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SCENARIO PLANNING: A BRIEF GUIDE TO THE FUTURE Ahti Pietarinen Department of Philosophy University of Helsinki Fudan University May 2013.

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Presentation on theme: "SCENARIO PLANNING: A BRIEF GUIDE TO THE FUTURE Ahti Pietarinen Department of Philosophy University of Helsinki Fudan University May 2013."— Presentation transcript:

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2 SCENARIO PLANNING: A BRIEF GUIDE TO THE FUTURE Ahti Pietarinen Department of Philosophy University of Helsinki Fudan University May 2013

3 Futures The Dao is above existence and non-existence. Existence is for men who use words, But the Dao does not use words. It is a silent as a flower. Words come from the Dao – the Dao produces words, But it does not use them. It loves and nourishes all things, but does not command them. I know not its name, I call it the...

4 Scenario Planning Lecture 1: Why the future is not like the past Lecture 2: Black Swans and Fat Tails Lecture 3: Uncertainty in the Real World (Scenario Planning) Key words: Fundamental Uncertainty; Futures; Risk; Possibility; Probability; Plausibility; Black Swans; Fat Tails; Abduction; Hypothetical Retrospection; Scenario Planning.

5 Top Philosophy Departments QS University Rankings 2012 World 1.University of Oxford 97.2 2. University of Cambridge 94.2 3. Harvard University 94.0 … Fudan University 79.7 15. Fudan University 79.7 21. Chinese U. Hong Kong 76.7 22. Peking University 34. Beijing Normal University 71.4 40. Wuhan University 69.5 41. Taiwan University 69.3 45. Renmin University 67.4 51-100. Tsinghua U.; U. Helsinki 101-150. Sun Yat-sen U. (Zhongshan) Asia 1.Fudan University 2.Chinese U. Hong Kong 3.Peking University 4.Beijing Normal University 5.Wuhan University 6.Taiwan University 7.National University of Singapore 68.70 8.Renmin University 9.Tsinghua U. 10.Sun Yat-sen U. 5

6 The problem with the future is that it is different. If you cannot think differently, the future will always be a surprise. I never think about the future. It will be here soon enough. - A. Einstein Reporter: Louis, wheres the jazz going ito be n the future? Louis Armstrong: Man, if I knew where jazz is going, Id already be there. Day by day, nothing seems to change. But soon, everythings different. Our lifes are defined by opportunities. Even the ones we miss. - F. Scott Fitzgerald

7 The tyranny of the present -Cicero No battle plan survives the contact with the enemy. The best plan wins the battle before it is fought. Interviewer to a politician: Is there anything that could disrupt the workings of your plan? - Events, dear sir, events!

8 I will never marry again. –Barbara Hutton, after her 2nd divorce, 1941 I will never marry again. –Barbara Hutton, after 3rd divorce, 1945 This is positively my final marriage. –Barbara Hutton, after marrying her 6th husband, 1955 He has all my previous husbands best qualities and none of the bad qualities. –Barbara Hutton, after marrying her 7th husband (Prince of Vietnam), 1964 (In 1966, she filed for divorce.)

9 Companies unprepared Polaroid (est.1937) – Bankrupt 2001: failed to go digital Sun Microsystems – Bought by Oracle for $7.4 billion in 2010 (value $200 billion in 2005): failed to go software from hardware Swissair (est.1931) – Bankrupt 2002: too aggressive; failed to see the low-fare airline boom Lehman Brothers (est.1850) – Bankrupt 2008 – no liquidity to respond client change Bookstores, Newspapers, CD industry, Aviation...

10 There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say, we know there are some things we do not know. But there are also unknown unknowns – the ones we dont know we dont know. – D. Rumsfeld, the former US Secretary of Defence, 2001 Fundamental Uncertainty – the Unknown Unknowns Instrumental rationality; optimization; min-max principle etc. Instrumental rationality; risk analysis; conditional probabilities Procedural rationality; no risk; no probabilities

