Statistical Model Checking of the Keynes+Schumpeter Model: A Transient Sensitivity Analysis of a Macroeconomic ABM

TL;DR

This paper demonstrates how statistical model checking can be effectively applied to macroeconomic agent-based models, providing a principled, automated, and reproducible analysis framework that quantifies uncertainty and simulation effort.

Contribution

It introduces a method to integrate statistical model checking with macroeconomic ABMs without rewriting simulators, enabling systematic sensitivity analysis and uncertainty quantification.

Findings

Transient effects vary significantly across parameter families.

Macro-financial and structural sweeps show stronger effects than heuristic-rule sweeps.

SMC provides reproducible, quantitative insights with explicit uncertainty and cost estimates.

Abstract

Agent-based models (ABMs) are increasingly used in macroeconomics, but their analysis still often relies on ad hoc Monte Carlo campaigns with heterogeneous statistical effort across parameter settings. We show how statistical model checking (SMC), implemented through MultiVeStA, can provide a principled analysis layer for a realistic macroeconomic ABM without rewriting the simulator in a dedicated formalism. Our case study is the heuristic-switching Keynes+Schumpeter(K+S) model, analysed hrough a transient sensitivity campaign over one-parameter sweeps, two macro observables (unemployment and GDP growth), and one auxiliary micro-level probe (market share) on the post-warmup phase of a 600-step horizon. The analysis is driven by reusable temporal queries, observable-specific precision targets, and confidence-based stopping rules that automatically determine the simulation effort required by each configuration. Results show a clear contrast across parameter families: macro-financial and structural sweeps produce the strongest transient effects, whereas several heuristic-rule sweeps remain much weaker under the same precision policy. More broadly, the paper shows that SMC can support reproducible and informative quantitative analysis of substantively rich economic ABMs, while making uncertainty estimates and simulation cost explicit parts of the reported results.