Organizational Learning from Crises with Machine Learning and Agent-Based Models

Organizational learning from crisis reflects that organizations may learn from their own and other’s experiences to increase organizational resilience and preparedness. However, it is for long been noticed that organizational learning from crisis often is hampered by barriers like, for example, cognitive narrowing and fixation or rigidity in core beliefs and assumptions—intermingled with actors’ building up of “defense lines” against mutual attributions of failure. Against this background, this chapter focuses on how machine learning and agent-based models could contribute to organizational learning from crisis. In particular, adopting the accountability perspective of organizational learning and building on Senge’s The fifth discipline: The art and practice of the learning organization. Doubleday, New York, 1990 prominent “five disciplines”, we derive a framework for machine learning and agent-based models to facilitate organizational learning. Moreover, we propose “systemic learning” as an integrated learning form that may capture the five disciplines for organizational learning.