The Planning Fallacy and Reference Class Forecasting


The theoretical and methodological foundations of reference class forecasting were first described by Kahneman and Tversky (1979b) and later by Lovallo and Kahneman (2003). Reference class forecasting was originally developed to compensate for the type of cognitive bias that Kahneman and Tversky found in their work on decision making under uncertainty, which won Kahneman the Nobel prize in economics 2002 (Kahneman 1994; Kahneman and Tversky 1979a). This work showed that errors of judgment are often systematic and predictable rather than random, manifesting bias rather than confusion, and that any corrective prescription should reflect this. They also found that many errors of judgment are shared by experts and laypeople alike. Finally they found that errors remain compelling even when one is fully aware of their nature. Thus awareness of a perceptual or cognitive illusion does not by itself produce a more accurate perception of reality, according to Kahneman and Tversky (1979b: 314). Awareness may, however, enable one to identify situations in which the normal faith in one’s impressions must be suspended and in which judgment should be controlled by a more critical evaluation of the evidence. Reference class forecasting is a method for such critical evaluation. Human judgment, including forecasts, are biased. Reference class forecasting is a method for debiasing forecasts.

Kahneman and Tversky (1979a, b) found human judgment to be generally optimistic due to overconfidence and insufficient regard to distributional information. Thus people will underestimate the costs, completion times, and risks of planned actions, whereas they will overestimate the benefits of the same actions. Lovallo and Kahneman (2003: 58) call such common behavior the “planning fallacy” and they argue that it stems from actors taking an “inside view” focusing on the constituents of the specific planned action rather than on the outcomes of similar actions that have already been completed. Kahneman and Tversky (1979b) argue that the prevalent tendency to underweigh or ignore distributional information is perhaps the major source of error in forecasting. “The analysts should therefore make every effort to frame the forecasting problem so as to facilitate utilizing all the distributional information that is available,” say Kahneman and Tversky (1979b: 316). This may be considered the single most important piece of advice regarding how to increase accuracy in forecasting through improved methods. Using such distributional information from other ventures similar to that being forecasted is called taking an “outside view” and it is the cure to the planning fallacy. Reference class forecasting is a method for systematically taking an outside view on planned actions.


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