11 Fundamental Uncertainty – the Unknown Unknowns 1.Non-measurable or unknown probabilities 2.Limited or no foresight 3.Open-ended, non-instrumental rationality: Bounded/procedural rationality in decision making (rational action + configurations of habits and practices) 4.Non-optimizing behaviour (satisficing, good enough; practical reasoning, focal points, salience) 5.Non-well-structured problem spaces; non-deterministic neural structures 6.Dispensing with methodological individualism 7.Moving away from situational reasoning (deduction/induction) to discovery, innovation, argumentation (abduction); the space of problem contexts no longer invariant

12 Lower freedom to configure the space of possible events lower uncertainty Maximum (fundamental) uncertainty: no constraints as to what future or possible events we may reasonably consider : Scenario planning How conceptual categories are formed? (prototypes, similarities, analogies, associations, metaphors...) Cognitive meanings Concepts and predicates that frame the situations, and particular concepts and predicates that provide focal points, are more relevant than standard computational skills and algorithms.

13 The classical distinction between risk and uncertainty (Keynes 1907): Risk is measurable uncertainty. Given any two alternative events, A and B, and given the evidence (conditional probabilities), either A is more likely/probable than B B is more likely/probable A A and B are equi-probable, or A and B are incomparable (= uncertainty). The problem of uncertainty concerns the mixture of the inferential and the representational aspects of events and abilities. The classical distinction between risk and uncertainty (Keynes 1907): Risk is measurable uncertainty. Given any two alternative events, A and B, and given the evidence (conditional probabilities), either A is more likely/probable than B B is more likely/probable A A and B are equi-probable, or A and B are incomparable (= uncertainty). The problem of uncertainty concerns the mixture of the inferential and the representational aspects of events and abilities.

14 More information can hurt (a game-theoretic example) Uncertainty can be a good thing.

15 What is risk? Risk analysis and risk assessment – Basic problem: the lack of knowledge about the effects of some new thing, medical treatment, technology,... What is risk? 1.Something unwelcome may or may not occur: Smoking is a big health risk 2.Probability of unwelcome event: (decision-making under uncertainty or under risk, gambling): How likely it is that an expensive treatment will fail 3.Severity measure (expectation value) obtained by multiplying the probability of unwelcome event with a measure of its disvalue (risk analysis): Is nuclear energy better than fossil fuels Risk: there is something we know about what we do not know Risk analysis and risk assessment – Basic problem: the lack of knowledge about the effects of some new thing, medical treatment, technology,... What is risk? 1.Something unwelcome may or may not occur: Smoking is a big health risk 2.Probability of unwelcome event: (decision-making under uncertainty or under risk, gambling): How likely it is that an expensive treatment will fail 3.Severity measure (expectation value) obtained by multiplying the probability of unwelcome event with a measure of its disvalue (risk analysis): Is nuclear energy better than fossil fuels Risk: there is something we know about what we do not know 15

16 Questions about Risk Are our daily risks getting higher or lower? – Life expectancy is growing, but on the other hand there are new possibilities of large-scale global risks Is risk analysis an optimisation problem? – In new and emerging technology assessment such as in N(ano)B(io)I(nformation)C(ommunication)- technologies, risk analysis happens under fundamental uncertainty: we do not even know the possible effects, let alone their probabilities Too often we are prone to the tuxedo fallacy Are our daily risks getting higher or lower? – Life expectancy is growing, but on the other hand there are new possibilities of large-scale global risks Is risk analysis an optimisation problem? – In new and emerging technology assessment such as in N(ano)B(io)I(nformation)C(ommunication)- technologies, risk analysis happens under fundamental uncertainty: we do not even know the possible effects, let alone their probabilities Too often we are prone to the tuxedo fallacy 16

17 The Tuxedo Fallacy

18 When the risks fail (When the angels fall) In dealing with uncertainty, we need: Plausibility judgments Identification of the most suitable cognitive contexts Space of events be given But then there is also Structural ignorance (fundamental uncertainty) for decision problems – Events cannot be naturally given by the modeller or the decision maker at all But maybe... Some spaces of conceivable events can be proposed (the scenarios, the futures...) In dealing with uncertainty, we need: Plausibility judgments Identification of the most suitable cognitive contexts Space of events be given But then there is also Structural ignorance (fundamental uncertainty) for decision problems – Events cannot be naturally given by the modeller or the decision maker at all But maybe... Some spaces of conceivable events can be proposed (the scenarios, the futures...)

