DSGE as a Structured World Model:Benchmarking Counterfactual Generalization in Economic Worlds

Modern world models – Dreamer, transformer world models (IRIS, Genie), and JEPA / next-latent architectures – learn dynamics from observed trajectories but share a weakness: their transition map is disciplined only where data were seen, so it degrades under policy-induced distribution shift and on counterfactual states off the training path. We argue that a Dynamic Stochastic General Equilibrium (DSGE) model is a structured world model: its state is a belief state – the very object a latent world model learns, but supplied with causal structure and hard cross-equation constraints. We introduce DSGE-Gym, a benchmark of eight DSGE environments with off-path counterfactual test sets, scaling to the ECB’s 230-variable New Area-Wide Model. We find that (i)learned world models match the dynamics on-path but collapse off-path (5σ tail RMSE up to \sim 40 the on-path level), and (ii)training the same architectures on data the DSGE generates across rare and counterfactual-policy states – coverage only a structural model can synthesize – roughly halves tail error and cuts policy-regime error 10–280 where the counterfactual rule shifts the ergodic support. Because such coverage cannot be sampled from any single history, this measures structure’s ability to manufacture the missing distribution. DSGE-Gym and all code are released as a reproducible testbed for counterfactual generalization.