TRADOC OE Dr. Bishop Presentation

UH FUTURES STUDIES: Preparing Foresight Professionals

TRADOC Mad ScientistNewport News VAJanuary 20, 2010

Strategic Foresight

Technological Forecasting

Dr. Peter BishopFutures Studies

University of Houston

Source: Technology Forecast Archive, Battelle,http://www.battelle.org/spotlight/tech_forecast/


Agenda

Change

Forecasting change

Forecasting technological change

Describing the Futures of Technology

Describing the Futures of Technology


Change


Change and Stability

Nothing

changes

everything


Four Basic Concepts

Sources

Times Levels

Rates

ChangeChange


Sources of Change

…but within limits

INBOUNDChange that happens to us

Predict…

OUTBOUNDChange we create ourselves

Control…Anticipate…Intelligence

Influence…Policy


Transactional Environment

Levels of Change

Enterprise

STEEPSTEEPPolitical

EnvironmentalTechnologicalEconomic

Social

Demographic

Cultural


Rates of Change

Continuous change• gradual improvement over long periods

• usually preserves the framework/context

Discontinuous change• sudden change to new levels

• usually destroys the framework/context

• always involves short-term loss


S-Curve

The Real Shape of Change

1

No problem.

2

What is going on here?

3 Whew!


Eras in Information Technology


Past Disruptions

Airlines

Automobiles

Telephones

Television

Health care

Retail

Utilities

The Common ElementDestruction of MonopoliesAppearance of Competition

The Common ElementDestruction of MonopoliesAppearance of Competition

Newspapers

Military

Elections

Schools?


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Estimation errors

Overestimation

Underestimation

Too late

Too soon


Hype Curve

ExpectationExpectation RealityRealityO

vere

stim

ate

Und

eres

timat

eThe media first overestimates the effect of a change.

Then the media underestimates its effect.


Punctuated Equilibrium

TransitionsTransformational, Discontinuous

TransitionsTransformational, Discontinuous

ErasIncremental, Continuous

ErasIncremental, Continuous


Speed

Successive Eras

Running

Riding

Motoring

Flying

Inherent capacity

for perfo

rmance

Law of dim

inishing

returns


x

The Gap

0

1

0

1

Old era

New eraTransitions inevitability

create problems...

…but problems are investments toward a better future.


Wider Broad scope, Context

big picture

Deeper Meaning, drivers, Pattern

behind the details

Longer Change, Future

implications

b


Forecasting Theory


The Predictable Future

Predictability, according to natural law, was one of the

most powerful cornerstones of the scientific revolution --

Newton, Leibniz, Enlightenment So much so that it became the default assumption about

the future for physical science,

social science, and the professions Based on the belief of order,

causality, connectedness, and flow The future as a river, following

one path and leading to a specific point


The Contingent Future

We learn history as a series of events and actions, some with clear causal connections, but often as the result of surprising contingencies.

In last century, the contingencies and uncertainties inherent even in natural phenomena have become even more apparent – Stochastic processes -- Galton– Quantum mechanics -- Bohr, Heisenberg– Biological evolution -- Gould– Chaos theory -- Lorenz– Complexity science -- von Neuman, Wolfram, Kauffman

Based on the dominance of chance and uncertainty over determinism and predictability

The future as a dice game


The Chosen Future

The religious, economic and political traditions of Western society place primary responsibility for the future on individuals—on their intentions and their actions.

Examples –– Religion claims that we will be rewarded and

punished according to our actions; the law also holds individuals responsible for their actions.

– Individuals in a market economy must provide for themselves and their families.

– When something goes wrong, we look for someone to blame; when something goes well, we hand out awards.

Based on the dominance of human agency and free will over the forces of determinism and chance

