We present a unifying representation of computation as a two-player game between an \emph{Algorithm} and \emph{Nature}, grounded in domain theory and game theory. The Algorithm produces progressively refined approximations within a Scott domain, while Nature assigns penalties proportional to their distance from the true value. Correctness corresponds to equilibrium in the limit of refinement. This framework allows us to define complexity classes game-theoretically, characterizing $\mathbf{P}$, $\mathbf{NP}$, and related classes as sets of problems admitting particular equilibria. The open question $\mathbf{P} \stackrel{?}{=} \mathbf{NP}$ becomes a problem about the equivalence of Nash equilibria under differing informational and temporal constraints.