In attempting to construct a general framework for the analysis of choice under uncertainty, researchers have long sought to establish reasonable criteria for the selection of one prospect over another.Among current researchers the concept of stochastic dominance 1 has attracted considerable attention.This paper attempts to clarify and generalize certain basic relationships between stochastic dominance and the maximization of expected utility.The paper begins with a critique of an article by Giora Hanoch and Haim Levy [lJ.Although Hanoch and Levy propose a series of interesting theorems relating stochastic dominance to the maximization of expected utility, errors appear in the statement and proof of these theorems which prevent (or should prevent) the researcher from using them directly.The necessary modifications are given in Part I below.An undesirable feature of many articles in the area of stochastic dominance are the regularity conditions imposed on the utility functions (e.g., bounded, differentiable) and the random variables (e.g., absolutely continuous distribution function, nonnegative).The important 1971

“All models are wrong, but some are useful. ” G.E.P. Box (1979, p. 202) Macroeconomists seek to understand the structure and performance of economies at a national or regional level and the manner in which government policy makers attempt to influence this structure and performance over time. Such understanding would seem to require a systematic exploration of the intricate feedback loops connecting micro behaviors, interaction patterns, and macro regularities as observed in real-world economies. In fact, however, mainstream macroeconomic theory remains firmly rooted in general equilibrium microfoundations (David Colander, 2006). Emphasis is on the isolated optimal choice behaviors of utility-maximizing households and profit-maximizing firms subject to budget and technological feasibility constraints, and on the equilibrium states attained through external imposition of conditions requiring fulfilled expectations and market clearing. Potentially important real-world factors such as subsistence needs, incomplete markets, imperfect competition, inside money, strategic behavioral interactions, and open-ended learning that tremendously complicate analytical formulations are typically not incorporated. Starting around the mid-1980s, various researchers have sought to develop agent-based computational economics tools able to capture in useful terms the complexity of real-world