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Analytical vs Judgmental Foresight and Scenarios
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Analytical vs Judgmental Foresight and Scenarios



Presentation to Futures Analysts Network (FAN), London, May 2009. Description of the limits of quantitative analysis and best of alternatives, including insights from Future Savvy, (Adam Gordon, ...

Presentation to Futures Analysts Network (FAN), London, May 2009. Description of the limits of quantitative analysis and best of alternatives, including insights from Future Savvy, (Adam Gordon, Amacom Press, 2009)



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Analytical vs Judgmental Foresight and Scenarios Analytical vs Judgmental Foresight and Scenarios Presentation Transcript

  • 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