Reference class forecasting,
or comparison class forecasting, is the method of predicting the future,
through looking at similar past situations and their outcomes.
Reference class forecasting predicts
the outcome of a planned action based on actual outcomes in a reference class
of similar actions to that being forecast. The theories behind reference class
forecasting were developed by Daniel
Kahneman and Amos Tversky. The theoretical work helped Kahneman win
the Nobel Prize in Economics.
Kahneman and Tversky found that human judgment is generally
optimistic due to overconfidence and insufficient consideration of
distributional information about outcomes. Therefore, people tend to
underestimate the costs, completion times, and risks of planned actions,
whereas they tend to overestimate the benefits of those same actions. Such
error is caused by actors taking an "inside view," where focus
is on the constituents of the specific planned action instead of on the actual outcomes
of similar ventures that have already been completed.
Kahneman and Tversky concluded that
disregard of distributional information, that is, risk, is perhaps the major
source of error in forecasting. On that basis they recommended that forecasters
"should therefore make every effort to frame the forecasting problem so as
to facilitate utilizing all the distributional information that is
available". Using distributional information from previous ventures
similar to the one being forecast is called taking an "outside view".
Reference class forecasting is a method for taking an outside view on planned
actions.
Reference class forecasting for a
specific project involves the following three steps:
- Identify a reference class of past, similar projects.
- Establish a probability distribution for the selected reference class for the parameter that is being forecast.
- Compare the specific project with the reference class distribution, in order to establish the most likely outcome for the specific project.
Whereas Kahneman and Tversky
developed the theories of reference class forecasting, Flyvbjerg and
COWI (2004)
developed the method for its practical use in policy and planning. The first
instance of reference class forecasting in practice is described in Flyvbjerg
(2006). This was a forecast carried out in 2004 by the UK government of the
projected capital costs for an extension of Edinburgh
Trams. The promoter's forecast estimated a cost of £255 million. Taking all
available distributional information into account, based on a reference class
of comparable rail projects, the reference class forecast estimated a cost of
£320 million. A report issued in August 2011 estimated that the final cost of
the yet unfinished project would be over £1 billion, for a shorter tram line
than the proposed Line 2.
Since the Edinburgh forecast,
reference class forecasting has been applied to numerous other projects in the
UK, including the £15 (US$29) billion Crossrail project
in London. After 2004, The Netherlands, Denmark, and Switzerland have also
implemented various types of reference class forecasting.
Before this, in 2001 (updated in
2006), AACE International (the Association for the
Advancement of Cost Engineering) included Estimate Validation as a distinct
step in the recommended practice of Cost Estimating (Estimate Validation is
equivalent to Reference class forecasting in that it calls for separate
empirical-based evaluations to benchmark the base estimate):
The estimate should be benchmarked
or validated against or compared to historical experience and/or past estimates
of the enterprise and of competitive enterprises to check its appropriateness,
competitiveness, and to identify improvement opportunities...Validation
examines the estimate from a different perspective and using different metrics
than are used in estimate preparation.
In the process industries (e.g.,
oil and gas, chemicals, mining, energy, etc. which tend to dominate AACE's
membership), benchmarking (i.e., "outside view") of project cost
estimates against the historical costs of completed projects of similar types,
including probabilistic information, has a long history.
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