Analytical vs.
Judgmental Foresight
Futures Analysts Meeting
May 20, 2009, London
Adam Gordon

1. Analytical vs. Judgemental foresight
2. Engaging ―the consumer‖ of future works
3. Some insights from Future Savvy.

Uncertainty: Binary view vs. ―Levels‖
1. Low uncertainty. Clear 1 2
view of the future.
A
Dependable outcome
B
2. Limited set of possible
future outcomes, one C
of which will occur
3. Outcomes 3 4
indeterminate, but
bounded in a range
4. A limitless range of
?
possible outcomes.
HUGH COURTNEY ET Al; 2020 FORESIGHT, HBS PRESS, 2001

Cynefin Framework (Snowden)
Relationship between cause & effect:

The 4th Quadrant
Yes/No Impact Extent of impact
uncertain
Exceptions: low impact
Exceptions: high impact
THE FOURTH QUADRANT: A MAP OF THE LIMITS OF STATISTICS [ Edge Foundation – Sept 15, 2008]

The Limits of Modelling
THE FOURTH QUADRANT: A MAP OF THE LIMITS OF STATISTICS [ Edge Foundation – Sept 15, 2008]

Neil Duncan: Why Can’t We Predict?
New Scientist, Vol. 136, Issue 1841 (October 1992)
1. What factors are relevant?
2. How these factors determine outcomes:
 Shape – the form of the relationship, how X produces Y
 Thresholds – discontinuities incausality of Y by X
 Interactions – effect ofX on Ydepends on the action of
one or more of other factors (Systems Dynamics)
 Lag – Yis affected not by the current value ofX, but by
an earlier or later value.

Implications for predicting
(or otherwise managing uncertainty)
1. Standard statistics, 1 2
market research,
A
projection, financial
analysis (DCF, NPV) B
2. Probability, decision C
trees, prediction
markets, game theory 4
3
3. ―Futuring‖ tools.
Scenario planning
(incorporating other ?
futures approaches)
4. No satisfactory tools

CLEAR ENOUGH vs. UNKNOWN CHANGE
Predictable environments:  Uncertain tech evolution
 Information rich  Uncertain demand for new
 Not prone to tech upheavals products& services
Well established markets  Uncertain performance of
new business models
 Stable players. High barriers
 Unstable macro-economic
 Stable regulatory environment conditions: inflation, interest
Consistent demand rates, currency
 No great social pressure  Shifting:values, morals
preferences, regulations

Scenarios:
Turning analytical foresight on its head
Managers spend their time thinking:
―what are the chances that X or Y will happen?‖
Determine which is most likely and plan for it.
They should be spending their time thinking:
―What if X happens, what will be effects, what
should we do / have done? What if Y happens,
what will be the effects, what should we do /
have done?…

Point prediction
Scenarios
Scenario planning
ANALYSIS

Exploring the cone of
plausible uncertainty
Cone Of plausible uncertainty
Critical variable
?
present
Adam Gordon 12FAN Club, May 2009

The official future
LGA

Not a scenario set:

A testbed for decision-makers:
Stress-testing decisions in environments of tomorrow
How will key
success
factors be
different?
What success
factors will be
key?
The Sixth Sense

Visionary scenarios
Arctic Marine
Navigation
(AMSA)

What‟s under the
hood, and does it
shed any light on
the problem of
forecasting vs
“futuring”?
Why it exists…

1. The limits of quantitative analysis
Understanding what drives up uncertainty
(what drives down predictability.)
Know when we go beyond realm of modelling.
Matching approach to the level of uncertainty
Goldilocks test: Too much certainty? Or too little?

2. Info-data quality, interpretation, bias
Bias-prone contexts recognition:
Sponsored? Self-interest?
Ideology? Single issue?
A. Purpose? All foresight is done for benefit.
Future Aligning vs Future Influencing
B. Editorial oversight bypassed?
Many self-publishing options on the Internet,
forecast escapes peer review.

3. Trend extrapolations
―Power struggle‖ between the forces of
change—drivers and enablers, vs friction
and blockers.
Poor forecast will take a trend at face value. A
better forecast will determine drivers,
enablers, friction and blockers, and their
durability over time.
How will resistance be overcome? Account for
the resources required to achieve this.

4. Complexity
Not steady evolution from present, a
constant rate of change. Critical mass,
trigger points, inflexion points.
No simple progression of one issue while rest
of world stands still. Systems perspective.
The Counter-Trend is always present
Exponential change only at rare times, and
not for long.

5. The Utility Test
Stunning innovation, unprecedented
technology… will only emerge from lab
if it favourably improves consumers’
cost-benefit equation (including the cost
of overcoming legacy systems and of new
necessary complements).
Technophiles have a history of seeing big
change when none is likely and rapid change
when slow is likely.

6. Assumptions, Paradigms,
Groupthink, Zeitgeist
Every forecast makes assumptions.
Assumptions connect inputs to outcomes.
Alternative assumption = alt outcomes.
A forecast only as good as its assumptions.
If the assumptions fail, the forecast will fail.
Good forecasts realise, acknowledge, and specify
their assumptions, argue their pertinence.
Awareness of groupthink and zeitgeist pressures

„1,735,087 will have dementia by 2051‟
Report: LSE & Kings College Inst. of Psychiatry,
for Alzheimer Society (2006).
Extrapolating simple trend 2-3% growth in
dementia; and Delphi forecast.
45 year forecast for future of medicine, holding still:
 Medical knowledge (brain function, aging)
 Evolution of technologies and remedies
 Evolution of service provision / business models
- Assumes steady evolution. Will be inflexion-complexity.
- Quantitative analysis beyond its limits.
- Future-influencing purpose problems.

Adam Gordon
adam.gordon@futuresavvy.net
www.futuresavvy.net
t. +44 (0)292 0257672
m. +44 (0)790 6054848