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Learning under Uncertainty: Networks in Crisis Management
Author(s) -
Moynihan Donald P.
Publication year - 2008
Publication title -
public administration review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.721
H-Index - 139
eISSN - 1540-6210
pISSN - 0033-3352
DOI - 10.1111/j.1540-6210.2007.00867.x
Subject(s) - variety (cybernetics) , computer science , crisis management , control (management) , learning network , knowledge management , network structure , risk analysis (engineering) , management science , artificial intelligence , business , machine learning , management , economics
This article examines learning in networks dealing with conditions of high uncertainty. The author examines the case of a crisis response network dealing with an exotic animal disease outbreak. The article identifies the basic difficulties of learning under crisis conditions. The network had to learn most of the elements taken for granted in more mature structural forms—the nature of the structural framework in which it was working, how to adapt that framework, the role and actions appropriate for each individual, and how to deal with unanticipated problems. The network pursued this learning in a variety of ways, including virtual learning, learning forums, learning from the past, using information systems and learning from other network members. Most critically, the network used standard operating procedures to provide a form of network memory and a command and control structure to reduce the institutional and strategic uncertainty inherent in networks.