Island Model + MultiVeStA — Statistical Model Checking of Economic Growth
This project reproduces and extends the Fagiolo & Dosi (2003) Island Model — a landmark agent-based model of endogenous economic growth — using MultiVeStA, a tool for sequential statistical model checking of stochastic systems.
The paper was accepted at MARS @ ETAPS 2026 (Workshop on Models for Formal Analysis of Real Systems, European Joint Conferences on Theory and Practice of Software).
Work with Giorgio Fagiolo, Daniele Giachini, Andrea Vandin, and Ernest Ivanaj (Scuola Superiore Sant’Anna).
Related pages: Proceedings, publication page, and presentation recap.
What the Model Does
The Island Model captures endogenous growth through the interaction of three types of heterogeneous agents operating on a fitness landscape:
- Miners exploit their current productive niche, accumulating skills over time
- Imitators copy the most successful visible agent, diffusing knowledge across the economy with probability φ
- Explorers search for new islands at random, driving innovation and preventing lock-in
The key insight is that the tension between exploitation and exploration — parameterised by ε — generates self-sustaining growth dynamics without requiring exogenous technological progress.
What MultiVeStA Adds
Standard Monte Carlo analysis uses a fixed number of simulation runs with no formal guarantee on estimation quality. MultiVeStA applies sequential statistical model checking: it runs simulations adaptively, stopping only when the 95% confidence interval on E[logGDP] is narrower than δ=0.05 at every time step. This provides a formal precision guarantee — the sample size is determined by the data variance, not by the experimenter.
Our analysis confirms the optimality of moderate exploration (ε ≈ 0.1), reproduces all stylized facts of the original model, and establishes through Welch t-test counterfactual analysis that 6 out of 7 pairwise parameter comparisons yield statistically distinguishable growth trajectories. The single exception (ρ=3.0 vs ρ=5.0) reveals a saturation effect in knowledge locality.
Interactive Explorer
The interactive app lets you explore the model live: watch agents move across the technology landscape, observe imitation cascades and exploration events, run MultiVeStA sensitivity analysis on α, φ, and ρ, and see how the sequential sampling converges.
