Delphi Technique

Conceptual Overview

Uncertainty in environmental contexts can be reduced to risk, structural, and unknowable components. In futures thinking, risk can be predicted and so is handled using a number of proven aids, for example, forecasting, trend impact, and cross-impact analysis. Structural interventions, such as natural disasters and technological upheavals, do not lend themselves well to probabilistic modeling as the underlying nature of a phenomenon is changed fundamentally. Here, the harnessing of expert opinion through the Delphi or morphological analysis is a fruitful way to inform future prospecting. Unknowable interventions cannot be predicted but only imagined through the lenses of some scenario-thinking techniques.

After WWII, the RAND Corporation was formed to strengthen the link between military operations and technological development. The limitations of probabilistic forecasting techniques soon became apparent as the research teams tried to tackle complex problems with numbers before any precise scientific laws had been established on which to build their modeling assumptions. Hence, in the early 1950s, Project Delphi investigated the most efficient and reliable use of groups of experts. Later, two RAND researchers, Olaf Helmer and Nicholas Rescher, published an article on “The Epistemology of the Inexact Sciences” in which they argued that because there were areas in which science had yet to develop its laws and boundary conditions, expert opinion was a vital and legitimate source of data. Incidentally, these founders did not like the Greek imagery portrayed by the name Delphi. Nevertheless, the name stuck.

Consequently, researchers at RAND developed a Delphi technique based on a Hegelian dialectical inquiry approach comprising the following: thesis, whereby an opinion is formed on a complex topic; antithesis, whereby a conflicting opinion is gathered; and synthesis, whereby a new consensus is established that becomes the new thesis on the topic. Creative thinking and the avoidance of groupthink are crucial aspects of the process. Understandably, the method was used first in long-run (e.g., 30-year) technology forecasting on issues like automation, space progress, and weapon systems. Thereafter, its use was extended to business interests like new product market assessment and then to the public good such as health care and education. Its accuracy in fortifying business forecasts seemed exceptional for the time. Shankar Basu and Roger Schroeder claimed that the Delphi technique predicted the sales of a new product during the first 2 years with accuracy of 4% to 5% compared with actual sales. Quantitative methods produced errors of 10% to 15% and traditional unstructured forecast methods had about a 20% error rate.

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