Organizational learning in production networks

If one accepts that a firm's behavior is determined by history-dependent capabilities that adapt in a goal-directed way one would like to know how a firm's organizational structure influences the way in which this distributed and partially tacit organizational memory evolves over time. In this paper, we study the impact that alternative information systems, incentive systems and modes of learning co-ordination have on the efficiency and generality of priority rules for job shop scheduling which are learnt by a network of production agents modeled by neural networks. When modeling the alternative organizational structures by different input layers, feedback and training methods, we find that efficient rules evolve when global incentives and synchronized learning are employed even if the system state is only partially known to an agent. However, organizational learning fails when it is performed asynchronously with local goals.