Transitive measurable utility

This paper continues a study of theories of preferences under risk that do not use the independence axiom of the von Neumann-Morgenstern theory. Unlike its predecessor, it assumes that preferences are transitive. The effects of transitivity are noted in two representations of preferences. The first, which also uses continuity and dominance axioms, involves a function u on a set P of probability measures for which u(p) > u(q) if and only if p is preferred to q. Although u might be nonlinear, it has other features of a von Neumann-Morgenstern linear utility function. The second representation has linear functions u and w on P, with w strictly positive except perhaps at preference-extreme measures—where it might vanish, such that u(p) w(q) > u(q) w(p) if and only if p is preferred to q. A symmetry axiom along with the axioms for the first representation are necessary and sufficient for the second representation.     

BEYOND GDP—NEW APPROACHES TO APPLIED STATISTICS

New social concerns and priorities–beyond the economic growth paradigm–pose a challenge to the established statistical systems. The initial response of developing overall welfare measures and social indicators of the human quality of life had little impact on official statistics. Environment statistics, on the other hand, has become accepted as a new branch of applied statistics, maturing from exclusive pollution monitoring to cover various aspects of natural resources and the man-made environment. Still, a rather artificial separation between ecological monitoring and modeling and anthropocentric environmental statistics and accounting persists. The assessment of complex and interrelated socioeconomic and environmental concerns requires comparable inter-disciplinary information. The provision of such information should be facilitated by a flexible framework approach to statistical coordination and integration.     

On the information provided by forecasting models

The aim of the paper is to evaluate the information provided by forecasting models that include explanatory variables besides the variables to be forecasted. It is argued that the content of a forecast is a combination of historical information about the variable to be forecasted and theoretical considerations, normally manifested by a model. The historical information is assessed by a time series model for the variable. In order to assess the theoretical information about a variable, one suggests a measure. This measure is based on the improvement of fit to the actual values of the values obtained from the forecasting model in comparison to the values obtained from the time series model. The R² measure, which frequently is used as a measure of the explanatory power of a forecasting model, is critically discussed.     

The so-called “transformation problem” revisited: A comment

This comment shows that Lipietz's Marxist transformation theorem represents but a simple, though useful reinterpretation of obvious mathematical consequences of a standard Sraffa model—by making appropriate use of its known degree of freedom. Labor values are not involved in this new interpretation of conventional prices of production. A proposal is therefore made how their role in Marx's transformation may be investigated further on the basis of Lipietz's theorem and its interpretation of the “value of labor power”.