Wisdom in the Wild: Generalization and Adaptive Dynamics

• 成果类型: Article
• 署名作者: Choi, Jaeho; Levinthal, Daniel
• 署名单位: University of Pennsylvania
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2022.1609
• 发表日期: 2023
• 页码: 1073-1089
• 关键词: organizational learning; generalization; categorization; computational model

摘要

Learning from experience is a central mechanism underlying organizational capabilities. However, in examining how organizations learn from past experiences, much of the literature has focused on situations in which actors are facing a repeated event. We direct attention to a relatively underexamined question: when an organization experiences a largely idiosyncratic series of events, at what level of granularity should these events, and the associated actions and outcomes, be encoded? How does generalizing from experience impact the wisdom of future choices and what are the boundary conditions or factors that might mitigate the degree of desired generalization? To address these questions, we develop a computational model that incorporates how characteristics of opportunities (e.g., acquisition candidates, new investments, product development) might be encoded so that experiential learning is possible even when the organization's experience is a series of unique events. Our results highlight the power of learning through generalization in a world of novelty as well as the features of the problem environment that reduce this power.