19 Predictions, extrapolations, forecasts... Hitler will never become Chancellor; the best he can hope for is to head the Post Department. -President of Germany, 1931 Germany has no desire to attack any country in Europe. -The News Chronicle, 1936 War between Japan and the US is not within the realm of reasonable possibility. A Japanese attack on Pearl Harbor is a strategic impossibility. -Major Eliot, 1938 No matter what happens, the U.S. Navy is not going to be caught napping. -US Navy Secretary, Dec 4, 1941

20 Predictions, extrapolations, forecasts... Victory is in sight. - General Harkins, Commander of U.S. forces in South Vietnam, 1963 One day it will be written: this was Americas finest hour. - President Nixon, 1973 (On April 1975, after the last US troops were evacuated from Vietnam, South Vietnam surrended unconditionally to North Vietnam. 58.209 Americans were killed.)

21 An expert is a man who has made all the mistakes which can be made in a very narrow field. – Niels Bohr An expert is a man who has made all the mistakes which can be made in a very narrow field. – Niels Bohr

22 Really the cases of probabilistic ignorance; the probability distributions just cannot be known nor assumed We need to work with ex ante structural ignorance (where the space of events is unknown or only partially known) Really the cases of probabilistic ignorance; the probability distributions just cannot be known nor assumed We need to work with ex ante structural ignorance (where the space of events is unknown or only partially known) The worst has passed. – Wall Street, Oct 24, 1929 This is the time to buy stocks. – New York Herald Tribune, Oct 30, 1929 A severe depression like that of 1920-21 is outside the range of probability –Harvard Economic Society, Nov 16, 1929 Probabilities, really?

23 Possibility, Plausibility, Probability Possibility (logical): that which can happen (is not impossible) Probability: that which can be measured Plausibility: (Lat. that which can be applauded) – Something defensible but not beyond all doubt (but perhaps beyond reasonable doubt) – Reliability of evidence needs to be assessed – Similarities, analogies take a central role – Concept formation; new conceptual categories created (and for which no words may exist) The logics for all three are very different...

24 Conjunction Fallacy Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable? 1.Linda is a bank teller. 2.Linda is a bank teller and is active in the feminist movement.

25 So, why was the future not like the past? Day by day, nothing seems to change. But soon, everythings different.

26 Non-linearity Output not proportional to input – Education; medication; business trips; traffic jams...

27 Chaos and Complexity Random variables that try to measure uncertainty are becoming more and more complex We are losing predictability even when knowing the precise laws and initial conditions We are losing the cause- effect structures Chaos: When the present determines the future, but the approximate present does not approximately determine the future.

28 Ergodicity Future is a statistical shadow of the past, based on samples of the past and current data Invariant measures of dynamical systems Time average becomes equal to space average Ergodic axiom is widely assumed in the mainstream standard textbook economics But it is a highly idealised assumption about macroeconomic processes of human behaviour

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30 Convexity effects

31 Being prepared Sitting quietly, doing nothing, Spring comes, and the grass grows by itself

32 SCENARIO PLANNING: A BRIEF GUIDE TO THE FUTURE Ahti Pietarinen Department of Philosophy University of Helsinki Fudan University May 2013

33 Futures The Dao is above existence and non-existence. Existence is for men who use words, But the Dao does not use words. It is a silent as a flower. Words come from the Dao – the Dao produces words, But it does not use them. It loves and nourishes all things, but does not command them. I know not its name, I call it the...

34 Scenario Planning Lecture 1: Why the future is not like the past Lecture 2: Black Swans and Fat Tails Lecture 3: Uncertainty in the Real World (Scenario Planning) Key words: Fundamental Uncertainty; Futures; Risk; Possibility; Probability; Plausibility; Black Swans; Fat Tails; Abduction; Hypothetical Retrospection; Scenario Planning.

