Why Policy Simulation Needs Uncertainty

Policy simulation should report distributions over outcomes, not only a single simulated future.

Simulation is often presented as if a policy produces a single outcome. Real systems rarely behave that way.

A policy simulator may contain uncertain behavioral parameters, incomplete causal structure, noisy observations, and stochastic interactions among agents. Reporting one trajectory hides these sources of uncertainty.

A Bayesian treatment offers a more useful alternative. Instead of calibrating the simulator to one best-fitting parameter vector, we infer a posterior distribution over plausible parameters. We then simulate each proposed intervention across many draws from that posterior.

The output is a distribution over policy outcomes.

This changes the question from:

What does the simulator predict?

into:

Across plausible models of the system, how often does this intervention produce the outcome we care about?

For expensive simulators, repeatedly performing Bayesian inference can itself become the bottleneck. Amortized inference offers a possible solution by learning a reusable mapping from observed simulator behavior to an approximate posterior over latent parameters.

The combination of causal structure, agent-based simulation, and amortized Bayesian inference is particularly appealing because each component addresses a different weakness: causal models provide intervention semantics, ABMs capture heterogeneous interaction, and Bayesian inference quantifies uncertainty.