This chapter builds upon the convergence results of EDM–PDM systems to develop systematic design methodologies for revision protocols and payoff mechanisms that guarantee convergence to generalized Nash equilibria (GNE) of the underlying population game. Leveraging the sufficient conditions established previously, prescriptive and non-prescriptive control approaches are proposed to ensure stability of the EDM–PDM dynamics while enforcing constraint satisfaction. In the prescriptive setting, revision protocols are explicitly designed to shape the agents’ decision-making behavior, whereas in the non-prescriptive setting, centralized or distributed payoff mechanisms are introduced to steer the collective outcome without altering individual learning rules. These design frameworks enable large-scale multi-agent systems to converge, in expectation, to stable and feasible configurations. Numerical simulations illustrate the proposed approaches in representative application scenarios.