Positionalist voting functions

Positionalist voting functions are those social choice functions where the positions of the alternatives in the voter's preference orders crucially influence the social ordering of the alternatives. An important subclass consists of those voting functions where numbers are assigned to the alternatives in the preference orders and the social ordering is computed from these numbers. Such voting functions are called representable. Various well-known conditions for voting functions are introduced and it is investigated which representable voting functions satisfy these conditions. It is shown that no representable voting function satisfies the Condorcet criterion. This condition and Arrow's independence condition, which are typical non-positionalist conditions, are shown to be incompatible. The Borda function, which is a well-known positionalist voting function, is studied extensively, conditions uniquely characterizing it are given and some modifications of the function are investigated.My thanks are due to professor Bengt Hansson for encouragement and several helpful suggestions.     

“Economic Theory’s Stance On No-Fault Divorce”

This article explores key insights that economic theory can shed on the issue of no-fault divorce in the United States, addressing modifications in the incentive structure of individuals that resulted from the legislative reforms of the 1970s. After stressing the importance of correctly interpreting and classifying divorce laws, this work investigates the contributions of the theory of property rights, the contributions of game theory and intra-household bargaining, and the contributions of general equilibrium analysis in our understanding of how divorce laws work and what their impact is. By doing so, this exposé analyzes the theoretical consequences of no-fault divorce on the decision whether to get married or divorced, on the characteristics of spouses and divorcees, on divorce rates, and on marital-specific and non marital-specific investments.     

Time series analysis of stratospheric ozone

Time series analyses of monthly average total ozone measured at 37 stations throughout the world were used to estimate the extent to which the average ozone trend correlates with the depletion curve hypothesized as due to chlorofluorocarbons (CFCs). Statistical characteristics of stations in the ensemble were used to help define appropriate model and station selection criteria. The maximum likelihood procedure developed herein estimates the weighted average trend, its. variance, and the intra- and inter-station variance components of the trend. Correlations among trends at different stations are also taken into account. The models were subjected to much checking and criticism. Variations in statistical methodology are used to show that the results are insensitive to details of the model selection criteria. The method does not discriminate well between the hypothesized CFC trend and a linear trend. The trend estimates represent the sum of all long-term global effects. The variance includes all effects that differ from station-to-station. The estimated trend and 2α limits for 14 stations with 20-year records (1958-79) is an ozone increase through 1979 of (1.5+1.0) percent. At the 23 stations with shorter records, the trend is (1.0=1.7) percent. It is concluded that no significant depletion in stratospheric ozone has occurred from any cause through the end of 1979.     

Distance equalizers

Distance equalizers are introduced as empirical measures of central tendency that make distances to univariate data as similar as possible. These measures are made precise by means of various so-called fluctuation functions which account for distances in different ways. Distance equalizers differ from the mean as well as from the median. Also, distance equalizers relate to dispersion measures. Algorithms and closed-form solutions for special cases are given. Some computations require to perform multiextremal function minimization. Distance equalization is extendable to data from higher dimensions and to function quantization in signal processing.