The Fortune Sellers

1. The Fortune Sellers Author William A. Sherden

2. Agenda • Brief Introduction • Detailed Discussion of Chapter 1 to 5 • Summary

3. Introduction • Sellers • People who makes benefits through predicting futures in a business field • Fortune • These predictions are not reliable and their success can be attributed to luck not science

4. CP1: The Second Oldest Profession • What will the future bring? • Express our uncertainty and desire of knowing future • The development of prediction industry • From ancient world to modern society • Promoted by human nature • Forecasting business now exists everywhere, economics, weather, population…

5. CP1: The Second Oldest Profession • Disappointing output of prediction industry • Although many people and billions of money involved in this industry, the prediction accuracy is disappointing. • Individual success can be explained by the law of probability. 100 people predict a sequence of throwing a coin five times. The probability of nobody predicts right is (1 - 1/32) ^ 100 = 4.2%

6. CP1: The Second Oldest Profession • Impossible mission for scientists • Determinism: Generate laws from historical patterns and cycles. Then make prediction. Newton and his gravitation. • Works in naive world. That’s why we always see lots of assumptions in a paper studying human behaviors in order to simplify the situation • Determinism cannot handle the complexity and chaos in real world

7. CP1: The Second Oldest Profession • What should we do? • Cannot make perfect prediction does not mean we can ignore future • Focus on the vital information in current environment. Not necessarily concern what it could bring • Question before believing • The boom of today’s social network and the burst of yesterday’s dot-com bubble

8. CP2: When Chaos Rains • Weather prediction • Has a long history and plays important role in people’s life. Transportation and agricultural firms relies on weather report to survive. • Main barriers: data collection and the speed of data transmission • Kites, balloon, radar and satellite. The desire of prediction future incites the development of technology • Much more like a kind of science compared with other prediction industry. Since it study the laws of nature.

9. CP2: When Chaos Rains • How to conduct prediction • Predict a change from current position A to future position B • Fully Understand A and B • Prove the law that’s bring us from A to B • Distinguish short-term and long-term prediction

10. CP2: When Chaos Rains • Why always wrong? • Weather situations are volatile • Not able to capture all the details • Natural laws cannot be stimulated in a linear system. e.g. “butterfly effect” • Feature of nonlinear system • Highly sensitive to initial point • High order functions make slight errors at beginning be amplified in future

11. CP2: When Chaos Rains • Chaos system • Sensitivity to initial conditions • Topological mixing • Density of periodic orbits

12. CP2: When Chaos Rains • Increasing initial accuracy helps in a non-linear system • Higher resolution of input image will not affect the output accuracy of an face recognition system based on neural networks

13. CP2: When Chaos Rains • Long-term view • Report from AMC and CAC slightly exceed random chance (37% to 33%) • The “only credible” Gray’s hurricane forecast model a little better than naive average in long term • Reason • Attribute meaning to meaningless patterns • Treat co-occurrence as cause-effect relationship • Rorschach factor: make conclusion based one’s own imagine

14. CP2: When Chaos Rains • Weather prediction is still useful and worth of investing • Severe weather events prediction is like a system with high precision but low recall • A successful prediction can save lots of money and an error prediction brings few harms • Pay little attention to long-term prediction • It is a competition between human with nature, a kind of science.

15. CP3: Dismal Scientists • The difference between weather predictor and economists • Economists predict the future of people and nations • Economists make predictions without anyone else’s approval ( president of CEA & CBO) • Economists use a set of assumptions and beliefs to analyze their particular economic ‘religion’

16. CP3: Dismal Scientists • Performance of economists’ prediction • Economists cannot predict the turning points in the economy • The accuracy drops with lead time • Average performance is about as good as guessing • No economic forecasters consistently lead in forecasting accuracy

17. CP3: Dismal Scientists • Economy is a complex system • refers to the phenomenon of order emerging from the interactions among its components • Features • No natural laws governing complex system • Can not be dissected into their components, people involved, daily transactions • Counterintuitive cause-effect results • Punctuated by unexpected moments • Adapts to the environment and evolve • No fixed cycles, histories do not repeat

18. CP3: Dismal Scientists • Economy is also nonlinear system • Nonlinear features can be described by positive feedback loops • Higher-production volume reduces costs, which lead to lower prices, which lead to increased sales, which lead to higher production • Contain self-influenced variables

19. CP3: Dismal Scientists • Problem of economics research • Poor quality of data, e.g. inaccuracy of international economic statistics • Assume consumer behaviors on a “rational” math equations. Economists derive theories in their academic offices. • Overlook psychology and sociology influence.