The future as a blueprint


Inference Model

Evidence

Evidence

EvidenceEvidence

Evidence

Assumption Assumption Assumption

Unobservable

Observable


Knowing Different Times

History

VisionsEvents

Trends

IssuesImages

DrawingsWritings

Artifacts

StructuresBones

Assumptions

Forecast

Assumptions

Present

Evidence


Prediction and Forecasting

Prediction

Physical evidence

Strong assumptions

Unitary phenomenon

Higher quality inferences

Forecasting

Intangible evidence

Weak assumptions

Multiple possibilities

Lower quality inferences


Framework Forecasting

Dr. Peter Bishop, Futures Studies, University of Houston

Alternative futuresAlternative futures

Information sources• texts• experts• organizations• periodicals• websites

Information sources• texts• experts• organizations• periodicals• websites

History• previous eras

separated by events/ discontinuities

• the current “era”beginning with the most recent discontinuities

History• previous eras

separated by events/ discontinuities

• the current “era”beginning with the most recent discontinuities

ImpactsImplications

ImpactsImplications

ImpactsImplications

ImpactsImplications

Response• policy• plans• actions

Response• policy• plans• actions

Dr. Peter Bishop, 2000

Framework Forecasting

Forces of change• ongoing trends• potential events• emerging issues• new ideas

Forces of change• ongoing trends• potential events• emerging issues• new ideas

UncertaintyUncertainty

}

Current conditions• social• technological• economic• environmental• political

Current conditions• social• technological• economic• environmental• political

STAKEHOLDERS

EffectsInformation

Research

Scanning

Leading indicators

Leading indicators

Baseline futureBaseline future


Common Futures Thinking

Futures Forces Thinking Techniques

Expected Constants Definite Historical analogy(baseline) Trends Scientific Extrapolation


Mechanisms of Change – The Baseline

TrendsPlans

TrendsPlans

Constants

Cycles

Trends

Stakeholder Intentions/Plans

Projections

Expected Future Implications


Thinking about the Future

Factors that limit our understanding of the future

• Lack of information“what we don’t know”

• Incorrect theories“what we think we know”

• Unexamined assumptions“what we believe we know”


Assumptions in Trend Extrapolation

Business School Enrollment, UH-Clear LakeDeseasonalized

80

85

90

95

100

105

110

115

120

80-1 81-1 82-1 83-1 84-1 85-1 86-1 87-1 88-1 89-1 90-1 91-1 92-1 93-1 94-1 95-1 96-1 97-1 98-1 99-1 00-1 01-1 02-1 03-1 04-1


Making the call

Assumptions resolve uncertainty…

…but resolving uncertainty may not be

the right thing to do.

Resolve as much uncertainty as you can, but no more.Resolve as much uncertainty as you can, but no more.

Katrina

Rita


Sources of Uncertainty

Insufficient or incorrect information

Insufficient or incorrect understanding

Inherently unpredictable systems (chaos)

Inherently critical systems

Inherently novel, creativeself-organizing systems

Human choice

???????

??

ChaosCriticality

ComplexityChoice


Limit

of P

lausib

ility

AlternativeFutures

Limit of Plausibility

The Cone of Plausibility

Past

The Future is many,not one.

The Future is many,not one.

Source: Charles Taylor, Army War College

Implications

Baseline

Present


Another Type of Futures Thinking



Plausible Discontinuities Speculative Scenarios(alternative) Surprises Imaginative Simulation


Mechanisms of Change -- Alternatives

DiscontinuitiesDiscontinuities

TrendsTrends Trend reversalsCounter-trends

Unfulfilled intentionsFailed strategies

Potential eventsWildcards

Unresolved issues

Novel ideasProposals

ScenariosImplications


The “Trick” of Scenarios

ImpactImpact

High

HighModerateLow

Low

ProbabilityProbability

U n i m p o r t a n t

Expec

ted

Scenar

io

Wild

card


Words and ProbabilitiesTerm… Means…

Impossible = 0%

Possible > 0%

Plausible >> 0%

Probable, likely > 50%

Certain = 100%

Most certain = ~0% or ~100%

Most likely (expected, surprise-free, official, baseline)

= more likely than any other, but is usually << 50%

Most uncertain = ~50%

Wildcard = ~0%, but with high impact if it does occur


The Language of Plausibility

Indicative

Will

Must

Should

Subjunctive

May

Might

Could

Past Present Future


The Real Purpose of Forecasting

Surprises


Two ways to the top

...rather than in a big way all at once!

...rather than in a big way all at once!

Being surprised in little ways over a long

time...

Being surprised in little ways over a long

time...


Forecasting -- Two Approaches

Short-term horizon (immediate) Long-term horizon

Single domain (specialization) Many domains

Few factors (reductionism) Many factors

Focus on continuity (models) Focus on discontinuity

Hide uncertainty (assumptions) Reveal uncertainty

Single forecast (predictions) Alternative forecasts

Quantitative techniques Qualitative techniques(math, computers)

Traditional Foresight


Technological Change


The Third Wave

Era 1

Era 1Hunting/Gathering

Agricultural

Industrial

Information


Other Waves

Era 1

Steam

Electricity

Petroleum

Information

Rail


Digital Technology


The Automobile Economy

Exxon, Mobil, GM, Ford Interstates, parking lots Suburbs, inner cities, Detroit Commuting, vacations Drive-ins, radio Patrol car, get-away car Independence, status, rite of passage Traffic deaths, pollution

Can you GO without it?Can you GO without it?


Changing of the Guard

Energy Society Oil Automobiles Aircraft Petrochemicals

Information Society Information

Communication

Finances

Media, Entertainment

Retail


The Reduction of...The Elimination of...


