Stackelberg strategies and incentives in multiperson deterministic decision problems

Discrete and continuous-time two-person decision problems with a hierarchical decision structure are studied, and the applicability and appropriateness of a function-space approach in the derivation of causal real-time implementable optimal Stackelberg (incentive) strategies under various information patterns are discussed. Results on existence and derivation of incentive strategies for dynamic games formulated in abstract inner-product spaces, in the absence of any causality restriction on the leader´s policies, are presented; these results are extended and specialized in two major directions: (1) discrete-time dynamic games, and (2) derivation of causal, physically realizable optimum affine Stackelberg policies for both discrete and continuous-time problems.