We all face biases or bring them to decision-making, so it is critical to develop strategies to reduce the risk of bias. The bias problem applies to both quantitative and discretionary decisions. Just because you are using a model does ot mean that you are immune to biases. There should be a checklist to review the potential impact of biased thinking. The paper “A user’s guide to debiasing” does a good job of exploring the basics of reducing biases and improving the quality of judgment in decision-making. Debiasing reduces logical inconsistencies and misperceptions or misjudgments of reality. Debiasing is not the same as gathering more factual informaiton. The objective is to improve decisions given a specific set of information.
Categorizing debiasing methods distinguishes between the person and the task. Improving the person requires training to help the decision-maker overcome their limitations. The second approach is to modify the environment to match the thinking required.
Many of the sources of our biases come from the confusion between system 1, fast and automatic responses, and system 2, which requires slower and more deliberate thinking. It is important to distinguish between narrow thinking and shallow thinking. Narrow thinking focuses attention on a single category of objectives and crowds out the ability to identify other alternative objectives. Shallow thinking devotes too little effort to the required task. For any decision, there has to be a level of readiness to perform better decision-making.
The person can be modified through better education that generates more alternatives, tempers optimism, focuses on improving judgmental accuracy, and assesses uncertainty. Decision-makers can use defaults to get closer to making better decisions through regular processing, nudges to induce reflection through prompts or planned interruptions, and the formation of a set of active choices. If this can be done for the individual, a similar process can be applied to the organization.
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