Summary: The planning fallacy is the tendency to underestimate task-completion times. Real-life examples in public policy may include the construction of the Sydney Opera House and the Big Dig in Boston, both of which ran many years past their planned schedule.

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For most of us, the tendency toward optimism is unavoidable. And it’s unlikely that companies can, or would even want to, remove the organizational pressures that promote optimism. Still, optimism can, and should, be tempered. Simply understanding the sources of overoptimism can help planners challenge assumptions, bring in alternative perspectives, and in general take a balanced view of the future.

But there’s also a more formal way to improve the reliability of forecasts. Companies can introduce into their planning processes an objective forecasting method that counteracts the personal and organizational sources of optimism. We’ll begin our exploration of this approach with an anecdote that illustrates both the traditional mode of forecasting and the suggested alternative.

In 1976, one of us was involved in a project to develop a curriculum for a new subject area for high schools in Israel. The project was conducted by a small team of academics and teachers. When the team had been operating for about a year and had some significant achievements under its belt, its discussions turned to the question of how long the project would take. Everyone on the team was asked to write on a slip of paper the number of months that would be needed to finish the project—defined as having a complete report ready for submission to the Ministry of Education. The estimates ranged from eighteen to thirty months…

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If you don’t want to be late, enumerate: Unpacking reduces the planning fallacy

People tend to underestimate how long it will take to complete tasks. We suggest that one reason people commit this planning fallacy is that they do not naturally “unpack” multifaceted tasks (e.g., writing a manuscript) into subcomponents (completing the literature review, general discussion, references section, etc.) when making predictions. We tested this interpretation by asking participants to estimate how long it would take them to complete one of several tasks: holiday shopping in Experiment 1, “getting ready” for a date in Experiment 2, formatting a document in Experiments 3 and 5, and preparing food in Experiment 4.

Participants prompted to unpack the task provided longer—and, in Experiments 3–4, less biased—estimates of how long the task would take than did participants who did not. Experiment 5 showed that the debiasing influence of unpacking is moderated by task complexity: the more multifaceted the task, the greater the influence of unpacking.

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