The early interest and development of dynamic programming in agricultural economics has not been generally adopted by ·the mainstream of quantitative agricultural economics, as have the essentially static optimal policy analysis techniques of linear and quadratic programming, econometric multiplier analysis, and suboptimal simulation. Why? The most likely answer is that the problems with the most quantitative appeal during this decade were simply not solvable by the numerical search dynamic progranming techniques currently available. The emphasis in quantitative modeling was, and still is, on multivariable systems and their stochastic properties. Except for cases of remarkable aggregation or simple physical sys tens, the aptly named "curse of dimensionality" prevented the achievement of solutions even under deterministic assumptions.