What Variation Identifies Payoffs in a Dynamic Game?

2026-09-12

AI Summary

研究解决了动态博弈中收益识别问题，通过改变转移概率和对手策略方法，分析了在不同策略环境下收益的可识别性。

Abstract

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 $\Ephi=\lceil(MK-1)/(M-1)\rceil$ 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 $η^{d}$ near a common interior baseline. Either rank fails or the smallest identified singular value is at most $κη^{\dPhi}$, independently of how many kernels are stacked. Oracle-GLS variance in that direction vanishes only if $nη^{2\dPhi}$ 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.

Problem

• Dynamic Game
• Payoff Identification
• Rival Policy

Innovation

• dynamic game
• continuation value
• Markov policy
• rank identification
• private shocks