Publication | Closed Access
Learning from Samples of One or Fewer
1.3K
Citations
43
References
1991
Year
Multiple Instance LearningEngineeringMachine LearningOrganization ScienceOrganizational BehaviorLearning OrganizationOrganizing (Management)Data ScienceData MiningPattern RecognitionManagementStatisticsSupervised LearningInstance-based LearningOrganizational SystemsConsistent AgreementKnowledge DiscoveryOrganizational ResearchValid KnowledgeOrganizational SurvivalOrganizational CommunicationBusiness HistoryOrganization DevelopmentBusinessOrganization TheoryEpistemologyStatistical InferenceKnowledge Management
Organizations learn from experience, yet historical data are often scarce. The study explores how organizations convert rare events into historical interpretations and seeks to identify the methods used, the problems involved, and potential improvements. The authors analyze the organizational processes of interpreting infrequent events and balancing agreement with correctness. Although the observed methods do not guarantee consistent agreement, valid knowledge, improved performance, or survival, they provide insights into learning from fragments of history.
Organizations learn from experience. Sometimes, however, history is not generous with experience. We explore how organizations convert infrequent events into interpretations of history, and how they balance the need to achieve agreement on interpretations with the need to interpret history correctly. We ask what methods are used, what problems are involved, and what improvements might be made. Although the methods we observe are not guaranteed to lead to consistent agreement on interpretations, valid knowledge, improved organizational performance, or organizational survival, they provide possible insights into the possibilities for and problems of learning from fragments of history.
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