Although the name was inspired by the ‘Oracle of Delphi’, a high priestess of Ancient Greece, The Delphi method has its roots in military warfare. The method was developed in the United States at the onset of the Cold War, as a way to predict the role that technology would play in future combative events.1 So, unlike the vast majority of theories and methods we encounter in behavioral science, the Delphi method didn’t really come about by way of academic research.
In 1944, General Henry Arnold commissioned a report for the United States Air Corps on technological capabilities that might be deployed by the military in the future. After much trial-and-error of conventional approaches to forecasting, including quantitative models and trend extrapolation, it became apparent that a novel technique was required when forecasting situations that involve yet-to-be-determined parameters. As a result, the American public policy think-tank RAND Corporation, led by Norman Dalkey and Olaf Helmer, developed the Delphi method. In its early applications, the technique was used to investigate the probability and potential effects of future attacks on the United States. Experts made estimates, discussed them, and then estimated again, in a process that would be known as ‘Estimate-Talk-Estimate’. The idea was that opinions would eventually start to converge around the same repeated estimations.
Since then, the Delphi method has been deployed in a wide range of settings, including business, government, medicine, and science.2 While there is great variation in how the technique is used, the general structure of Estimate-Talk-Estimate defines the Delphi method. In policy-making, the Policy Delphi3 is used to generate the most divergent political views on how a major policy issue should be addressed. It’s also been influential in the development of direct democracy and stakeholder engagement, as policymakers increasingly try to involve a wide range of experts in their decision-making.
That said, the Delphi method was not the first technique to advocate for the leveraging of groups in decision-making. Scientists and mathematicians had long observed the benefits of using groups instead of individuals to make decisions. In 1907, Francis Galton (a cousin of Charles Darwin) observed how the average of all the entries in a county fair ‘guess the weight of the ox’ competition proved incredibly accurate – more so than the guesses of most individuals, even farmers and so-called cattle experts. 4 The concept was developed and is known today as the Wisdom of Crowds.