Baecker D. (1994) The intelligence of ignorance in self-referential systems. In: Trappl R. (ed.) Cybernetics and systems: Proceedings of the Twelfth European Meeting on Cybernetics and Systems Research, Vienna, Austria, 5–8 April 1994. World Scientific, Singapore: 1555–1562. https://cepa.info/7609

Self-referential systems theory does not provide for a concept of intelligence. There is even a certain resistance to intelligence that seems to block any explicit exchange of concepts with artificial systems theory. The paper describes the intelligence service in self-referential systems as the self-referential and, hence, paradoxical switching from the self-reference of these systems to other-reference. How this might work is shown by means of G. Spencer Brown’s calculus of indications and Heinz von Foerster’s notion of double closure.

The paper recalls some skeptical comments Norbert Wiener made regarding the potential use of cybernetics in social sciences. A few social scientists were seduced by cybernetics from the beginning, but cybernetics never really caught on in sociology. The paper argues that one reason for this may lie in the mathematical theory of communication entertained by early cybernetics. This theory which maintains that there are probability distributions of possible communication is at odds with the sociological theory’s idea of a communication driven by improbable understanding. Yet the move from first-order cybernetics to second-order cybernetics, by re-entering the observer into the very systems she observes, provides for a bridge between cybernetics and sociology.

Purpose: The purpose of this paper is to demonstrate that cybernetic theory explains learning curves and sets the curves as legitimate and efficient tools for a pro-active energy technology policy. Design/methodology/approach – The learning system is a non-trivial machine that is kept in non-equilibrium steady state at minimum entropy production by competitive, equilibrium markets. The system has operational closure and the learning curve expresses its eigenbehaviour. This eigenbehaviour is analysed not in calendar time but in the characteristic time of the system, i.e., its eigentime. Measured in eigentime, the minimum entropy production in the steady-state learning system is constant. The double closure mechanism described by Heinz von Förster makes it possible for the learning system to change (adapt) its eigenbehaviour without compromising its operational closure. Findings: By obeying basic laws of second order cybernetics and of non-equilibrium thermodynamics the learning system self-organises its learning to follow an optimal path described by the learning curve. The learning rates are obtained through an operator formalism and the results explain observed distributions. Application to solar cell (photo-voltaic) modules indicates that the silicon scarcity bubble 2005–2008 produced excess entropy corresponding to costs of the order of 100 billion US dollars. Research limitations/implications: Grounding technology learning and learning curves in cybernetics and non-equilibrium thermodynamics open up new possibilities to understand technology shifts through radical innovations or paradigm changes. Practical implications: Learning curves are legitimate and efficient tools for energy policy and industrial strategy. Originality/value – Grounding of technology learning and learning curves in cybernetic and thermodynamic theory provides a stable theoretical basis for applications in industry and policy.