Increasing Returns, Learning-By-Doing And Neural Networks

In this paper we draw an analogy between the process of learning-bydoing and the learning process which develops in a neural network context. The bridging tool we refer to is a dynamic production function whose only variable input is labour. By concentrating on the 'neural network production function' we show that the learning process can lead to increasing returns. The simulations show that when learning is characterized by an upper limit. returns are increasing for some time, while in the long run they go back to the level where they are constant.