Forecasting is a major part of our professional and private lives. A number of predictive judgments are based on analyses or computations, but most involve System 1 and intuition. Some intuitions draw on expertise and skill, gained through experience. The automatic and quick judgments and decisions of physicians, chess masters and fire chiefs are examples of skilled intuitions. They quickly come with solutions, because they recognize familiar cues. Other intuitions are the result of (substitution) heuristics. Numerous judgments arise from a combination of intuition and analysis. 

What are nonregressive intuitions?

A question regarding a current situation and a prediction activates System 1. “Mark is currently a bachelor student. He could count to 30 when he was two years old. What is his GPA?” People who have knowledge about the educational system provide quick answers thanks to the operations of System 1:

• Seeking a causal connection between the evidence (Mark’s counting) and the target of the prediction (his GPA), which in this case is academic talent. The associative memory then automatically and rapidly forms the best possible story from the available information (WYSIATI).

• Evaluating the evidence in relation to the relevant norm. How precocious is a child who counts to 30 at the age of two?

• Substitution and intensity matching. The evaluation of the evidence of cognitive ability at an early age is substituted as an answer to the question about his GPA in college. Mark will be assigned the same percentile score for his GPA and for his achievements as an early counter.

• Intensity matching: from the general impression of Mark’s academic achievements to a GPA score that matches the evidence for his talent. This impression needs to be translated into a corresponding GPA score.

The task required evaluating the evidence and predicting an outcome. This example clearly shows the role of substitution: people substitute an evaluation about the evidence when a prediction is asked, without being aware of the fact that the question they answer is not the question they were asked. This will lead to systematically biased predictions, as regression to the mean is fully ignored.

How can intuitive predictions be corrected?

The right way to predict Mark’s GPA is by using a formula for the factors that determine college grades and counting age:

GPA = factors specific to GPA + shared factors = 100%

Counting age = factors specific to counting age + shared factors = 100%

The shared factors are the degree to which family supports academic interests, genetically determined aptitude and other factors that would cause similar people to be precocious counters as minors and academical talents as adults. The correlation between both measures (GPA and counting age) equals the proportion of shared factors among their determinants. Assume the proportion being 30%. You are now ready to generate an unbiased prediction, in four steps:

1. Estimate the average GPA (baseline).

2. Determine what GPA matches your impression of the evidence (intuitive prediction).

3. Estimate the correlation between GPA and counting precocity (moving from the baseline towards the intuition).

4. Move 30% away from the average to the matching GPA (makes the prediction more moderate).

This is a general roadmap for predicting quantitative variables, like a GPA, company growth or investment profit. It builds on intuition, but moderates it by regressing it towards the mean. An intuitive prediction is not regressive, therefore biased, and needs to be corrected.

Common biases of predicting the probability of an outcome are insensitivity to the accuracy of evidence and neglect of base rates. The biases of predictions that are expressed on a scale and the corrective procedures are similar to the biases of discrete predictions. Similarities of the corrective procedures are: containing a baseline prediction, containing an intuitive prediction, aiming for a prediction that lies between the baseline and the intuitive answer, in the absence of relevant evidence: staying with the baseline, other extremes: staying with the initial prediction.

System 2 is responsible for correcting intuitive predictions. Finding the relevant reference category, estimating the baseline prediction and evaluating the quality of evidence requires some effort, which is justified only when there is a lot at stake and you can’t afford making a mistake. A willingness to predict rare events from low-quality evidence and extreme predictions are typical for System 1. The associative machinery naturally matches the extremeness of the prediction to the extremeness of the supporting evidence (substitution). System 1 also produces overconfident judgments. On the other hand: System 1 finds it hard to understand the idea of regression. Students often struggle with this topic. System 2 needs extra training to comprehend it.