Wisdom in the Wild: Generalization and Adaptive Dynamics
Jaeho Choi () and
Daniel Levinthal ()
Additional contact information
Jaeho Choi: Management Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Daniel Levinthal: Management Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Organization Science, 2023, vol. 34, issue 3, 1073-1089
Abstract:
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.”
Keywords: organizational learning; generalization; categorization; computational model (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
http://dx.doi.org/10.1287/orsc.2022.1609 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:inm:ororsc:v:34:y:2023:i:3:p:1073-1089
Access Statistics for this article
More articles in Organization Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().