2008/02/08 by Donald P. Moynihan · 388 citations
Decision Sciences · Social Sciences · #Artificial intelligence #Business #Complex Systems and Decision Making #Computer science #Control (management) #Crisis management #Disaster Management and Resilience #Economics #Knowledge management #Learning network #Machine learning #Management #Management science #Network structure #Public Relations and Crisis Communication #Risk analysis (engineering) #Variety (cybernetics)
paper · open access · doi:10.1111/j.1540-6210.2007.00867.x
published in Public Administration Review 68(2), 350-365 (Wiley)
openalex publication_date 2008/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
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.