What Variation Identifies Payoffs in a Dynamic Game?

Observed choice in a dynamic game mixes current profit with continuation value. A rival adds a second problem: the same comparison averages over the rival’s equilibrium policy. Changing the primitive transition rewrites continuation technology; changing the rival’s Markov policy, holding that law fixed, rewrites the mixture over rival-contingent payoffs. The two are not substitutes. For a rival-feature payoff of rank $K$, rank identification up to location requires $\text{Ephi}=\lceil (MK-1)\mathrm{/}(M-1)\rceil$ policy environments, and a second kernel when payoffs are saturated. Rank can still be restored by arbitrarily small policy differences. Independent private shocks force mixed rival actions to factor, so a payoff that depends jointly on $d$ rivals is visible only at order ${\eta }^d$ near a common interior baseline. Either rank fails or the smallest identified singular value is at most $\kappa {\eta }^{\text{dPhi}}$, independently of how many kernels are stacked. Oracle-GLS variance in that direction vanishes only if $n{\eta }^{2\text{dPhi}}$ diverges. An Anderson–Rubin set that carries first-stage error in the design matrix covers without a vanishing-risk condition. On U.S.\ airline entry, even among rank-identified directions, the most favorable rival-dependent contrast is several times wider than observed behavior.