Research on Zero-Determinant strategy in networked evolutionary games with memory bias

The Zero-Determinant (ZD) strategy allows players to unilaterally control the expected payoffs of all participants by establishing a specific linear relationship. Research in this area has grown increasingly prominent in recent years.This study focuses on developing ZD strategies for multi-agent finite iterated games under memory bias.To employ semi-tensor product (STP) operations, we first probabilistically encode memory bias into individual players’ strategy transition matrices, thereby constructing the global profile transition matrix. Subsequently, the framework is generalized from traditional multi-player games to networked evolutionary games (NEG), where ZD strategy design under memory constraints is rigorously analyzed.