Dematerializing the World

Information from Paper

Communication from Mail

Libraries from Books

Money from Currency

Selling from Stores

Entertainment from Theaters

Meetings from Travel

“All things solid melt into air.” --Karl Marx and Frederick Engels, 1848

“All things solid melt into air.” --Karl Marx and Frederick Engels, 1848


MolecularTechnology

Bio-Nano-Cogno


Bio-Technology

Domestication of plants and animals (breeding, grafting) Genetic markers

– Werner Arber, et. al. – restriction enzymes, cuts the DNA (late 1960s) Recombinant DNA

– Stanley Cohen, Herbert Boyer – recombinant DNA, inserts new DNA (1978) Nuclear Transfer

– Robert Briggs, Thomas King – cloned tadpoles (1952)– Ian Wilmut – cloned sheep, Dolly (1996)

Stem cells– Hayflick limit – cells die after certain number of reproductions (1965)– Teratocarcinoma – first cells that never died or differentiated (1964)– M Evans, M Kaufmann, G Martin – stem cells from mouse embryo (1981)– James Thompson -- stem cells from human embryo (1998)

Neuroscience– Functional Magnetic Resonance Imaging (fMRI, 1990)– Neuron-chip communication


New Lines of Research Genomics Proteomics Combinatorial and ADMET* chemistry Assay development Computer modeling Bioinformatics Gene shuffling High throughput machinery Pharming

Source: The Biotech Investor’s Bible, George Wolff, 2001.

*Absorption-Distribution-Excretion- Metabolism-Toxicity


But Why Stop at Medicine?

Agriculture Higher yields

Pest-resistant

Herbicide resistant

Lower water needs

Nitrogen fixation

More nutritious

Industry Materials

Manufacturing

Energy

Waste management

Computing


Nano Applications

Air Quality Medicine Chemical Sensors Consumer Products Energy Electronics Food Molecular Manufacturing MEMS Space Water Quality


Neuroscience – Unanswered Questions

Consciousness Perception Learning, memory Neuroplasticity Cognition, decisions Language Development, evolution Diseases


Energy?


Source: ASPO-USA; created by OilPoster.org

One possible scenario…


Temperature Projections to 2100

Source: IPCC Data Distribution Center, http://ipcc-data.org/


Abrupt Climate Change

Meridional Overturning Circulation (North Atlantic Conveyer) Rapid West Antarctic and/or Greenland Ice Sheet Collapse Volcanoes Methane Hydrate

and/or Permafrost Methane Instability Biogeochemical

(carbon source/sink)

Source: IPCC, 4th Assessment Report, http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter8.pdf


Energy Sources

Fossil Fuels– Coal– Oil– Natural Gas

Nuclear Renewables

– Hydro– Geothermal– Wind– Biofuels

» Crops» Cellulosic» Algae

– Solar» Solar thermal» Photovoltaic (PV)

– Tidal/wave


Commerce, Comfort &

Connectivity

Emissions Control

Crude Oil Natural Gas Coal

Bio-fuels

Wind, Tides, Currents,

Waves, Solar

NuclearEngineered

Crops

ICEFuel Cell

Gasoline & Diesel

Gasification

Combined Cycle

Chemical Separation

Syngas

Hydrogen

Fischer TropschRefining

CO2 Flue Gas

PC

Sequestration

Nuclear Waste Transportation

Bio-refiningChemical

Processing

Transmission

Batteries

Transmission

Personal Mobility

A Complex Energy Picture

Societal Needs


Getting to Work


Mechanisms of Change -- Preferred

ChoiceChoice

DiscontinuityDiscontinuity

TrendTrend


Three Types of Futures Thinking



Plausible Discontinuities Speculative Scenarios(alternative) Surprises Imaginative Simulation

Preferable Choices Visionary Visioning(visionary) Images Empowered Planning


Language of the Future

STEEP

Demographic Technological Economic Environmental

Political Cultural

STEEP

Demographic Technological Economic Environmental

Political Cultural

Expectations

TrendsPlans

Expectations

TrendsPlans

PreferencesValuesVisionGoals

StrategiesActions

PreferencesValuesVisionGoals

StrategiesActions

ScenariosTrend reversals

Unfulfilled plansPotential events

Unresolved issuesNew ideas

ScenariosTrend reversals

Unfulfilled plansPotential events

Unresolved issuesNew ideas


A Final Word

Learning faster than your competitors is the only sustainable competitive advantage in an environment of rapid innovation and change.

-- Arie de Geus, former Director Corporate Planning Royal Dutch Shell Group

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TRADOC OE Dr. Bishop Presentation

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