Accounting for International War: The State of the Discipline

In studies of war it is important to observe that the processes leading to so frequent an event as conflict are not necessarily those that lead to so infrequent an event as war. Also, many models fail to recognize that a phenomenon irregularly distributed in time and space, such as war, cannot be explained on the basis of relatively invariant phenomena. Much research on periodicity in the occurrence of war has yielded little result, suggesting that the direction should now be to focus on such variables as diffusion and contagion. Structural variables, such as bipolarity, show contradictory results with some clear inter-century differences. Bipolarity, some results suggest, might have different effects on different social entities. A considerable number of studies analysing dyadic variables show a clear connection between equal capabilities among contending nations and escalation of conflict into war. Finally, research into national attributes often points to strength and geographical location as important variables. In general, the article concludes, there is room for modest optimism, as research into the question of war is no longer moving in non-cumulative circles. Systematic research is producing results and there is even a discernible tendency of convergence, in spite of a great diversity in theoretical orientations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69148/2/10.1177_002234338101800101.pd

A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential components of microeconomic theory including convex indifference curves, constrained utility maximization, demand functions, et cetera. This paper presents a new stochastic MDS procedure called MICROSCALE that attempts to operationalize many of these traditional microeconomic concepts. First, we briefly review several existing MDS models that operate on paired comparisons data, noting the particular nature of the utility functions implied by each class of models. These utility assumptions are then directly contrasted to those of microeconomic theory. The new maximum likelihood based procedure, MICROSCALE, is presented, as well as the technical details of the estimation procedure. The results of a Monte Carlo analysis investigating the performance of the algorithm as a number of model, data, and error factors are experimentally manipulated are provided. Finally, an illustration in consumer psychology concerning a convenience sample of thirty consumers providing paired comparisons judgments for some fourteen brands of over-the-counter analgesics is discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45748/1/11336_2005_Article_BF02294463.pd

A general model for preferential and triadic choice in terms of centralF distribution functions

multivariate triads, preferential choice, quadratic forms, choice models, multidimensional scaling, preference, unfolding, F distribution,