“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