ForewordThree decades of debate over energy questions have demonstrated the need for a multidimensional approach to explore alternative energy futures and the viability of different energy options. The answer has been to develop increasingly sophisticated modeling tools for analyzing different disciplinary perspectives. The results, however, are not easily translated from one model to another, and sometimes two different perspectives generate conflicting results. These difficulties have been reflected in ongoing discussions on energyefficiency improvements and the costs of C0 2 reductions. To consider the whole situation, special tools and procedures for interpreting results and for negotiating between different perspectives are required.This paper describes procedures that link economic models with systems engineering models. It is based on the modeling framework used for a number of assessments including the joint IIASA and the World Energy Council (WEC) study presented in Global Energy Perspectives to 2050 and Beyond. This assessment framework includes a set of linked energy, economy, and environmental models. The macroeconomic and systems engineering models are part of this framework and provide different perspectives on the energyeconomy-technology interactions. Both models have roots in work done at IIASA in the mid-1970s.The current report uses a common, formalized language to develop stringent linking procedures. The results indicate the value of such a formal linking methodology. The procedures translate a much-debated , key parameter describing energy-efficiency improvements in the macroeconomic model into results obtained in the systems engineering model. They also shed light on fallacies that may result from a reliance on only one perspective. The methodology illustrates how linking enhances the capacity of existing, peer-reviewed models and supports the approach IIASA has taken in using models with a proven track record for its assessment of long-term energy perspectives.Nebojsa Abstract-Informal linking or softlinking of macroeconomic and systems engineering models can provide high variety tools for joint energy-economy analysis. A necessary condition for internal control of such linking is a common, formalized language describing areas of overlap between the models. The principle of a common language is discussed and demonstrated for the softlinking of a macroeconomic model (ETA-MACRO) and a systems engineering model (MESSAGE III).

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PurposeConsidering the technology learning system as a non‐trivial machine, this paper seeks to take a first step to ground experience and learning curves in cybernetic theory.Design/methodology/approachAssuming operational closure, feedback regulation and a constant elasticity of output/input ratio to cumulative output makes it possible to calculate eigenvalues for the self‐reflecting loop in the learning system.FindingsThe results imply a zero mode learning rate of 20 per cent with higher modes providing learning rates smaller than 8 per cent. The results reproduce the grand features of technology learning.Research limitations/implicationsThe NTM approach provides basis for work to understand improvements in grafted technologies and effects on learning from radical innovations.Practical implicationsFurther inquiries into the learning system need complementary organisational analysis.Originality/valueBased on the theory of the non‐trivial machine, this paper takes the first step to ground the experience and learning curves in cybernetic theory.

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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.