Gary Klein is one of the great researchers in practical decision-making; however, he has been overshadowed by the behavioral bias revolution and the more popular work of Nobel prize winner Dan Kahneman. That is unfortunate and should be rectified. Klein focuses on naturalistic decision-making; the fact that decision-making in real life is significantly different than anything in a controlled environment.

In the natural world, there is value with shortcuts and intuition that would be scoffed at as biases by those who work in controlled research environment. Biases may exist, but some are a response to settings in the real world. Experience from the past exercise of judgment is useful for making more efficient and quick decision when time and uncertainty are critical factors. For those interested, Klein synthesizes natural decision making in a short article.

Natural decision-making is often representative of those who are either discretionary or systematic traders. A natural decision-maker is flexible and fluid with his decision and does not fit a theoretic foundation like maximizing expected utility based on assessing all probabilities. The discretionary trade is a natural decision-maker who uses his set of experiences to drive action when faced with new situations. Many investment decisions may not easily lend themselves to traditional decision analysis. Hence, intuition is valuable. We make the distinction between countable and non-countable decisions. A problem that can apply large amounts of countable data is more efficiently solved using quantitative techniques. Problems that are not able to use countable data require different skills and decision-making.  For example, reacting and profiting from central bank commentary is more art than science and requires an ability to connect events with future responses differently.

Nevertheless, even systematic traders may have to use experience, rules of thumb, and some shortcuts in order to effectively react to fast moving uncertain events. Any model building may require throwing out some information and limited the analysis to a manageable process.

Building models is a complex process that requires speed to offset uncertainty. Assumptions have to be made. Shortcuts taken. This is based not on expediency, but on a desire to get things right. Take the simple example of a trend-follower who is faced with limited information, volatile markets, and a requirement to control risk. It may be more effective to focus on analysis of price over trying to incorporate all alternatives. This is especially the case when the fundamental information is not readily available or provided with a lag.

While some have viewed natural decision-making at odds with behavioral biases, we argue that reality by be more nuanced. In a complex and uncertain world, there is a necessity to find useful shortcuts based on experience to increase decision efficiency. These approaches need to be scrutinized for their effectiveness but should not be dismissed as inappropriate solely because it does not fit the steps outlined for formalistic decision-making.