Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
The episode reviews a framework for calibrating complex, over-parametrized simulations that cannot be solved with a single unique answer. It details how to use an 'eligibility set' to manage uncertainty. The hosts discuss extracting meaningful features from massive datasets and comparing them against real data using advanced statistical methods, showing the method's applicability to systems like financial market simulations.