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The present-day standard textbook narrative on the history of growth theory usually takes Robert Solow's 1956 contribution as a key starting point, which was extended by David Cass and Tjalling Koopmans in 1965 by introducing an intertemporal maximization problem that defines the saving ratio in the economy. However, the road connecting Solow to the Ramsey-Cass-Koopmans model is not so straightforward. We argue that in order to understand Koopmans's contribution, we have to go to the activity analysis literature that started before Solow 1956 and never had him as a central reference. We stress the role played by Edmond Malinvaud, with whom Koopmans interacted closely, and take his travel from the French milieu of mathematical economics to the Cowles Commission in 1950-51 and back to France as a guiding line. The rise of turnpike theory in the end of the 1950s generated a debate on the choice criteria of growth programs, opposing the productive efficiency typical of these models to the utilitarian approach supported by Malinvaud and Koopmans. The Vatican Conference of 1963, where Koopmans presented a first version of his 1965 model, was embedded in this debate. We argue that Malinvaud's (and Koopmans's) contributions were crucial to steer the activity analysis literature toward a utilitarian analysis of growth paths.

AbstractFor a multidimensional criteria space to have an additive utility function, the strong condition of independence among the co‐ordinates (also known as separability) must be satisfied. However, if we are allowed to transform the criteria space into another, then a theorem due to Kolmogorov implies that we can, even in the absence of independence, transform the space into one that has a continuous additive utility function. Since this transformation is complex, it has only theoretical significance. However, certain related results from neurocomputing theory are quite practical and signify that there exists some scope for neurocomputing in multi‐criteria decision analysis.

This book reflects the challenges in utility industries and provides solutions from a managerial perspective. It features insights from managers, researchers, professionals in utility-related banking, consulting, and public and supranational organizations.

We present an analogy between joint cumulative probability distributions and a class of multiattribute utility functions, which we call attribute dominance utility functions. Attribute dominance utility functions permit assessing multiattribute utility functions using common techniques of joint probability assessment such as marginal-conditional assessments and the method of copulas. By itself, this class of utility functions appears in many cases of decision analysis practice. Furthermore, we show that many functional forms of multiattribute utility function can be decomposed into attribute dominance utility functions that are easier to elicit. We introduce the notion of utility inference analogous to Bayes' rule for probability inference and provide a graphic representation of attribute dominance utility functions, which we call utility diagrams.