Games on Multiplex Networks
构建了一个多层网络模型，分析个体在多层结构中分配努力的行为，发现单层模型预测与多层模型差异显著，对政策设计有重要启示。
We develop a simple multilayer network model in which agents allocate effort across layers with heterogeneous structures, subject to an aggregate effort constraint. Incentives are shaped by agents’ network positions within each layer, and equilibrium behavior reflects both within- and cross-layer interactions. We analyze how shocks propagate through the network and characterize optimal targeting interventions. Our results show that effective policy design must account for effort allocation across layers. We also demonstrate that predictions from monolayer models can diverge sharply from those of multilayer models, underscoring the importance of accounting for network complexity in both empirical and policy analyses.