  We provide two methodological insights on \emph{ex ante} policy evaluation
for macro models of economic development. First, we show that the problems of
parameter instability and lack of behavioral constancy can be overcome by
considering learning dynamics. Hence, instead of defining social constructs as
fixed exogenous parameters, we represent them through stable functional
relationships such as social norms. Second, we demonstrate how agent computing
can be used for this purpose. By deploying a model of policy prioritization
with endogenous government behavior, we estimate the performance of different
policy regimes. We find that, while strictly adhering to policy recommendations
increases efficiency, the nature of such recipes has a bigger effect. In other
words, while it is true that lack of discipline is detrimental to prescription
outcomes (a common defense of failed recommendations), it is more important
that such prescriptions consider the systemic and adaptive nature of the
policymaking process (something neglected by traditional technocratic advice).
