{"id":68942,"date":"2022-06-09T11:17:00","date_gmt":"2022-06-09T15:17:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=68942"},"modified":"2022-06-23T08:30:35","modified_gmt":"2022-06-23T12:30:35","slug":"decision-hygiene","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/decision-hygiene\/","title":{"rendered":"Decision Hygiene: Principles and Best Practices"},"content":{"rendered":"\n

What is decision hygiene? How can following the decision hygiene principles help you filter out noise and bias<\/a> and make the best choices?<\/p>\n\n\n\n

The term “decision hygiene” comes from the book Noise <\/em>by Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein. The book focuses on how to improve the judgments that affect some of the most important aspects of our lives, including our justice system, medical care, education, and business decisions.<\/p>\n\n\n\n

Here’s how to practice<\/a> good decision hygiene, according to Noise.<\/em><\/p>\n\n\n\n\n\n\n\n

Decision Hygiene<\/strong><\/h2>\n\n\n\n

Despite the potential advantages of mechanical judgments<\/a>, the authors of Noise<\/em> are most interested in finding ways to reduce noise in human judgments. They say that the best way to improve human judgments is by implementing \u201cdecision hygiene\u201d\u2014consistent, preventative measures put in place to minimize the chance of noise. <\/strong>Decision hygiene consists of a loose set of suggestions, practices, and principles which we explore below. (Shortform note: With one exception (see the Sample Hiring Procedure below), the authors don\u2019t lay out a specific, systematic course of action. Presumably, organizations should strive to implement as many of the following suggestions as are relevant and practicable.)<\/p>\n\n\n\n

Think Statistically<\/h3>\n\n\n\n

Recall that our normal, causal way of thinking is prone to errors and biases that manifest as noise. To make our thinking more accurate, we have to take a statistical view. <\/strong>The authors suggest that instead of treating each case as its own unique item, we should learn to think of it as a member of a larger class of similar things. Then, when predicting how likely an outcome is, we should consider how likely that outcome is across the whole class.<\/strong> Returning to an earlier example, if we\u2019re trying to predict the likelihood that a student will graduate from college, we first need to know what percentage of all incoming college students end up graduating from college.<\/p>\n\n\n\n

How to Think Statistically<\/strong>

Our failure to think statistically is a major theme in Thinking, Fast and Slow<\/em>. In that book, Kahneman offers a more detailed look at thinking errors of this type<\/a> and suggests ways to overcome them. As is also suggested in Noise<\/em>, the basic idea is to take base probabilities into account<\/a>.

In The Signal and the Noise<\/em><\/a>, Nate Silver suggests another approach to statistical thinking based on a statistical formula known as Bayes\u2019 Theorem. When making a prediction using Bayes\u2019 Theorem, you start with a preliminary guess about the likelihood of an event. Ideally, this guess is based on hard data, such as a base probability. Then you make some calculations in which you adjust the starting probability in the face of specific evidence relating to the thing you are trying to predict. Finally, you repeat this process as many times as you can, each time starting with your most recently updated probability.

This approach has two advantages. First, it explicitly accounts for the noise in human judgment<\/a> by building human estimates and predictions into the formula. Second, it calls for repeated testing of a prediction or hypothesis in order to improve accuracy in response to updated evidence. Interestingly, Silver argues that a Bayesian approach<\/a> would have prevented the replicability crisis that has recently plagued the sciences\u2014including some of the studies in Thinking, Fast and Slow<\/em>.<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Choose (and Train) Better Judgers<\/h3>\n\n\n\n

The authors argue that it\u2019s possible to improve the quality of human judgers. We can do so by finding better judgers in the first place and by helping judgers improve their techniques and processes.<\/p>\n\n\n\n

There are two factors to keep in mind when identifying good judgers. Some fields deal with objectively right or wrong outcomes; in these cases judgers can be measured by their objective results.<\/strong> However, as the authors point out, other fields are based instead on expertise, which can\u2019t be measured with a metric. But judgers in any field can be assessed by their overall intelligence, their cognitive style, and their open-mindedness<\/strong>; these traits are correlated with better judgment skills<\/a>. The authors emphasize, however, that intelligence alone doesn\u2019t make someone a good judger. The other two traits are just as important, if not more.<\/p>\n\n\n\n

The authors also note that some members of the general population are superforecasters<\/a>, and their predictions are consistently more accurate than those of the average trained expert. Ideally, these are the people we should hire or appoint as judgers. The authors identify several traits exhibited by superforecasters that we can use to choose better judgers, or to better train the judgers already in place:<\/p>\n\n\n\n