35 Scenario Planning II: Black Swans and Fat Tails

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42 Black Swans When you look back, they all make some sense (retrospective rationalizability) Rare events do not show up in samples, because they are rare They come from model predictions, not from experience or empirical evidence But models (especially in economics) assume – Instrumental rationality – Ergodicity – Probabilities computed by extrapolation A high error rate in computing small probabilities As probability goes down, the impact grows bigger Small probabilities are really non-measurable

43 Fat tails

44 Normal (Gaussian) distributions

45 Everything that can be invented has been invented. –The U.S. Office of Patents, 1899 The phonograph is not of any commercial value. – T. A. Edison, c.1880 Heavier than air flying machines are impossible. – Lord Kelvin, c.1895 The horse is here to stay, but the automobile is only a novelty-a fad. – President of the Michigan Bank, advicing Henry Fords lawyer not to invest in the Ford Motor Company, 1903. Innovations

46 I think there is a world market for about five computers. –T. J. Watson, Chairman of the Board for IBM, 1943 There is no reason for any individual to have a computer in their home. –President of Digital Equipment Corporation, World Future Society Conference, 1977 By 1980 all power is likely to be costless. –H. Luce, founder of Time and Fortune magazines, 1956

47 Discoveries are also Scientific discoveries – Microbies (L. Pasteur) – Penincillin, antibiotics (A. Fleming) In experiments, we need the option to retain or discard the result – Millikan oil-drop experiments – Evolution (genetic mutations) And such discoveries really change the world Chance favours the prepared mind. -L. Pasteur Chance favours the prepared mind. -L. Pasteur

48 But the real chance is not blind Discoveries cannot be based just on blind luck, chance, or serendipity – Research is grounded on exploiting uncertainty, making use of the unknown unknowns What is the generator of processes of coping with uncertainty and ignorance? – Abductive reasoning

49 We also have a power of abduction (Charles Peirce) 1.Deduction: M is PAll the beans in this bag are white S is M These beans in my hand are from this bag S is (necessarily) PThese beans in my hand are white. 2.Induction: S1, S2, S3,... are M These beans in my hand are from this bag S1, S2, S3,... are P These beans in my hand are white Any M is (probably) P All the beans in this bag are white. 3.Abduction: M is P1, P2, P3,... All the beans in this bag are white S is P1, P2, P3,... These beans in my hand are white S is (plausibly) M These beans in my hand are from this bag. 1.Deduction: M is PAll the beans in this bag are white S is M These beans in my hand are from this bag S is (necessarily) PThese beans in my hand are white. 2.Induction: S1, S2, S3,... are M These beans in my hand are from this bag S1, S2, S3,... are P These beans in my hand are white Any M is (probably) P All the beans in this bag are white. 3.Abduction: M is P1, P2, P3,... All the beans in this bag are white S is P1, P2, P3,... These beans in my hand are white S is (plausibly) M These beans in my hand are from this bag. 49

50 Abduction 1.The surprising fact, C, is observed 2.But if A were true, C would be a matter of course 3.Hence, there is reason to suspect that A is true. Abduction seeks a hypothesis to account for facts by guessing Is fallible, preserves ignorance, is not intended to generate new knowledge Oftenest even a well-prepared mind guesses wrong. But the modicum of success of our guesses far exceeds that of random luck, and seems born of attunement to nature by instincts developed or inherent, especially insofar as best guesses are optimally plausible and simple in the sense of the facile and natural, as by Galileos natural light of reason. (Peirce) 1.The surprising fact, C, is observed 2.But if A were true, C would be a matter of course 3.Hence, there is reason to suspect that A is true. Abduction seeks a hypothesis to account for facts by guessing Is fallible, preserves ignorance, is not intended to generate new knowledge Oftenest even a well-prepared mind guesses wrong. But the modicum of success of our guesses far exceeds that of random luck, and seems born of attunement to nature by instincts developed or inherent, especially insofar as best guesses are optimally plausible and simple in the sense of the facile and natural, as by Galileos natural light of reason. (Peirce) 50

51 Ignorance – How it Drives Science Stuart Firestein (2013) There are a lot of facts to be known in order to be a professional anything lawyer, doctor, engineer, accountant, teacher. But with science there is one important difference. The facts serve mainly to access the ignorance… Scientists dont concentrate on what they know, which is considerable but minuscule, but rather on what they dont know…. Science traffics in ignorance, cultivates it, and is driven by it. Mucking about in the unknown is an adventure; doing it for a living is something most scientists consider a privilege.

