The main emphasis of the book is not on formal (mathematical) models for time predictions, but on judgment-based time predictions. We hope that you will enjoy the book and that your time predictions will improve.
A Prediction Success
Prediction Disasters
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Mental Time Travel
The importance of mental time travel becomes even more apparent when observing those who have lost this ability. The ability to perform mental time travel is therefore not only a condition for good predictions, but also essential to define and experience who we are as people.
How Did You Make that Prediction?
Take home message 1: The main purpose of remembering the past is to enable predictions about the future, including time predictions. Take home message 1: We don't know much about the mental processes that lead to judgment-based time predictions.
Time Predictions Are Everywhere
Additionally, our brains make many time-use-related calculations that we might not classify as time predictions. Interestingly, the students included time predictions, not just of the how long it will take type.
How Good Are We at Predicting Time?
Does this mean that the general impression that people tend to make overly optimistic weather predictions is wrong? For larger projects, however, time forecasts tend to be biased to be too low, with a median time overrun of about 20%.
Precisely Wrong or Roughly Right?
Developer D's time prediction was rated least reliable by 36% of respondents, and developer's competence was rated lowest by 55%. Developer C was ranked as the most reliable time forecaster by 74% of respondents and the most competent developer by 70%.
Communication of Time Predictions
Requesting a time prediction without specifying exactly what is desired can lead to time predictions that represent anything from best-case-averse thinking. Take home message 1: It is often not clear what people mean when they give a weather prediction.
Probability-Based Time Predictions
The most frequently observed value also depends on the granularity of the time use values included in the distribution (decimals can give a different mode than whole minutes). Using the data in Figure 3.1, we would find that a time prediction based on the most likely value (30 minutes) would be within ±5 minutes of the actual time 36% of the time.
Right-Skewed Time Distributions
Small difference between the sum of time predictions and the sum of actual time usage. Which figure probably corresponds to the distribution of the time spent by a few hundred graphic designers doing the same job.
To add time forecasts correctly, we need to take into account the long tail of the distribution of driving time use. When the most likely time forecasts are added, you will get a forecast of the most likely total time usage that is too low.
How to Predict the Mean Time Usage
Take home message 1: The sum of the most probable time use of individual activities is not the same as the most probable use of the total time of the same activities. The forecast, the reference, can be a forecast of the most likely use of time or some other type of time forecast.
How Time Predictions Affect Performance
Take home message 1: Time predictions can affect work, especially when it is very flexible. Time predictions that are too low can also lead to deteriorations in the quality of the work.
Optimism, Overoptimism, and Overoptimistic Predictions
The time predictions of the participants in the first group were, as expected, usually lower than those of the other group. Adding the most likely values of time usage gives time predictions that are too low.
The Bene fi ts of Overoptimism
The Desire to Control Time
So there seems to be a connection between overly optimistic time predictions and a general motivation to keep things under control. Take home message: The motivation to do something quickly and the desire for control usually lead to lower time predictions.
Motivation to Make Accurate Time Usage Predictions
Take home message 1: Rewards for accurate time predictions typically yield higher, but not necessarily more accurate predictions. Take home message 2: It's risky to reward accurate time predictions or penalize overly optimistic time predictions.
Selection Bias
This strategy can eliminate the tendency for over-optimistic time predictions, but can also lead to price manipulations in bidding contexts. Take Home Message 1: Even if people's weather predictions don't tend toward over-optimism, the world still seems to be full of over-optimistic weather predictions.
Deception
Nevertheless, the studies showed that overly optimistic time and cost forecasts can sometimes best be described as lies, deceit or deliberate distortions of the time forecasts. Take home message: we don't know how often and when deception is the main reason for overly optimistic time predictions.
Who Makes the Most Realistic Time Predictions?
In contrast, confidence in the accuracy of a particular time prediction is not a robust indicator of time prediction accuracy [9]. Kelly WE (2000) Conscientiousness and the prediction of task duration: evidence of the role of personality in time prediction.
The Team Scaling Fallacy
Doubling the number of scientific staff not only requires a doubling of the administrative staff, but rather an increase in time. Awareness of the team's scaling error is also important when predicting time based on past time spent on other projects or tasks.
