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The time prediction and planning capacity of the human race is particularly evident in some of the early great constructions. An excellent example is the building of the Great Pyramid of Giza, around 4500 years ago. We do not know much about the methods they used to predict the time needed and how they managed to finish the pyramid before the pharaoh’s death. Most likely, their time and resource predictions were influenced by experience from building previous pyramids. However, even if they could use previous experience, they would have to adjust the predictions for differences in the pyramid’s size and location and the availability of resources. This is not an easy task, even for today’s construction planners, with better tools and more historical data.

We associate the human memory with the past, because memories are established in the past. While it is true that our memories are about things in the past, their purpose is to help us predict and manage the future. The recollection of a positive experience makes us approach similar events and an unfortunate encounter with a hot cooking plate helps us avoid harm in the future. Thus, we learn from experience and update our memory, consciously and unconsciously, for the sake of the future. The future is, however, seldom or never identical to the past and our brain has developed extreme flexibility in the way it handles memories. We can, for example, use our memories to simulate future outcomes before they have happened. This requires a high degree of flexibility and malleability of memories.

A project manager states that a project will require 432 hours. Your friend sends you a text message saying that he will be at your place in 12 minutes. The precision of these time predictions is most likely misleading when interpreted according to the rules of significant digits, where the number of trailing non-zero values indicates the intended accuracy. For example, 432 hours and 12 minutes should indicate that the time prediction error is ±1 hour and ±1 minute—not very likely for most types of project or arrival time predictions.

We usually say that a time prediction is overoptimistic when the actual time usage is greater than the predicted time usage. This does not mean that an optimistic or overoptimistic view on time usage was the cause of the too low time prediction. Lack of knowledge, miscalculation during the prediction process, and bad luck in the execution of the project are examples of alternative reasons for too low time predictions. Describing too low, or overoptimistic, time predictions as caused by overoptimism, in the rose-coloured glasses sense, not only is incorrect but may also stop us from seeking other explanations of time overrun besides overoptimism (Roy in Front Psychol 5:624, 2014, []).

To a larger extent than we like to think, our judgements and decisions are affected by irrelevant factors. Fortunately, there are patterns to our irrationality. We are, in a sense, []. These patterns of irrationality are what we call judgement and decision biases. This chapter describes some of the biases relevant to understanding when and why we make systematic time prediction errors. Better knowledge about the biases and fallacies may help us become better at designing time prediction processes and avoiding situations and information that mislead us.

There is a degree of uncertainty in all time predictions. We would not even use the term if there were no uncertainty in our statements about the usage of time for future tasks. A realistic view of this uncertainty is essential, as illustrated in the following real-life case.

The prominent approach for reducing a problem’s complexity is to decompose it into less complex subproblems, solve each of these, and then aggregate the subsolutions into an overall solution. In time prediction contexts, this approach is typically the basis of what has been referred to as the bottom-up method, the activity-based method, or predictions based on a work breakdown structure. Generally, across a range of domains, decomposition has been found to improve judgement quality and increase prediction accuracy (Armstrong et al in J Bus Res 68:1717–1731, 2015 []). In the domain of time predictions, however, there are also situations in which decomposition leads to overoptimistic and less accurate judgements (Jørgensen in Inf Softw Technol 46:3–16, 2004 []).

There are many time prediction methods and principles. How should we choose between them? Time prediction methods have advantages and disadvantages that depend on the situation, but there is a scarcity of useful guidelines on how to select time prediction methods. We attempt to provide some guidance in Fig. 8.1, with more detailed explanations of the questions and the time prediction method selection advices in the subsequent paragraphs.

Below is a list of elements describing how to get low time predictions, ranked by what we believe is their magnitude of impact. The elements are likely additive, where combinations of more elements may further lower the time predictions. However, the effect of adding biases has not been studied much, so we do not know how bad things can get in such combinations. The list is meant to be a about how the person requesting a time prediction can easily contribute to overoptimism. That person could be you, for example, in the role of the client or project manager or when asking a carpenter about the time needed to remove an interior wall in your home. If, in spite of our warning, you include one or more of the elements described here to manipulate other people’s time predictions, you have only yourself to blame for low work quality, frustrated coworkers, increased coordination costs, and missed deadlines.