The two-person zero-sum game approach, while effective in deriving optimal policies under uncertainty, has several potential limitations. One significant drawback is the assumption of adversarial behavior between the two players, which may not accurately reflect the dynamics of many real-world decision-making scenarios. In many applications, agents may not be strictly competing against each other; instead, they may have cooperative or mixed motives, which a zero-sum framework does not accommodate.
Furthermore, the complexity of solving zero-sum games can lead to computational challenges, especially in high-dimensional state spaces. The need to compute mixed strategies and equilibria can result in increased computational overhead, making the approach less scalable for larger or more complex environments.
Additionally, the reliance on the assumption that one player's gain is exactly equal to the other's loss may oversimplify the interactions in multi-agent systems. This can lead to suboptimal strategies that do not account for collaborative opportunities or synergies that could be exploited for better overall outcomes.