Transcript Slide 1

The Second Oldest Profession
"Those who have knowledge, don't predict.
Those who predict, don't have knowledge. "
--Lao Tzu, 6th Century BC Chinese Poet
How old is it?
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I-CHING---- a “Chinese Oracle”
created more than 4000 years ago.
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the world's earliest known intuitive
decision-making system.
The most intricate numerically-based
oracle ever devised—
“Ying vs. Yang” 0 and 1.
Oracle of Delphi----the most
successful prediction business in
history
Today
A Multibillion-dollar Industry
Business
Technology
Society
Professional
Economics
Research
Weather
Futurists
Financial Services
Consulting
Population
Fortune-teller
Business Planning
MIS Consulting/Research/Operation/Guide
A Look of MIS “Prediction” Industry
Venture Investors
IT Consultants
IT Managers
IT Strategists
News Media
IT Economists
MIS Researchers
The Reign of Error
$200 billion
in (mostly erroneous) information each year
$4,800 million
revenues from just two IT “consulting” companies in
InformationWeek Top 50
Author
says “these experts whose advice we pay handsomely
for routinely fail to predict the major events that shape our
world…”
Somebody says “ forecasting is the art of saying what will
happen … … and then explaining why it didn't! "
Future Imperfect
Determinism
Chaos & Complexity
Theories
Situational Bias
Chaos & Complexity Theories in MIS:
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Decision-making process in information systems
Forecasting… forecasting? (e.g. stock market)
Complex adaptive information systems
Semi-Confusing Information systems in changing
environments
Voodoo Economics
Governments make decisions based on faulty forecasts.
Decision makers rely on faulty technology forecasts.
“Dirty
(faulty) data can jeopardize your CRM”
Emerging technology for SCM:
Collaborative Planning, Forecasting & Replenishment
Technology (CPFR)
The Last of the Tooth Fairies
Therefore,
just because we cannot predict it does not mean we
can ignore the future.
We
need to learn how to pluck the gold nuggets of advice.
"It is far better to foresee even without certainty than not to
foresee at all. "
------ Henri Poincare in The Foundations of Science
A pragmatic remark from one of the foundation builders of chaos theory
Chaos and Complexity
"Prediction is very difficult,
especially if it's about the future."
--Nils Bohr, Nobel Laureate in Physics
Examples of Chaos and Complexity
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Weather
Severe Storm Warnings
 Agriculture Planning
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Economics
Government Policy – Federal Reserve
 Elections – Candidate Platform
 Most Investments
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Forecast Reliability
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Weather
Forecasts with 10-14 day lead time are
reasonably accurate
 Distant forecasts impossible to determine
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Economic
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On average not much better that chance
Naïve Forecasting
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Assume No Change from Normal Levels
Economics
Highly volatile areas – Naïve better
 Highly stable – Forecasters better
 Middle – Even
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Weather – Follows Seasonal Norms
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Naïve forecast is close to 90% correct – better
than almanacs
Chaotic Weather
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Chaos Amplifies Mistakes
Chaos Has No Starting Point
Based on Proven Science
Chaos Theory
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Variability within bounds
Chaotic Systems are
Predictable*
*Fine Print: Accuracy decreases
with lead time.
Complex Economy
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No Natural Laws Governing Behaviors
Cannot be Dissected into Component Parts
Highly Connected with External Forces
No Fixed Cycles
Complex Systems are NOT
Predictable*
*Fine Print: Your broker who says it
is will be discussed later in the
presentation
MIS Application
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Naive Forecasting
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Chaotic Systems
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Explore and understand chaotic problems
Fuzzy logic, behavioral analysis
Complex Systems
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Decent reliability, mush less cost
When in doubt, tomorrow will be the same as today
Be aware, don’t predict
Informed Decisions
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Department policies
Legislative procedures
Appropriate Models, Methods,
and Methodologies
"Wall Street indices predicted nine out
of the last five recessions ! "
--Paul A. Samuelson in Newsweek,
Science and Stocks, 19 Sep. 1966.
Background
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Prediction of stock market has existed since
its creation (400 years)
Payoff is huge
Stock market predictions inaccurate
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Complexity, chaos, speed
Fundamentalists –vs- technicians
Random walk theory, EMH, etc.