Anchoring
Evidence for the importance of the anchor effect includes findings from randomized controlled field experiments. The strongest effect was found for a low anchor, in the form of a short expected completion time ('the work must be completed within three weeks from the start date').
Sequence Effects
Another explanation is that people start at the anchor value and adjust until they arrive at what they think is a reasonable time prediction. The only safe method to avoid anchoring effects is to avoid exposure to information that can act as a time prediction anchor.
Format Effects
The same format effect emerged for IT staff who predicted the time consumption to complete software development work. Take home message: When the time frame is short and a large amount of work needs to be done, the reverse request format, 'How much do you think you can do in X hours?', tends to lead to more over-optimistic time estimates.
The Magnitude Effect
The observed correlation is simply a result of random variation in time predictions and the fact that we used predicted time consumption as our measure of task size. The above two situations illustrate that we should expect larger time overruns for larger tasks when the task size is measured as the actual time consumption (or cost) and larger time underruns for larger tasks when the task size is measured as the predicted time consumption (or budgeted cost).
Length of Task Description
Rationally, the task is the same and the time predictions should be the same for both groups. Based on the above two studies, we can get the impression that it is easy to manipulate time predictions by increasing or decreasing the length of the task description.
The Time Unit Effect
Assessments of the accuracy of forecasting models in the field of weather forecasting seem to reach the same result. Include an estimate of time usage uncertainty, for example, using time forecast intervals.
Why Are We Overcon fi dent?
What Can We Do to Avoid Overcon fi dence?
The Use of Alternative Interval Prediction Formats
The above two pX values can be used to calculate the probability distribution of time use. In Fig.6.1 we see that the most probable time use is 10.6 hours, i.e. a little more than the predicted time use.
Learning from Accuracy Feedback
Nevertheless, we believe that the method will be better at providing realistic time prediction uncertainty information than typical, unsupported predictions of minimum-maximum time consumption for given confidence levels. Take home message: We propose a method (with associated tool) for assessing time prediction uncertainty.
Unpacking and Decomposition
One group predicted the total time of the first three tasks and then made separate time predictions for each of the last three tasks. This led to a stronger bias towards too high time predictions for the decomposed time predictions.
Analogies
In the studied office task situation, the general tendency was towards over-predictions. As in the first experiment, the parsed time predictions were higher, but now led to more accurate time predictions.
Relative Predictions
In general, when there is a strong similarity in time spent on similar tasks, more weight should be given to the closest analogy or analogies. When, on the other hand, there is a large difference in time consumption or productivity of similar tasks, more weight should be given to the average of a larger set of analogies.3.
Time Prediction Models
Time prediction models that are intended to be applicable in different contexts tend to be less accurate [19]. For example, based on the above ingredients, we can develop the following very simple test review time prediction model.
Consider Alternative Futures
On the sixth day, the student spent less time on the task than predicted and immediately updated the time prediction to something even better than his new best performance. This setup seems to make people realize that their time prediction of the most likely outcome should differ from the best-case scenario, and therefore they will make higher and often more realistic time-use predictions.
Combinations of Time Predictions
Take Home Message 1: The average of several weather forecasts is always more accurate than at least half—and usually more—of the individual weather forecasts. Take Home Message 2: Unless you have good reason to believe that one source of weather forecasts is systematically and substantially more accurate than the other sources, use the average or trimmed mean of several weather forecasts.
Let Other People Make the Prediction?
When it comes to predicting time usage—rather than completion time, as in the study above—the improvement from using observers instead of actors is even less clear. However, there are some contexts where time predictions become more accurate when people predict.
Removing Irrelevant and Misleading Information
Take home message 2: Observers' estimates of time prediction uncertainty appear more realistic than actors' estimates, especially when historical data on the accuracy of previous time predictions is available. Take home message: No known method can be used to remove the influence of irrelevant and misleading time prediction information, such as prediction anchors.
From Fibonacci to T-Shirt Sizes: Time Predictions Using
Take home message 2: Don't expect the use of alternative scales, such as non-linear scales, to improve the accuracy of time predictions. In the final stage, the group agrees on a time prediction or uses a mechanical combination of the individual predictions.