52 Science not a body of knowledge Working scientists dont get bogged down in the factual swamp because they dont care all that much for facts. Its not that they discount or ignore them, but rather that they dont see them as an end in themselves. They dont stop at the facts; they begin there, right beyond the facts, where the facts run out. Facts are selected, by a process that is a kind of controlled neglect, for the questions they create, for the ignorance they point to. Being a scientist requires having faith in uncertainty, finding pleasure in mystery, and learning to cultivate doubt. There is no surer way to screw up an experiment than to be certain of its outcome. We must teach students how to think in questions, how to manage ignorance.

53 Sample the future, aim to get a glimpse at the phenomenon, do not try to forecast it For the meaning of concepts, look at the cases of use under various conditions When you look for it, you cannot see it When you listen to it, you cannot hear it But when you use it, it is inexhaustible (Laozi) My study is neither difficult nor easy. When I am hungry I eat. When I am tired I rest.

54 The importance of ignorance Science is all about exploiting uncertainties 1.Improve the payoff, not knowledge (High risk-high gain; research is a fat tail phenomenon) Cheap science should be funded first Reduce the cost per testing a hypothesis (fallibilism minimizing the losses) Higher expected return from a series of small trials than from a large single trial (non-linearity, convexity) Simplicity counts How to take the step from stone axe to hand axe...

55 Convexity effects

56 Importance of Ignorance 2.Get rid of restrictive planning – Need for exit strategies – Follow the unforeseen; stretch your mental models, invent new concepts – Centralized decisions tend to fail – Lots of attempts needed before success (Angry Birds was Rovios 56 th game) – Invest on people, not on procuring strategies or research plans

57 Importance of Ignorance 3.Theory/science not a necessary condition for practical/technological development – telling (angry) birds how to fly 4.Knowing well what doesnt work – Consequences of positive bias: where to publish the negative results? How not to become successful in life How I failed to make my first million

58 Science & Technology 10 Unsolved Mysteries, Scientific American 10/2011Scientific American 1. How Did Life Begin? 2. How Do Molecules Form? 3. How Does the Environment Influence Our Genes? 4. How Does the Brain Think and Form Memories? 5. How Many Elements Exist? 6. Can Computers Be Made Out of Carbon? 7. How Do We Tap More Solar Energy? 8. What Is the Best Way to Make Biofuels? 9. Can We Devise New Ways to Create Drugs? 10. Can We Continuously Monitor Our Own Chemistry? 58

59 What if... Counterfactual reasoning Virtual histories Use imagination and creativity (but not just create fiction) Explore the semantics of possible worlds Niall Ferguson (ed.): 1997. Virtual History: Alternatives and Counterfactuals. Geoffrey Hawthorn: 1991. Plausible Worlds: Possibility and Understanding in History and the Social Sciences. Our lifes are defined by opportunities. Even the ones we miss. - F.S. Fitzgerald

60 SCENARIO PLANNING: A BRIEF GUIDE TO THE FUTURE Ahti Pietarinen Department of Philosophy University of Helsinki Fudan University May 2013

61 Futures The Dao is above existence and non-existence. Existence is for men who use words, But the Dao does not use words. It is a silent as a flower. Words come from the Dao – the Dao produces words, But it does not use them. It loves and nourishes all things, but does not command them. I know not its name, I call it the...

62 Scenario Planning Lecture 1: Why the future is not like the past Lecture 2: Black Swans and Fat Tails Lecture 3: Uncertainty in the Real World (Scenario Planning) Key words: Fundamental Uncertainty; Futures; Risk; Possibility; Probability; Plausibility; Black Swans; Fat Tails; Abduction; Hypothetical Retrospection; Scenario Planning.

63 Coping with Uncertainty in the Real World: Scenario Planning Compensates common errors in decision making by avoiding under- and overpredictions Expands the range of possibilities without drifting into science fiction Copes with shocks, black swans and fat tails Stretches the mental models and invites imagination Lays out new models and concepts

64 Scenario Planning Differs from contingency planning, which examines one uncertainty at a time (the base case and an exception) Explores joint impact of many uncertainties – Sensitivity analysis (e.g. bank stress tests) examines the effect of a change in one variable at a time – But in a networked world, a small change may lead to huge, unforeseen impacts (chaos, complexity) Avoids the best case vs. worst-case (probabilistic, risk- based) analyses Includes factors that cannot be formally modelled or simulated – New regulations, policy changes, value shifts, innovations, customer behaviours, unexpected consequences…

