Learning under Uncertainty: Networks in Crisis Management


Donald P. Moynihan is an associate professor of public affairs at the University of Wisconsin–Madison. He has published many articles on performance management, organizational change, homeland security, and organizational behavior in journals such as the Public Administration Review, Journal of Public Administration Research and Theory, and Governance. His book The Dynamics of Performance Management: Constructing Information and Reform is available from Georgetown University Press.
E-mail: dmoynihan@lafollette.wisc.edu


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.