From Nobel Prize to Project Management: Getting Risks Right

TL;DR

This paper introduces reference class forecasting, a new method based on decision theory to improve accuracy in project risk estimates by using data from comparable projects, addressing biases like optimism and misrepresentation.

Contribution

It presents the first practical application of reference class forecasting for infrastructure project costs, offering a novel approach to mitigate forecasting biases.

Findings

Reference class forecasting improves forecast accuracy.

The method effectively addresses optimism bias.

Practical implementation shows promising results.

Abstract

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk, based on theories of decision making under uncertainty which won the 2002 Nobel prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of optimism bias and strategic misrepresentation. Third, the theoretical basis is presented for a promising new method called "reference class forecasting," which achieves accuracy by basing forecasts on actual performance in a reference class of comparable projects and thereby bypassing both optimism bias and strategic misrepresentation. Fourth, the paper presents the first instance of practical reference class forecasting, which concerns cost forecasts for large transportation infrastructure projects. Finally, potentials for and barriers to reference class forecasting are assessed.