Making Organizational Learning Operational: Implications from Learning Classifier Systems

Abstract

The concepts of organizational learning in organization and management science cover a very wide range of organization-related activities in organization. Since socially situated intelligence is one of such activities, this paper makes the concept of organizational learning operational from the computational viewpoint for investigating socially situated intelligence. In particular, this paper focuses on the characteristics of multiagent learning as one kind of socially situated intelligence, and analyzes them using four operationalized learning mechanisms in organizational learning. A careful investigation on the characteristics of multiagent learning has revealed the following implications: (1) there are two levels in the learning mechanisms for multiagent learning (the individual level and organizational level) and each mechanism is divided into two types (single- and double-loop learning). The integration of these four learning mechanisms improves socially situated intelligences and (2) the following properties support socially situated intelligence: (a) different dimensions in learning mechanisms, (b) interaction among various levels and types of learning mechanisms in addition to interaction among agents, and (c) combination of exploration at an individual level and exploitation at an organizational level.