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This paper argues that macro models should be as simple as possible, but not more so. Existing models are more so by far. It is time for the science of macro to step beyond representative agent, DSGE models and focus more on alternative heterogeneous agent macro models that take agent interaction, complexity, coordination problems and endogenous learning seriously. It further argues that as analytic work on these scientific models continues, policy-relevant models should be more empirically based; policy researchers should not approach the data with theoretical blinders on; instead, they should follow an engineering approach to policy analysis and let the data guide their choice of the relevant theory to apply.

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David Colander

Peter Howitt

Alan Kirman

Axel Leijonhufvud

Perry Mehrling

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