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66 Why Scenario Planning Helps managers etc. to maintain several internally consistent and plausible, but mutually incompatible scenarios in the mind Challenges the prevailing mind- set Invites for changes they otherwise would ignore Counters the optimism bias

67 Scenario Planning: Who should do it? Facing high uncertainty (anything to do with complex systems) Many costly surprises occurred in the past Organization cannot generate new innovations – Organization culture too formal, bureaucratic, top- down – Quality of strategic thinking low Others are using it, too! (currently the most widely used method in strategic management)

68 Building Scenarios Cross-sectional involvement – outside the organization, invite clients, suppliers, politicians, think tanks, academics, journalists, lay people… Objective: to see the future in terms of key fundamental drivers, trends and uncertainties Process more important than product…

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70 The Building Process 1.Define the scope – Time frame (5-20 years or more; technology change, product life cycles, elections, competitors situation,…) – What is the new value that is being sought for? – Counterfactual history (What if we had done X in the past) 2.Identify basic trends and drivers – Economical, political, societal, technological, scientific, educational, environmental, legal,… 3.Identify key uncertainties – What events would affect the desired value? Are they related?

71 The Building Process 4.Construct initial scenarios – Separate negative and positive elements, cluster the most important trends and drivers 5.Check for consistency, plausibility, stability – Are the trends and time frames compatible? – Are outcomes of uncertainties compatible? – Would the major stakeholders accept them? 6.Develop learning scenarios – Generate themes/stories from simple scenarios – Name the scenarios

72 The Building Process 7.Identify research needs – Find blindspots, study the technological development, competitors,… 8.Develop quantitative models – To prevent implausible scenarios – To assess the consequences of various scenarios 9.Evolve toward decision scenarios – Iterate steps 1-7 until those scenarios are built against which actual strategies can be tested – Judge the balance and focal points of the scenarios

73 Which scenarios are good? 1.Relevance to concepts and concerns of the users and stakeholders 2.Statistically consistent (hard!) 3.Archetypal (future prototypes), general descriptions of radically different, competing futures 4.Equilibrium, homeostasis of a system 5.Cover a wide range of possibilities

74 Examples of Scenario Methods (see on the blackboard) 1.Two-axis method – Suitable for long-term horizons, high uncertainty – Unknown unknowns 2.Branch analysis method – Suitable for mid-term horizon, specific scenarios – Known unknowns (risk, causal links) 3.Cone of plausibility method – Suitable for short to mid-term horizon – Known+unknown unknowns (prediction+plausible alternatives + a Black Swan)

75 What scenarios are not How the future will turn out to be – Sometimes the mark of a good scenario is that once it was created, we were able to avoid it Predictions, which tend to be biased on confirming evidence and discounting disconfiming evidence (prone to cognitive biases) – We have very one-sided minds for testing new ideas – Which one you would choose: A drug X that is safe on 90% confidence level or the drug Y that does not kill you on 90% confidence level? 1000 RMB today or 1100 RMB by the new year?

76 Summary Given the ever-increasing category of unknown unknowns, our cognitive biases are affecting our judgments increasingly more Scenario planning: a study of our collective ignorance – Institutionalises the hunt for weak signals, black swans, non-measurable probabilities – Calls for intellectual courage: to choose evidence that does not quite fit our current concepts

77 Unexpected Consequences: Why The Things We Trust Fail, James W. Martin, 2011 The world is full of wonderful products and services that occasionally disappoint and even harm us. This book explores the reasons these failures occur, examining them from technological, human, and organizational perspectives. Using more than 40 recent catastrophic events to illustrate its points, the book discusses structural and machine failure, but also the often-overlooked failure of people and of systems related to information technology, healthcare, and security. Faulty technology played a surprisingly small part in many of the scrutinized disasters, but cognitive factors and organizational dynamics, including ethics, are major contributors to most unexpected and catastrophic failures. 77

78 Hypothetical retrospection How to make morally right decisions concerning the unknown unknowns? – If nothing is known of the consequences of our actions, are we freed from moral considerations? Hypothetical retrospection: decisions evaluated assuming one possible future has materialized – Evaluation based on present values and on information available when the action was taken – Decision rule: choose an alternative that emerges as morally acceptable from all such hypothetical retrospections – Involves systematic search for future viewpoints