20. CP3: Dismal Scientists • Harmful influence of faulty econ predictions • Undue influence in the election of those who run governments; voters should ignore econ promises and predictions • Result in faulty decision making by policymakers, business executives who are misguided by predictions • Doomsday predictions will bring needless anxiety, which will cause negative feedback loops

21. CP3: Dismal Scientists • Some suggestions • Economists should look more to biological science than to physics • Develop theories based on real world observation. • Be aware of self-influenced variables • Making hypotheses based on nature observation and testing hypotheses with controlled experiments

22. CP4: The Market Gurus • Predictors in Wall Street • Predict the Bull and the Bear of the market • Two groups of people: Fundamentalists and Technicians • Fundamentalists using logic to deduce how the market respond to events • Technicians derive patterns from history to predict future

23. CP4: The Market Gurus • An example of technicians’ solutions Yields and Years of Chinese bonds have some relationship; We need to build a yield curve to judge the performance of a certain bond; Distribution of bonds should follow this curve, so we learn the function from historical data;

24. CP4: The Market Gurus • Fundamentalist Steps • Estimate the intrinsic value of stock • Determine whether the market has over or undervalued the stock • Assume the price of this stock will eventually rise or fall to its true value • Stricken by the Efficient Market Hypothesis • EMH states that the stock market knows everything that is knowable, in another word, no stock is being over or undervalued

25. CP4: The Market Gurus • Observation Facts • 70 percent of investment professionals fail to beat the market • 30 percent of mutual funds beat the market in any given years, none do so with any consistency • Statistics show stock market is unpredictable

26. CP4: The Market Gurus • Exceptions • Peter Lynch, consistently outperforms the market • Roger Babson, predicted the market crash of 1929 • Reason • Peter Lynchfocused on those small firms which are overlooked by the market • Roger Babsonwas remembered by once successful prediction and his reputation let him foment instead of predicting a crash. In long term, his accuracy is not better than random chance

27. CP4: The Market Gurus • Enlightenment • Pay attention to those neglected instances in a group to be studied. May we can get opposite result against mainstream idea • Compare those sophisticated model or approaches with classical naive methods. E.g. Naive Bayes for prediction or classification / Euclidean distance for similarity measurement

28. CP5: Checking the “Unchecked Population” • Population Forecasts • Not a moneymaking business but has more convincing predictions than weather and economics • Simple but useful equation: Future = Current + births – deaths + net immigration • Development of technology reduces the death rate and make lifetime stable for research

29. CP5: Checking the “Unchecked Population” • Predict death is easy than birth • Death is generally controlled by nature • Birth involves more human motivation and even controlled by government policy which makes it more complex • Future is not promising • Technology makes us no more vulnerable to bacterium; it also makes microorganismsbecome immune to modern medicines.

30. CP5: Checking the “Unchecked Population” • Limitations of Population Prediction • The prediction skills are simple, only accurate in global forecasting. (different region / different religion…) • Cannot handle tuning points Pattern and predictable = smooth Hard to find a predictable function to capture the tuning point

31. Conclusion • Prediction is a impossible mission for any science and technology, at least in long-term forecasting • Maybe the only certain thing is the development of technology itself. This topic will be discussed in the following chapters

32. Thank you!

33. The Fortune Sellers-- Chapter 6 - 9 Authored by: William A. Sherden Presented by: Weifeng Li 10/24/2012

34. Chapter 6 Science fact and fiction

35. Widespread fascination with future technology, however, is a recent phenomenon in human history. • We had to experience technological progress first before we could foresee it happening in the future • Inventions influenced science fiction writers; fiction has influenced science as well. • Rocket scientist Константи́н Эдуа́рдович Циолко́вский and Verne

36. High-tech anxiety • The flow of technology forecasts pyramid • Largest technology forecaster in Japan • STA of MITI • Many US politicians and policy analysts see government sponsored technology forecasting and other forms of industrial policy as misdirected • Efforts to control commercial events that are best left to the marketplace to resolve

37. The flow of technology forecasts MEDIA Futurists Wall Street Analysts Industry Associations Government Agencies Forecasting Firms Think Tanks High-Tech Firms

