Ahti Pietarinen
Department of Philosophy
University of Helsinki
Fudan University
May 2013
SCENARIO PLANNING:
A BRIEF GUIDE TO THE FUTURE
It loves and nourishes all things,
but does not command them.
I know not its name,
I call it the...
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.
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.
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
…
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
”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
”Our lifes are defined by
opportunities. Even the
ones we miss.” - F. Scott
Fitzgerald
”Day by day, nothing
seems to change. But
soon, everything’s
different.”
Reporter: ”Louis, where’s the jazz going
ito be n the future?
Louis Armstrong: ”Man, if I knew where
jazz is going, I’d already be there.”
”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!”
”The tyranny of the present” -Cicero
”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.)
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...
“There are known knowns; there
← Instrumental rationality;
are things we know we know. optimization; min-max principle etc.
We also know there are known
unknowns; that is to say, we know ← Instrumental rationality; risk
there are some things we do not analysis; conditional probabilities
know. But there are also
unknown unknowns – the ones we ← Procedural rationality; no risk;
don’t know we don’t know.”
no probabilities
– D. Rumsfeld, the former US
Secretary of Defence, 2001
Fundamental Uncertainty – the Unknown Unknowns
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
• 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.
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.
Uncertainty can be a good thing.
More information can hurt
(a game-theoretic example)
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.
2.
3.
Something unwelcome may or may not occur:
“Smoking is a big health risk”
Probability of unwelcome event: (decision-making under
uncertainty or under risk, gambling):
“How likely it is that an expensive treatment will fail”
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
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’
16
The Tuxedo Fallacy
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...)
Predictions, extrapolations, forecasts...
”Hitler will never become
Chancellor; the best he can
hope for is to head the Post
• ”War between Japan and the
Department.”
US is not within the realm of
-President of Germany,
reasonable possibility.”
1931
”A Japanese attack on Pearl
Harbor is a strategic
”Germany has no desire to
impossibility.” -Major Eliot,
attack any country in
1938
Europe.” -The News
Chronicle, 1936
• ”No matter what happens,
the U.S. Navy is not going to
be caught napping.” -US
Navy Secretary, Dec 4, 1941
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 America’s 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.)
“An expert is a man
who has made all the
mistakes which can
be made in a very
narrow field.”
– Niels Bohr
Probabilities, really?
”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
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)
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...
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.
So, why was the future not like the past?
”Day by day, nothing
seems to change. But
soon, everything’s
different.”
Non-linearity
• Output not proportional
to input
– Education; medication;
business trips; traffic
jams...
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 causeeffect structures
Chaos: When the
present determines
the future, but the
approximate present
does not
approximately
determine the future.
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
Convexity effects
Being prepared
Sitting quietly, doing nothing,
Spring comes, and
the grass grows by itself
Ahti Pietarinen
Department of Philosophy
University of Helsinki
Fudan University
May 2013
SCENARIO PLANNING:
A BRIEF GUIDE TO THE FUTURE
It loves and nourishes all things,
but does not command them.
I know not its name,
I call it the...
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.
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.
Scenario Planning II: Black Swans and Fat Tails
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
Fat tails
Normal (Gaussian) distributions
Innovations
”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 Ford’s lawyer not to invest in the
Ford Motor Company, 1903.
”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
Discoveries are also
• Scientific discoveries
– Microbies (L. Pasteur)
– Penincillin, antibiotics (A. Fleming)
”Chance favours the
prepared mind.”
-L. Pasteur
• 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
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
”We also have a power of abduction”
(Charles Peirce)
1.
Deduction:
All the beans in this bag are white
These beans in my hand are from this bag
These beans in my hand are white.
2.
M is P
S is M
S is (necessarily) P
Induction:
These beans in my hand are from this bag
These beans in my hand are white
All the beans in this bag are white.
3.
S1, S2, S3,... are M
S1, S2, S3,... are P
Any M is (probably) P
Abduction:
M is P1, P2, P3,...
S is P1, P2, P3,...
S is (plausibly) M
All the beans in this bag are white
These beans in my hand are white
These beans in my hand are from this bag.
49
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 Galileo’s
natural light of reason.” (Peirce)
50
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 don’t
concentrate on what they know, which is
considerable but minuscule, but rather on what
they don’t 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.”
Science not a body of knowledge
“Working scientists don’t get bogged down in the factual
swamp because they don’t care all that much for facts.
It’s not that they discount or ignore them, but rather
that they don’t see them as an end in themselves. They
don’t 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.”
”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)
• ’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
”My study is neither difficult
nor easy.
When I am hungry I eat.
When I am tired I rest.”
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...”
Convexity effects
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 Rovio’s 56th game)
– Invest on people, not on procuring ‘strategies’ or
‘research plans’
Importance of Ignorance
3. Theory/science not a necessary condition for
practical/technological development
– “telling (angry) birds how to fly”
4. Knowing well what doesn’t 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”
Science & Technology
“10 Unsolved Mysteries”, Scientific American 10/2011
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
”What if...”
”Our lifes are defined by opportunities.
Even the ones we miss.” - F.S. Fitzgerald
• 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.
Ahti Pietarinen
Department of Philosophy
University of Helsinki
Fudan University
May 2013
SCENARIO PLANNING:
A BRIEF GUIDE TO THE FUTURE
It loves and nourishes all things,
but does not command them.
I know not its name,
I call it the...
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.
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.
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
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, riskbased) analyses
• Includes factors that cannot be formally modelled or
simulated
– New regulations, policy changes, value shifts, innovations,
customer behaviours, unexpected consequences…
Why Scenario Planning
• Helps managers etc. to
maintain several internally
consistent and plausible, but
mutually incompatible
scenarios in the mind
• Challenges the prevailing mindset
• Invites for changes they
otherwise would ignore
• Counters the optimism bias
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, topdown
– Quality of strategic thinking low
• Others are using it, too! (currently the most
widely used method in strategic management)
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…
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?
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
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
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
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)
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?
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
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
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
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
Information Technology
Evolution of Information Technology
• The evolution of ICT:
1. Recording technologies (prehistory→ 19th century→)
1.
2.
3.
Writing systems, written records, non-biological memory
Mechanical reproduction (printing)
Universal language projects (17th century→)
2. Communicational functions (1837→)
1.
2.
Telegraphs
Cinema, radio, telephone, television (mass media)
3. Processual (elaborative) functions (1950→)
1.
2.
3.
Computation, the computer
the Internet
Mobile communication, Social Media
• Intelligent/Big data, Information repositories,…?
81
Policy guidance in Technology
Assessment and Governance
1. Safety engineering:
1.
2.
3.
+
-
Primary prevention (hazard elimination)
Safety barriers
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
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.”
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,…)
In the Real World...
•
•
•
•
Climate change
Aging population
High energy prices
Increased life-expectancy (longer healthier
years)
The UN Millennium Development
1.
2.
3.
4.
5.
Eradicate extreme poverty and hunger.
Achieve universal primary education.
Promote gender equality and empower women.
Reduce child mortality.
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.
86
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!
88: Recommended Reading
1. Schoemaker, Paul (1995). “Scenario Planning: A Tool
for Strategic Thinking”, Sloan Management Review 36,
25-40.
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.
6. Hendricks, V. et al. (2011). The Routledge Companion
to Philosophy of Technology, Routledge.