79 Future of Science & Technology We cannot predict future technologies 1.Fundamental uncertainty in the behaviour of technologies; the list of device failures can never be known to be completed 2.Behaviour of users unpredictable The Volvo Effect 3.The emergence of new social, cultural and economic patterns inherently unpredictable Telephone, mobile communication, social networking 4.Technology part of complex systems that behave chaotically Markets, societies, ecosystems,... 79

80 Information Technology

81 Evolution of Information Technology The evolution of ICT: 1.Recording technologies (prehistory 19 th century ) 1.Writing systems, written records, non-biological memory 2.Mechanical reproduction (printing) 3.Universal language projects (17 th century ) 2.Communicational functions (1837 ) 1.Telegraphs 2.Cinema, radio, telephone, television (mass media) 3.Processual (elaborative) functions (1950 ) 1.Computation, the computer 2.the Internet 3.Mobile communication, Social Media Intelligent/Big data, Information repositories,…? 81

82 Policy guidance in Technology Assessment and Governance 1.Safety engineering: 1.Primary prevention (hazard elimination) 2.Safety barriers 3.Safety factors +Copes with uncertainties and not only risks -May become a safety risk itself… 2.Scenario planning/Hypothetical Retrospection 3.Participatory Technology Assessment Issues of technological future inseparable from social, personal and cultural issues; risk is only one factor among many in decision making. 82

83 The Precautionary Principle The Maastricht Treaty: The absence of certainties, given the current state of scientific and technological knowledge, must not delay the adoption of effective and proportionate preventive measures aimed at forestalling a risk of grave and irreversible damage to the environment at an economically acceptable cost. Principle 15 of the 1992 Rio Declaration: Where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures to prevent environmental degradation.

84 Precautionary Principle Implications: to understand it we need to understand the nature of (i) potential for irreversible harm (risks) and (ii) scientific uncertainties is a normative principle (favours environmental and human health factors over others) refers to reasons for action, is not a guide or a recipe for what action to take applies in all contexts (technology, policy making, governance, international law, trade,…)

85 In the Real World... Climate change Aging population High energy prices Increased life-expectancy (longer healthier years)

86 The UN Millennium Development 86 1.Eradicate extreme poverty and hunger. 2.Achieve universal primary education. 3.Promote gender equality and empower women. 4.Reduce child mortality. 5.Reduce by three quarters the maternal mortality rate. 6.Combat HIV/AIDS, malaria, and other diseases. 7.Ensure environmental sustainability. 8.Develop a global partnership for development. 1.Eradicate extreme poverty and hunger. 2.Achieve universal primary education. 3.Promote gender equality and empower women. 4.Reduce child mortality. 5.Reduce by three quarters the maternal mortality rate. 6.Combat HIV/AIDS, malaria, and other diseases. 7.Ensure environmental sustainability. 8.Develop a global partnership for development.

87 Future of Humanity Static or evolving conception of human civilisation? – Posthumanism, superintelligence – Singularity Hypothesis The simulation argument: either 1.nearly all human-level civilizations go extinct before becoming posthuman, or 2.any posthuman civilization is extremely unlikely to run a significant number of simulations of their evolutionary history, or 3.we are almost certainly living in a computer simulation (Boström 2009) Find serious errors in these arguments! RUBBISH!

88 88: Recommended Reading 1.Schoemaker, Paul (1995). Scenario Planning: A Tool for Strategic Thinking, Sloan Management Review 36, 25-40.Scenario Planning: A Tool for Strategic Thinking 2.Van Der Hejden, Kees (2005). Scenarios: The Art of Strategic Conversations, John Wiley. 3.Taleb, Nassim (2007). The Black Swan: The Impact of the Highly Improbable, Random House. 4.Firestone, Stuart (2013). Ignorance: How It Drives Science, Oxford University Press. 5.Hansson, Sven Ove (2007). Hypothetical Retrospection, Ethical Theory and Moral Practice 10, 145-157.Hypothetical Retrospection 6.Hendricks, V. et al. (2011). The Routledge Companion to Philosophy of Technology, Routledge.


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