Dynamic Games with Asymmetric Information: Common Information Based Perfect Bayesian Equilibria and Sequential Decomposition
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Abstract (arXiv)
We formulate and analyze a general class of stochastic dynamic games with asymmetric information arising in dynamic systems. In such games, multiple strategic agents control the system dynamics and have different information about the system over time. Because of the presence of asymmetric information, each agent needs to form beliefs about other agents' private information. Therefore, the specification of the agents' beliefs along with their strategies is necessary to study the dynamic game. We use Perfect Bayesian equilibrium (PBE) as our solution concept. A PBE consists of a pair of strategy profile and belief system. In a PBE, every agent's strategy should be a best response under the belief system, and the belief system depends on agents' strategy profile when there is signaling among agents. Therefore, the circular dependence between strategy profile and belief system makes it difficult to compute PBE. Using the common information among agents, we introduce a subclass of PBE called common information based perfect Bayesian equilibria (CIB-PBE), and provide a sequential decomposition of the dynamic game. Such decomposition leads to a backward induction algorithm to compute CIB-PBE. We illustrate the sequential decomposition with an example of a multiple access broadcast game. We prove the existence of CIB-PBE for a subclass of dynamic games.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Oct 2015.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2024Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Gamescited by 14
- 2022No-Regret Learning in Dynamic Stackelberg Gamescited by 12
- 2023Scalable Learning of Intrusion Responses through Recursive Decompositioncited by 8
- 2023Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learningcited by 4
- 2026Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Informationcited by 0
- 2026Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information10 Jul 2026
- 2024Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games29 Feb 2024
- 2023Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning6 Oct 2023
- 2023Scalable Learning of Intrusion Responses through Recursive Decomposition6 Sep 2023
- 2022No-Regret Learning in Dynamic Stackelberg Games10 Feb 2022