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Cultural differences in futures thinking

June 13, 2011

Why is this important?
Futures thinking is increasingly being used to develop and test policies by governments world wide. While the pressures of voters’ perceptions and beliefs from their past experience remain at the forefront, in a number of policy areas such as pensions, health care and social policy, the need to create a system fit for purpose in the future is clear.

In a nutshell
While researching for an article to be published in Futures, I encountered some interesting data on “Comparing foresight styles in six world regions”[1].

They analysed almost 2,000 foresight activities reported over the last decade and found significant differences in approach. Literature Review was the most favoured approach in Europe, while Asia and North America favoured Expert Panels. Scenarios were more widely used in Asia and Europe than in North America. Delphi did not appear on the top ten list in either Europe or North America, but was widely used elsewhere.

The authors discuss why this might be: it could be due to societal cultural factors, or the relative maturity of foresight itself allowing for a different paradigm, the Futures Workshop, to be significant in N W and E Europe and North America but less used elsewhere.

What difference might this make?

 1.    Delphi forecasting methodology

Delphi is named for the famous Delphic Oracle who was consulted about the future by supplicants in Ancient Greece. It is the most widely used forecasting technique for technology availability. The methodology involves iteration among experts until they agree on a forecast. This can be done electronically, though it was also extensively used by the Japanese during their Foresight exercises, where they used a mixture of meetings and correspondence through a facilitator. The role of the facilitator is to set the time scale for responses, to précis the consensus and highlight outliers and reasons.

A review of accuracy of outcomes was undertaken after 25 years of Japanese Foresight projects using Delphi processes. What they found was that accuracy was better when a wider range of subject experts were included in the process. So for instance, if the subject was the future of surface chemistry, the best result came from consulting surface chemists, together with chemists from other branches, plus chemical engineers, physicists, biologists, economists, and mathematicians, as is discussed in Ben Martin’s book, Research Foresight: Priority setting in Science[2]. The reason for this is that changes in a domain come from breakthroughs or discoveries in neighbouring domains and these are often not visible to people in the core domain.

And it is also worth remembering that adoption of technology often takes paths surprising to the technologist – as in the take up of text messaging. And now that people are mobile, demographic forecasts wich may be globally accurate are much more uncertain in any specific geography.

Do the same guidelines apply to forecasting in other domains? While Delphi is mostly used for scientific forecasting, could it give good results in the social sciences? Would consulting a wide range of experts provide good forecasts?

The best evidence I have seen on this is from a study covering regional forecasting exercises from hundreds of experts on dozens of countries, covering topics as wide ranging as transition to democracy, economic growth, interstate violence and nuclear proliferation. What it found was that formal economic models did best of all in forecasting across this range of social, economic and political data, Mindless competition (including chimpanzees selecting balls from a bag) did quite well.

People with all backgrounds and types of expertise did considerably worse than mindless competition.

This suggests that Delphi methods should be used sparingly outside expert domains. In expert domains, for instance in determining a response to a crisis event, Delphi is very powerful in bringing the knowledge of experts into an accessible form.

2.    Forecasting Black Swans

The ability to anticipate high-impact, hard-to-predict, and rare events beyond the realm of normal expectations (Black Swans)[3] is rare, and is often found in science fiction. The ability to anticipate these extreme outliers is more frequent than  the ability of organisations to prioritise their planning: for instance – how much planning should an organisation do for the possibility (foreseen in a movie) of a plane crashing into the World Trade Centre in New York?

Futures Workshops are probably the most likely format for a group of people to “create” Black Swans.

3.    Scenarios

One of the best definitions of a scenario is by Michael Porter[4]:

“An internally consistent view of what the future might be, not a forecast but one possible future outcome.”

Scenario planning is a set of processes for creating several scenarios or mental models and using them to aid decision making. They provide a mechanism for understanding complex systems: scenarios provide a structure for tying together topical intelligence – from the front end of the organisation – with structural and strategic work. By sharing information from horizon scanning, forecasting and scenario exercises with the eyes and ears of the organisation, intelligence is made accessible to the organisation. The importance of this is growing as large organisation struggle to implement strategy.

Most uses of scenarios relate to a need to:

  • stimulate debate about choices;
  • develop strategies resilient against several futures;
  • test business plans against different futures;
  • try to anticipate futures as an aid to decision making.

This approach is typically able to bring forward the decision making time of the organisation, through being able to anticipate or spot changes more quickly. Whereas forecasts adopted by a group tend to get locked in, so that disconfirming evidence is ignored, scenarios provide a context for seeing weak signals.


  1.  While forecasting is essential for many applications, believing forecasts is dangerous unless trends are monitored.
  2. Workshop mode is best for looking for “Expected Surprise”
  3. Scenarios provide a framework for decision making that recognises that the world is complex.

To discuss any of these ideas further, contact

[1] Michael Keenan and Rafael Popper in Foresight, volume 10, 6, pp16-38, 2008

[2] Martin, B. R. and Irvine, J., Research Foresight: Priority Setting in Science, Pinter. page 154, 1989

[3] Taleb, N. N., ‘The Black Swan’, Random House, 2007

 [4] Michael Porter, “Competitive Advantage”, Free Press New York, 1985

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