ABSTRACT

Learning systems (LS) can be described as operationally closed non-trivial machines. The learning curve describes the performance of such systems in a competitive environment. Operational closure is the key rationale for quantum modelling of learning systems and their performance. In the same way as the particle-wave dualism in quantum mechanical systems, operational closure severely reduces the degrees of freedom of the system and results in eigenstates and eigenbehaviour. The insight that the learning system works as a steady-state non-equilibrium thermodynamic system guides the approach. The task is to find the quantum dynamical counterpart. Learning curves are widely used in industry and since the 1990s also for developing energy policy. Many efforts have been made to understand the phenomenon but so far no comprehensive theory has emerged. The lack of a theoretical foundation has raised serious doubts on the reliability of the curves in designing and applying strategies and policies.