Markovian Embeddings for Coalitional Bargaining Games
arXiv:2306.11104v1h-index: 2
Originality Synthesis-oriented
AI Analysis
This addresses a theoretical issue in game theory for researchers, but appears incremental as it adapts existing methods to a specific constraint.
The paper tackled the problem of coalition bargaining games where past rejected proposals cannot be repeated, by proposing a Markovian embedding with filtrations to render the states Markovian and fit into the framework of stochastic games.
We examine the Markovian properties of coalition bargaining games, in particular, the case where past rejected proposals cannot be repeated. We propose a Markovian embedding with filtrations to render the sates Markovian and thus, fit into the framework of stochastic games.