science

Reference class forecasting

Reference class forecasting is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Reference class forecasting rather than just read about it. In short: Reference class forecasting or comparison class forecasting is a method of predicting the future by looking at similar past situations and their outcomes. The theories behind reference class forecasting were developed by Daniel Kahneman and Amos Tversky.

Key takeaways

Reference class forecasting belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Reference class forecasting to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Reference class forecasting from memory before moving on to harder problems.

Reference excerpt

Reference class forecasting or comparison class forecasting is a method of predicting the future by looking at similar past situations and their outcomes. 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. Reference class forecasting is so named as it predicts the outcome of a planned action based on actual outcomes in a reference class of similar actions to that being forecast. Discussion of which reference class to use when forecasting a given situation is known as the reference class problem.

Overview Kahneman and Tversky found that human judgment is generally optimistic due to overconfidence and insufficient consideration of distributional information about outcomes. 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, i.e. 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.

Reference class tennis The reference class problem, also known as reference class tennis, is the discussion of which reference class to use when forecasting a given situation. Suppose someone were trying to predict how long it would take to write a psychology textbook. Reference class tennis would involve debating whether we should take the average of all books (closest to an outside view), just all textbooks, or just all psychology textbooks (closest to an inside view).

Practical use in policy and planning 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, which was published as an official Guidance Document in June 2004 by the UK Department for Transport. In policy and planning environments, reference class forecasting is strengthened when project teams measure variables that were previously treated as intangible, since quantifying these uncertainties creates more accurate distributions for costs, schedules and expected benefits. This measurable approach reduces optimism bias and supports more accountable planning decisions. The first instance of reference class forecasting in practice is described in Flyvbjerg (2006). This forecast was part of a review of the Edinburgh Tram Line 2 business case, which was carried out in October 2004 by Ove Arup and Partners Scotland. At the time, the project was forecast to cost a total of £320 million, of which £64 million – or 25% – was allocated for contingency. Using the newly implemented reference class forecasting guidelines, Ove Arup and Partners Scotland calculated the 80th percentile value (i.e., 80% likelihood of staying within budget) for total capital costs to be £400 million, which equaled 57% contingency. Similarly, they calculated the 50th percentile value (i.e., 50% likelihood of staying within budget) to be £357 million, which equaled 40% contingency. The review further acknowledged that the reference class forecasts were likely to be too low because the guidelines recommended that the uplifts should be applied at the time of decision to build, which the project had not yet reached, and that the risks therefore would be substantially higher at this early business case stage. On this basis, the review concluded that the forecasted costs could have been underestimated. The Edinburgh Tram Line 2 opened three years late in May 2014 with a final outturn cost of £776 million, which equals £628 million in 2004-prices. 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 2011), 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.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Reference class forecasting

Start with the simplest possible case. Write down what Reference class forecasting claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Reference class forecasting before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Reference class forecasting ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Reference class forecasting

In research: Reference class forecasting appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Reference class forecasting in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Reference class forecasting is common in secondary-school and first-year university syllabi. It links to neighbouring topics Economic forecasting, Evaluation methods, Futures techniques, so understanding it makes those chapters shorter.
In everyday life: Look for Reference class forecasting outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

Ask Teacher Smith questions about this articleOpens your AI tutor with a question about “Reference class forecasting” →

Affiliate

Preply — study more efficiently by working with a personal tutor. 50% off.

How to study Reference class forecasting in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Reference class forecasting means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Reference class forecasting out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Reference class forecasting in simple terms?

Why does Reference class forecasting matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Reference class forecasting?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Reference class forecasting.