Accelerating.org Angus Maddison’s Phases of Capitalist Development, 1982* *Also Pentti Malaska’s Funnel Model of Societal Transition, 1989/03 Network/Services/KM Society Society of Intangible Needs (“Weightless Economy”) Network 1.0 “McJobs” & Service 65% of Jobs, 2000’s Network 2.0 New Middle Class 40% of Jobs, 2030’s Network 3.0 Consolidation Again 15% of Jobs, 2060’s Manufacturing/Information Society Society of Tangible Needs (“Property Economy”) Manufacturing 1.0 Exploitive Jobs 50% of Jobs, 1900’s Manufacturing 2.0 New Middle Class 35% of Jobs, 1950’s Manufacturing 3.0 Offshoring/Globalizing 14% of Jobs, 2000’s Agricultural Society Society of Basic Needs (“Food/Shelter Economy”) Agriculture 1.0 Subsistence Jobs 80% of Jobs, 1820’s Agriculture 2.0 Family Farms 50% of Jobs, 1920’s Agriculture 3.0 Corporate Farms 2% of Jobs, 1990’s

68. Los Angeles New York Palo Alto © 2006 Accelerating.org Network Economy 1.0 Remittances (From Guest Workers in U.S. and Canada) Foreign Direct Investment (Corporate) NGO’s (Nonprofit Contribs) Government Aid (IMF, WB, G8, USAID) Q: Which is a larger monetary flow in Latin America today, the bottom-up green or the top-down purple column?

69. Los Angeles New York Palo Alto © 2006 Accelerating.org Network Economy 1.0 Remittances (From Guest Workers in U.S. and Canada) Foreign Direct Investment (Corporate) NGO’s (Nonprofit Contribs) Government Aid (IMF, WB, G8, USAID) Q: Which is a larger monetary flow in Latin America today, the bottom-up green or the top-down purple column? A: Remittances, since 2003. This may be a permanent shift. Shows what could happen in Africa, Russia, and other continually emigrating (“brain drain”) nations. Future of Philanthropy, GBN, 2005

70. Los Angeles New York Palo Alto © 2006 Accelerating.org Tools for Networking 1.0: Social Network Analysis Note the linking nodes in these “small world” (not scale free) networks. “Chains of Affection,” Bearman & James Moody, AJS V110 N1, Jul 2004

71. Los Angeles New York Palo Alto © 2006 Accelerating.org Networking Books Linked, Albert-Laszlo Barabasi, 2003 Six Degrees, Duncan Watts, 2003

72. Los Angeles New York Palo Alto © 2006 Accelerating.org The New Paradigm: Out of (Individual) Control. The Wisdom of the (Well Organized) Crowd.

73. Los Angeles New York Palo Alto © 2006 Accelerating.org Back to the Greek Future Greece built an enviable empire on the backs of human slaves. 21C humanity is building an even more enviable one on the backs of our robotic servants. Expect machine emancipation, too. “The more things change, the more some things stay the same.”

74. 4. Four Foresight Practices (and Domains) Predicting, Planning, Profiting, Innovating (Science, Society, Economics, Technology)

75. Los Angeles New York Palo Alto © 2006 Accelerating.org Systemic (Integrated) Foresight: Greeks, Pronouns, Skill Sets and Processes Greeks True What Is Good What ‘We’ Want Beautiful What ‘I’ Want Pronouns It/ItsWe/He/She/YouI/Me Foresight Skill Sets Discovery Universal Management Social Creativity Individual Processes Development Convergence Statics/Dynamics Law/Emergence Evolution Divergence

76. Los Angeles New York Palo Alto © 2006 Accelerating.org Integral Maps: Ken Wilber’s Process Quadrants Computational Processes Management/Validity Tests We need foresight in all quadrants (processes and management tests). All drive change. None can be reduced to the others There are no others as basic!

77. Los Angeles New York Palo Alto © 2006 Accelerating.org Systemic Thinking: Edward De Bono’s Six Thinking Hats It/ItsWe/He/She/YouI/Me White (Facts) Yellow (Social Positive) Red (Intuition) Blue (Process) Black (Social Negative) Green (Creative)

78. Los Angeles New York Palo Alto © 2006 Accelerating.org Types of Intelligence: Gardner’s Eight ‘Frames’/ ‘Modules’ Gardner has developed research and metrics for eight different “frames” or “modules” of human capacity. A promising way to look at thinking.

