# Common Information based Markov Perfect Equilibria for Linear-Gaussian Games with Asymmetric Information

arXiv: https://arxiv.org/abs/1401.4786 · HTML: https://mlsanity.com/paper/1401.4786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2014.

## Built on by (most cited first, 1 of 1)

- [Strategic Communication under Threat: Learning Information Trade-offs in Pursuit-Evasion Games](https://mlsanity.com/paper/2510.07813/) (2025)

## Abstract (arXiv)

We consider a class of two-player dynamic stochastic nonzero-sum games where the state transition and observation equations are linear, and the primitive random variables are Gaussian. Each controller acquires possibly different dynamic information about the state process and the other controller's past actions and observations. This leads to a dynamic game of asymmetric information among the controllers. Building on our earlier work on finite games with asymmetric information, we devise an algorithm to compute a Nash equilibrium by using the common information among the controllers. We call such equilibria common information based Markov perfect equilibria of the game, which can be viewed as a refinement of Nash equilibrium in games with asymmetric information. If the players' cost functions are quadratic, then we show that under certain conditions a unique common information based Markov perfect equilibrium exists. Furthermore, this equilibrium can be computed by solving a sequence of linear equations. We also show through an example that there could be other Nash equilibria in a game of asymmetric information, not corresponding to common information based Markov perfect equilibria.