RISK, UNCERTAINTY, AND COMPLEXITY

Complexity theory provides formal procedures for analyzing problem dif ficulty. Frank H. Knight in Risk, Uncertainty and Profit assumed intel ligence is finite and stressed the difficulty of solving problems invo lving uncertainty. In this paper, a risk decision is a stochastic opti mization problem where the parameters and the functional forms require d to determine the optimal decision are known. An uncertain decision i s a stochastic optimization problem where at least one parameter or fu nctional form must be estimated. Using complexity theory, a valid dist inction can be made between risk and uncertainty which is consistent w ith Bayesian statistics. From the perspective of bounded rationality K night's concepts of consolidation and specilization can be reconciled with the Bayesians.