There are indeed connections between the game-theoretic approach developed in the paper and other optimization techniques used in the analysis of stochastic processes. Dynamic programming, for example, is a powerful optimization method commonly used in sequential decision-making problems. The game-theoretic formulation can be seen as a specialized form of dynamic programming where the interactions between decision makers and nature are modeled as a two-person zero-sum game. By framing the stopping problem in this way, the optimal stopping rules can be derived by solving the game and finding the equilibrium strategies for both players. Similarly, Markov decision processes (MDPs) can also be related to the game-theoretic approach, as both involve modeling decision-making in a stochastic environment and finding optimal strategies to maximize rewards or minimize costs over time. By drawing parallels between these optimization techniques, researchers can leverage insights from game theory to enhance the analysis of stochastic processes and improve decision-making strategies in dynamic settings.