https://stefano-blando.github.io/en/tags/matlab/MATLABHugoBlox Kit (https://hugoblox.com)en-USSun, 12 Apr 2026 00:00:00 +0000https://stefano-blando.github.io/media/icon_hu_8d0dee6c10a3c598.pnghttps://stefano-blando.github.io/en/tags/matlab/https://stefano-blando.github.io/en/publications/island-model-smc/Sun, 12 Apr 2026 00:00:00 +0000https://stefano-blando.github.io/en/publications/island-model-smc/https://stefano-blando.github.io/en/projects/island-model-smc/Fri, 06 Mar 2026 00:00:00 +0000https://stefano-blando.github.io/en/projects/island-model-smc/<p>This project reproduces and extends the <strong>Fagiolo &amp; Dosi (2003) Island Model</strong> — a landmark agent-based model of endogenous economic growth — using <strong>MultiVeStA</strong>, a tool for sequential statistical model checking of stochastic systems.</p> <p>The paper was accepted at <strong>MARS @ ETAPS 2026</strong> (Workshop on Models for Formal Analysis of Real Systems, European Joint Conferences on Theory and Practice of Software).</p> <p><em>Work with Giorgio Fagiolo, Daniele Giachini, Andrea Vandin, and Ernest Ivanaj (Scuola Superiore Sant&rsquo;Anna).</em></p> <p>Related pages: <strong> </strong>, <strong> </strong>, and <strong> </strong>.</p> <h2 id="what-the-model-does">What the Model Does</h2> <p>The Island Model captures endogenous growth through the interaction of three types of heterogeneous agents operating on a fitness landscape:</p> <ul> <li><strong>Miners</strong> exploit their current productive niche, accumulating skills over time</li> <li><strong>Imitators</strong> copy the most successful visible agent, diffusing knowledge across the economy with probability φ</li> <li><strong>Explorers</strong> search for new islands at random, driving innovation and preventing lock-in</li> </ul> <p>The key insight is that the tension between exploitation and exploration — parameterised by ε — generates self-sustaining growth dynamics without requiring exogenous technological progress.</p> <h2 id="what-multivesta-adds">What MultiVeStA Adds</h2> <p>Standard Monte Carlo analysis uses a fixed number of simulation runs with no formal guarantee on estimation quality. MultiVeStA applies <strong>sequential statistical model checking</strong>: 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.</p> <p>Our analysis confirms the <strong>optimality of moderate exploration</strong> (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 <strong>saturation effect</strong> in knowledge locality.</p> <h2 id="interactive-explorer">Interactive Explorer</h2> <p>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.</p> <ul> <li><strong> </strong></li> <li><strong> </strong></li> <li><strong> </strong></li> <li><strong> </strong></li> </ul>