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Games on Multiplex Networks
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AI
DOI：10.1257/aer.20240763.png)
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Abstract
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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.
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