38. The dark art of technology forecasting • Analytic tools • All useless in predicting significant events • S-curve • Useful in forecasting existing though inaccurate • Delphi • Widely conducted • “Consensus is achieved mainly by group pressure to conformity”

39. Promises, promises • Long-term technology predictions have been wrong about 80 percent of the time • 75 percent of Japan’s STA’s predictions are wrong • Amusing predictions of Herman Kahn • Nuclear power – too cheap to measure • Robots – work only 10 hours a year • Thinking computers

40. Out of the blue • Inability of experts to predict the major breakthrough technologies • “all inventions met similar derision shortly before becoming practical realities” • Airplane/telephone/light bulb/electronic computers • Experts have repeatedly failed to foresee breakthrough innovations, the turning points in the evolution of technology • Our capacity to foresee the future has not improved at all • Situational bias has continually been a major barrier in envisioning future technology • Imagine future technologies as mere extensions of things that already exist • Radio • Those most likely to develop a new technology often do not • Source of innovation varies by industry

41. The hidden path of technological Darwinism • Unpredictability of the evolution of technology • Uncertainties in technological evolution • Unworkable concepts • Computer: require 100-foot-long rooms to house • Fusion reactor: generate more energy than it consumes • Superconducting materials: hard to fashion • Superfluidity: temperatures near absolute zero • Unknown applications • Laser: situational bias • When technologies are developed to solve a problem, it is difficult to envision how it might otherwise be used: steam engine; semiconductor material • Unproved value • Technological synergies • Superior innovations typically emerge from a combination of different technologies • Creative destruction • The creation of new technologies kills off existing ones • Technological lock-in • Lock-in occurs when a technology or convention becomes the industry standard before competitors come on the scene • Inferior technologies might be locked in as industry standard • QWERTY • Chance events • Large events • Small events • DOS

42. Proceed with extreme caution • “technological forecasting is largely a bogus and fraudulent enterprise” • Reasons to be skeptical about predictions: • Weak tools • “the illusion of knowing what is going to happen is worse than not knowing” • Technology forecasts cannot be used to justify business ventures • Government and industry should work together to set technical standards • Government should not be in the business of predicting winning technologies of the future • Government should provide a large number of smaller grants than fewer larger ones

43. Chapter 7 The futurists

44. The infirm foundations of social science • Sociology is “science with the greatest number of methods and the least results” – Jules Henri Poincaré • Society as an unpredictable complex system • Scientific predictions were impossible in social science • Adapt, • Individual, • Chance and accident, and • Heisenberg uncertainty principle contribute to unpredicability • Corollary principles • History does not repeat itself • Major social trends, movements, and revolutions surprise those closet to the events • Social theories are necessarily weak and ephemeral in their application to social phenomena • Social predictions are subjective and, accordingly, susceptible to situational bias, political agendas, and wishful thinking • Social predictions tend to be wrong

45. The Newtonian socialists • Social prophets believed that social progress was inevitable and that it was as much a law of nature as Newton’s laws of motion • John Stuart Mill/Karl Marx

46. From utopia to techno-totalitarianism • Utopia: confident expectation of constant technological progress and steady social improvement • H. G. Wells/Edward Bellamy • After WWI, utopian optimism dwindled, while visions of the techno-society gone bad proliferated • 1984 • Although the dismal visions of the future have been similarly exaggerated, they continue to provide legitimate warnings of the potential dangers of technology and totalitarianism

47. Futurology • Futurists who call themselves as such • Beliefs about the practice of futurology • There is no single future…the objective therefore becomes to identify and describe a useful range of alternatives… • We can see those alternative futures… • We can influence the future… • We have a moral obligation to use our capability to anticipate and to influence the future… • Modern practice of futurology • H. G. Wells • By the 1920’s: Keynes/Freud/Churchill • After 1940, the futurist movement really started to take shape: Herman Kahn/Daniel Bell/Alvin Toffler

48. The trend spotters • Commercial trend spotters • People who search for emerging societal trends and sell their discoveries to businesses and governments. • Naisbitt • Faith Popcorn • Cocooning • Environment, education, and ethics in the 1990s

49. An excuse to do the inexcusable • An evil side to social prediction: the misuse of prophecy by demagogues to control the masses in order to achieve their master plan for society. • The best way for a society to deflect the dangerous effects of false prophecy is to ensure a high level of education throughout its citizenship and to protect the freedom of all people to dissent.

50. Chapter 8 Corporate chaos