79. Los Angeles New York Palo Alto © 2006 Accelerating.org Integral Intelligence: Gardner’s ‘Frames,’ Wilber’s ‘Lines’ I (Innovating)It (MEST Mgmt - Profiting) Intrapersonal/Self-Identity Body/Kinesthetic/Health Cog-Emot/Needs/Self-Care Creativity/Innovating/Vision Visual/Spatial Aural/Musical MEST/Thing-Care Decisionmaking/Adapting We (Social Mgmt - Planning)Its (Predicting) Interpersonal/Social-Identity Linguistic/Social-Narrative Intimacy/Social-Care Moral/Cultural/Social-Relation Nature/Systems Logical/Mathematical Object Relatns/Structure-Care Discovery/Predictive/Counting Meta/Integral/Spiritual (Attractor) Wilber proposes additional intelligence lines/dimensions on top of Gardner’s. I’ve mapped nine I recognize to his quadrants above. They fit nicely. Wilber also proposes all lines follow a developmental vector, that the higher levels of all lines look spiritual, and that the spiritual line is a convergent intelligence attractor that continually tries to look meta (above, beyond) all the other lines.

80. Los Angeles New York Palo Alto © 2006 Accelerating.org Integral Foresight Development: Wilber, De Bono, Gardner, Ichazo, Jenkins, Jung, Myers-Briggs, Smart I (Innovating) Subjective Self It (MEST Mgmt - Profiting) Objective Self Intrapersonal/Self-Identity Body/Kinesthetic/Health Cog-Emotional/Needs/Self-Care Creativity/Innovating/Visioning The Individualist (4) (Type A) The Enthusiast (7) (Type B) “I” Introverted Orientation “F” Feeling Function INFP, INFJ, ISFP Visual/Spatial Aural/Musical MEST/Thing-Care Decisionmaking/Adapting (Z & NZ) The Challenger (8) (Type A) The Loyalist (6) (Type B) “J” Judging Process (Think or Feel) “S” Sensing Function ESTJ, ISTJ, ESFJ, ISFJ We (Social Mgmt - Planning) Subjective System Its (Predicting) Objective System Interpersonal/Social-Identity Linguistic/Social-Narrative Intimacy/Social-Care Moral/Cultural/Social-Relation The Achiever (3) (Type A) The Helper (2) (Type B) “E” Extroverted Orientation “N” Intuition Function ENFP, ENFJ, ENTP, ENTJ Nature/Systems Logical/Mathematical Object Relations/Structure-Care Discovery/Predictive/Counting The Reformer (1) (Type A) The Investigator (5) (Type B) “P” Perceiving Process (Intuit or Sense) “T” Thinking Function INTP, ESTP, ISTP  Meta/Integral/Spiritual (Attractor)  The Peacemaker (9) (Types A and B)  INTJ, ESFP (Integral Types) Wilber’s Four “Quadrants” Smart’s Four “Foresight Skills” Gardner’s Eight “Intelligences” (Multiple Intelligences) Wilber’s Nine Additional “Developmental Lines” (Smart’s Interpretation) Ichazo/Naranjo’s (Enneagram) Nine “Personality Types”, (Subtyped by Jenkin’s Type A/Type B Classifiers Myers-Briggs Sixteen Personality Types (Jung’s 4 Mental Functions, 2 Orientations, and 2 Processes). Fourteen of the sixteen M-B types weight to one of the four quadrants by possessing both its function and its orientation or process. Note that there are eight M-B “manager” (the most prevalent), three “creator” types, three “discoverer” types, and two “integral” types. This seems a good reflection of these skills and prevalence in the general population.

81. Los Angeles New York Palo Alto © 2006 Accelerating.org Four Foresight Domains: Technological, Social, Economic, Scientific I (Individual/Self) Creativity-Driven Futures It (Organizational/Contractual) Agenda-Driven Futures Technological Innovating Economic Profiting We (Social/Kinship) Consensus-Driven Futures Its (Global/Species) Research-Driven Futures Social Planning Scientific Predicting

82. Los Angeles New York Palo Alto © 2006 Accelerating.org Four Essential Foresight Practices: Innovating, Planning, Profiting, and Predicting Innovating/Creating (I) Thinking and acting by personal preferred futures Planning/Negotiating (We) Thinking and acting by social consensus plans Profiting/Adapting (It) Thinking and acting by objectively measurable results Predicting/Discovering (Its) Thinking and acting by statistically predictive forecasts

