GeMA: Learning Latent Manifold Frontiers for Benchmarking Complex Systems
GeMA:学习潜在流形前沿以对复杂系统进行绩效比较
Benchmarking the performance of complex systems such as rail networks, renewable generation assets and national economies is central to transport planning, regulation and macroeconomic analysis. Classical frontier methods, notably Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), estimate an efficient frontier in the observed input-output space and define efficiency as distance to this frontier, but rely on restrictive assumptions on the production set…