Example 1 – 15 minutes of fame
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Roger Babson – Crash of 1929
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Joseph Granville – Granville Market Newsletter
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Psuedoscientific market analysis
Market responded to his news
Robert Precther –Elliott Wave Theorist Newsletter
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“Chicken Little” predictions
Downfall came after 1987 due to continued gloom and doom predictions
Elaine Garzarelli – Black Monday (1987)
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Model simplistic; future predictions inaccurate
Huge reputation
Lessons Learned
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Appropriately Use models
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Simplistic models can’t account for everything
Use informed, but cautious decision making
Communication
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How do you get the word out?
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News letter, to the media
Its all about presentation
Prediction is a risky business
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Being a market guru is a short lived honor
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Repeat performances are rare
Shotgun approach - If you predict a lot you are likely to hit it right once
Making predictions made the individual’s career high risk
Lessons Applied
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Appropriately Use models
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Simplistic models can’t account for everything
Use informed, but cautious decision making
Communication
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How do you get the word out?
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Conferences, journals, workshops
Can you move your research area?
Its all about presentation
Prediction is a risky business (especially in MIS)
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Being a guru in an area is a short lived honor
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Especially if tied to technology
Shotgun approach - If you predict a lot you are likely to hit it right once
Making predictions showcases your credibility
Focus on today with possibilities of tomorrow
Example 2 - Peter Lynch
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Managed Fidelity’s Magellan Fund 1977-1990
Increased 2700% during his management
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Philosophy
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“I don’t believe in predicting markets”
Takes a systematic approach in areas that aren’t
saturated
Evaluates a lot of companies
Lessons Learned
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Choose an appropriate methodology
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Understand the Saturation Principle
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Doesn’t pay attention to the overall market
Hedge your risk (low risk strategy with high reward payoff)
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Stay out of the known zone
Boring names; disagreeable things
Carve your niche
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He works through bottom up fundamental analysis of individual
businesses
Use various methods to analyze the “unknown” problems
Have several stocks that are conservative, have a few that are
risky. The risky ones will make a big impact, but are minimized by
the conservative picks.
Work hard
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The person that turns over the most rocks wins the game
Lessons Applied
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Choose an appropriate methodology
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Understand the Saturation Principle
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Understand the principles of the entire field, but focus on a specific area
that intrigues you
Hedge your risk (low risk strategy with high reward payoff)
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Is the known zone of research saturated? Look at “unkown” or little
known problems
Boring names; disagreeable things might be a hotbed for publications
Carve your niche
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Establish a concrete sound methodology appropriate for your research
field
Use various methods to analyze the “unknown” problems
Have several projects that are mainstream (conservative), but dabble with
some revolutionary (high risk) ideas.
Work hard
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The person that turns over the most rocks wins the game
Science Fact and Fiction
and
The Futurists
“The illusion of knowing what’s going to
happen is worse than not knowing”
– James Utterback
Background
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Seek to predict technology trends and their
impact on society.
Seek to predict the future of society in
general.
Motivation for Technology
Forecasting
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Gain competitive advantages
Identify business opportunities
Allocate scarce resources appropriately
Pure entertainment value (e.g., science
fiction books)
Example 1 – Technology Forecasting
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Japan’s Ministry of International Trade and
Industry
Long range forecasting
 Panel of 3000 scientists, engineers and experts
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Study revealed that 75% of their predictions
were wrong.
Example 2 – Technology Forecasting
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Microsoft’s reaction to the Internet
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“The Internet? We are not interested in it.” – Bill
Gates, 1993
“Sometimes we do get taken by surprise. For
example, when the Internet came along, we had
it as a fifth or sixth priority.” – Bill Gates, 1998
Pros/Cons of Techniques
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Technological trends take uncertain twists
that make prediction difficult.
80% of new products never see commercial
success.
Change can be rapid or take a long time.
Pros/Cons of Techniques
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Social predictions tend to be wrong and are
subject to situational biases, wishful
thinking and political agendas.
Society is a complex system that evolves
and cannot be predicted.
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“Sociology is the science with the greatest
number of methods and the least results”
– Henri Poincaré.
