Understanding how to design learning and pricing mechanisms in the presence of strategic agents is essential for the effective operation of societal systems. These challenges arise in domains such as transportation, supply chains, and online platforms, where individuals respond strategically to information and incentives. In the first part, we introduce a novel learning model based on hypothesis testing, wherein agents form beliefs about their opponents’ strategies and update them via a stochastic process driven by hypothesis testing and utility-based exploration. We show that in any game, this learning dynamic converges to a Nash equilibrium that maximizes the minimum utility among all players. The second part of the thesis presents two applications of strategic pricing and intervention. In High Occupancy Toll (HOT) lane systems, we develop a game-theoretic model to design toll prices that incentivize carpooling among travelers with heterogeneous values of time and carpooling constraints. Using empirical data from California’s I-880 highway, we identify Pareto-efficient tolling strategies that balance travel time reduction, revenue generation, and social welfare. In a two-tier supply chain setting, we study a commission-based pricing game in which manufacturers delegate pricing to retailers through linear commission contracts. We characterize the subgame-perfect equilibrium in closed form and identify conditions under which such delegation induces price subsidization and leads to higher retail prices compared to direct sales.