83. Los Angeles New York Palo Alto © 2006 Accelerating.org Exercise: Categorize these Foresight Practices (Innovating, Planning, Profiting, or Predicting) sci-fi and utopian studies budgeting accounting and finance business intelligence scenarios and creative thinking roadmapping social and environmental impact marketing research individual visioning management by consensus business IT (ERP, CRM, etc.) soft sciences and systems theory social networking collective visioning innovation command leadership enterprise planning management by meas. results forecasting and trends sci-tech R&D conflict resolution risk management and insurance management by forecast entrepreneurship strategic planning scanning history of prediction community building statistics and actuarial science hard sciences

84. Los Angeles New York Palo Alto © 2006 Accelerating.org Four Essential Foresight Practices: Innovating, Planning, Profiting, and Predicting Innovating/Creating (I) Management by personal preferred futures: command leadership, sci-fi and utopian studies, visioning, creative thinking, scenarios, entrepreneurship, innovation, sci-tech R&D Planning/Negotiating (We) Management by social consensus: social networking, collective visioning, conflict resolution, community building, strategic planning, roadmapping, enterprise robustness and resilience planning Profiting/Adapting (It) Management by measurable results: accounting, finance, budgeting, measured economic, social, and environmental benefits, risk mgmt (insurance), hedging, business IT (ERP, CRM, etc.) Predicting/Discovering (Its) Management by forecast (soft to hard): scanning, marketing research, business intelligence, soft sciences and systems theory, history of prediction, forecasting, statistical trends, actuarial science, hard sciences

85. 5. Five Foresight Systems: Individual, Social, Organizational, Global, Universal

86. Los Angeles New York Palo Alto © 2006 Accelerating.org Five Foresight Systems: Individual, Social, Organizational, Global, Universal I (Individual/Self) Creativity-Driven Futures It (Organizational/Contractual) Agenda-Driven Futures Technological Innovating Creating (introverted, feeling) Caring [Love/Beauty] Economic Profiting (Measuring) Managing-Politics-Law-Etc. (judging, sensing) Acting [Wealth/Progress] We (Social/Kinship) Consensus-Driven Futures Its (Global/Species) Research-Driven Futures Social Planning (Negotiating) Managing-Politics-Law-Etc. (extroverted, intuiting) Acting [Peace/Unity] Scientific Predicting Discovering (thinking, perceiving) Counting [Truth/Knowledge] All (Universal/Metascientific) [Transcendence] (Attractor) Physics-Driven Futures

87. Los Angeles New York Palo Alto © 2006 Accelerating.org Three Fundamental Foresight Studies: Futures, Development, and Acceleration I (Individual/Self) Creativity-Driven Futures It (Organizational/Contractual) Agenda-Driven Futures Technological Innovating Economic Profiting We (Social/Kinship) Consensus-Driven Futures Its (Global/Species) Research-Driven Futures Social Planning Scientific Predicting Acceleration Studies (Universal System Attractor) Question: Which is unlike the others? The universal system grows asymptotically via science and technology, and secondarily via economic and social change. All five (individual, kinship tribe, contractual tribe, species, universe) may be astrobiologically developmental. Futures Studies (Evolutionary) Development Studies (Developmental)

88. Los Angeles New York Palo Alto © 2006 Accelerating.org Four Types of “Futures Studies” – Exploratory/Creativity-Driven (Speculative Literature, Art) – Consensus-Driven (Political, Trade Organizations) – Agenda-Driven (Institutional, Strategic Plans) – Research-Driven (Stable Developmental Trends) The last is the critical one for acceleration studies and development studies It is also the only one generating falsifiable hypotheses Accelerating and increasingly efficient, autonomous, miniaturized, and localized computation is apparently a fundamental meta-stable universal developmental trend. Or not. That is a key hypothesis ASF seeks to address.

89. Los Angeles New York Palo Alto © 2006 Accelerating.org Smart’s Laws of Technology 1. Tech learns ten million times faster than you do. (Electronic vs. biological rates of evolutionary development). 2.Humans are selective catalysts, not controllers, of technological evolutionary development. (Regulatory choices. Ex: WMD production or transparency, P2P as a proprietary or open source development) 3. The first generation of any technology is often dehumanizing, the second is indifferent to humanity, and with luck the third becomes net humanizing. (Cities, cars, cellphones, computers).

90. Discussion