Self-Confirming Biased Beliefs in Organizational “Learning by Doing”

• Published in: Complexity (New York, N.Y.) N.Y.), 2021, Vol.2021 (1)
• Main Authors: Park, Sanghyun, Puranam, Phanish
• Format: Article
• Language: English
• Subjects: Bias; Distance learning; Employers; Human bias; Machine learning; Organizational learning; Product development
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2021/8865872

Learning by doing, a change in beliefs (and consequently behaviour) due to experience, is crucial to the adaptive behaviours of organizations as well as the individuals that inhabit them. In this review paper, we summarise different pathologies of learning noted in past literature using a common underlying mechanism based on self-confirming biased beliefs. These are inaccurate beliefs about the environment that are self-confirming because acting upon these beliefs prevents their falsification. We provide a formal definition for self-confirming biased beliefs as an attractor that can lock learning by doing systems into suboptimal actions and provide illustrations based on simulations. We then compare and distinguish self-confirming biased beliefs from other related theoretical constructs, including confirmation bias, self-fulfilling prophecies, and sticking points, and underscore that self-confirming biased beliefs underlie inefficient self-confirming equilibria and hot-stove effects. Lastly, we highlight two fundamental ways to escape self-confirming biased beliefs: taking actions inconsistent with beliefs (i.e., exploration) and getting information on unchosen actions (i.e., counterfactuals).