A cybernetic view on learning curves and energy policy

Abstract

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.

Future energy system development depends on past learning opportunities

“…Wene uses nonequilibrium thermodynamics to derive a relation between the eigentime of the learning system and the calendar time of the Observer. The key assumption prescribes that the unperturbed, operationally closed learning system is in a steady nonequilibrium state.…”

“…The earlier derivations of learning rates focused on assessing and designing. Wene argues that the findings in these papers can be combined with the results for the learning equation to provide an equation of motion for the learning system. Figure shows the theoretical results for the learning equation and the basic learning mode applied to the time series for PV‐modules 1976–2010.…”

“…The relative stability of the learning curve over a large spectrum of technologies and over time supports the hypothesis of technology learning as a fundamental property of the learning system. Proof will require a solid theoretical platform, but would increase the predictive value of learning curves and provide reliable extrapolation procedures.…”

Cybernetics of Value Cocreation for Product Development

“…To this end, after exploring the issue of cocreation, we discuss a comprehensive complexity management approach grounded in the VSM (Beer, , ) that aims to offer a theoretical framework for value cocreation grounded in second‐order cybernetics. depict value cocreation as a dynamic process of cybernetic loops of interaction. In this perspective, the product is considered an ‘accumulated output’ produced by recurrent interactions (Miles, , Wene, ) or an eigenform (von Foerster, , , , ) deriving from an ongoing dynamic seeking satisfactory complexity management in the cocreation process. shed light on how consumers and producers can engage in increasingly effective cocreation processes. supply, through a method of variety balances (Espejo, ), a framework to manage and improve the quality of cocreation processes and to explore effective ways of fostering collaborative strategies for existing and new product development. This implies modelling cocreation relationships as homeostatic loops underpinned by variety operators. …”

“…In this perspective, the product is considered an 'accumulated output' produced by recurrent interactions (Miles, 2007, Wene, 2015 or an eigenform (von Foerster, 1981a(von Foerster, , 1981b(von Foerster, , 1981c(von Foerster, , 1981d deriving from an ongoing dynamic seeking satisfactory complexity management in the cocreation process.…”