Lessons Learned / Application
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"The herd instinct among forecasters makes
sheep look like independent thinkers. "
– Edgar R. Fiedler in The Three Rs of
Economic Forecasting-Irrational, Irrelevant
and Irreverent , June 1977.
More Lessons Learned / Application
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Must be critical thinkers and avoid the herd
mentality.
Develop sound theories that can be applied
to various technologies.
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“Technology is probably not in your theory”.
– Dr. Bob Briggs
More Lessons Learned / Application
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“In times of change learners inherit the
earth, while the learned find themselves
beautifully equipped to deal with a world
that no longer exists.”
– Eric Hoffer
Check the “Unchecked
Population”
“The reason why population is hard to
predict is because when I left my
home country the population dropped
by 10 percent."
-- My 250-Pound Neighbor
Background
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Human life expectancy
From 25 to 47, took 200,000 years
 From 47 to 77 (in developed countries), took
100 years
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Human population: over 5 billion people
Questions:
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Can we feed us all? For how long?
Is Global Famine Unavoidable?
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“I call the disaster the Time of Famines and
I say that the Time of Famine will be upon
us by 1975.”
– Famine – 1975!, by William and Paul Paddock,
1967
Malthus’ Prediction
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“Population, when unchecked, increases in
a geometrical ratio [while]… subsistence
increases only in an arithmetical ratio….
Famine seems the be the last, the most
dreadful resource of nature.”
Lessons Learned
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Never ignore major factors
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Underestimated the productivity of modern
agriculture, the “green revolution”
Population can be controlled via different
ways
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Birth control
Real Challenges to Predict
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Future births, “the unborn”
Migration is driven by complex
circumstances
Economic hardship
 Civil war
 Governments’ immigration policies
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Population Predictability
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Twenty-year population forecasts could be
accurate
The bigger the target, the better the
accuracy
Lessons Learned
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Start with simpler situations
Focus on specific area first
Solving something is better than solving
nothing
Corporate Chaos
“The most effective style to fight is no style."
-- Jin Yong, Famous Chinese Martial-Art
Novelist, …… and Bruce Lee
Background
70s, GE’s elite group of 193 planners
 70s, Eastern Europe’s planning
economy
 From early 50s to late 70s, China’s
planning economy
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Did Long-Term Planning Really Help?
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80s, Japan’s intrusion to Western markets
with superior, low-cost products
Crash of telecommunication industry, e.g.,
Nortel, and Santera
Why Did That Happen?
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Strong influence of international politics
Dramatic change of social structures
Impact of unpredictable events
Unpredictability of organizations
Lessons Learned
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Self-organization
Empowerment: decentralize the decision
making procedure
 Guiding principles: set up basic rules
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More Lessons Learned
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Go with the short-term benefit: let the cash
flow
Natural reflexes: no style is better than any
style; autonomic computing
The Certainty of Living in an
Uncertain World
“Computers in the future may weigh
no more than 1.5 tons.”
-Popular Mechanics (1949)
Thinking Critically
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Credibility Assessment:
Is the forecast based on hard science?
 How sound are the methods used to make the
projection?
 Does the forecaster have credible credentials?
 Does the forecaster have a proved track record?
 To what extent is my belief in a particular
forecast influenced by my own personal beliefs
and wishful thinking?
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Scientific Method
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Laws of Nature?
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Physics, Chemistry, Mathematics
Behavior of Social Systems?
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Stock Market, Political Events
Soundness of Methods
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Extending Trends
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Moore’s Law, Global Warming
Cyclic Behavior
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Stock Market Crashes, Earthquakes
Forecaster Credentials
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Physical credentials
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Paper credentials
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Appearance, Conviction
Institution, resume
Hyped-up credentials
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Role of the Media
Forecaster Track Records
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Lucky Guess?
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Spray technique
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Vagueness
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Million guesses, one has to hit
Nostradamus
A lie?
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Revisionist history
Biases
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What we want to hear?
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Similar imaginings
Barnum effect
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Something for everyone
Questions?
"Those who have knowledge, don't predict.
Those who predict, don't have knowledge. "
--Lao Tzu, 6th Century BC Chinese Poet