Research | Stefano Blando

Island Model + MultiVeStA — Statistical Model Checking of Economic Growth

Fri, 06 Mar 2026 00:00:00 +0000

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: , , and .

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.

Robust Portfolio Optimization under Systematic Market Disruptions (PFSE)

Fri, 06 Mar 2026 00:00:00 +0000

This project introduces the Parallel Factor Space Estimator (PFSE), a hybrid framework for robust covariance estimation that resolves a fundamental trade-off in institutional portfolio management: traditional robust estimators (MCD, Tyler) offer strong statistical guarantees but are computationally infeasible for daily rebalancing of 100+ assets, while efficient methods (Ledoit-Wolf, sample covariance) have zero breakdown point against systematic contamination.

PFSE exploits a structural insight: during systematic market disruptions — flash crashes, monetary policy shocks, crisis contagion — extreme movements propagate through common factors, not idiosyncratic components. By concentrating robust estimation in reduced k-dimensional factor space (k=5 versus p=100–1000), PFSE inherits 25% breakdown point from MCD while achieving 32× computational speedup.

Work with Alessio Farcomeni (University of Roma Tor Vergata). Submitted to Computational Economics (Springer), March 2026.

Key Results

Validated through three complementary stages:

Monte Carlo (p=100, ε=10% contamination): PFSE Sharpe 1.42 vs 0.96 for sample covariance — maintains 97% of clean-data performance while sample covariance degrades 31%
S&P 500 backtest (2015–2025): Out-of-sample Sharpe 1.87 vs 1.63 (+14.7%), max drawdown −24.3% vs −34.1% (−29%) during COVID-19, turnover −42%
Five stress scenarios: PFSE rank-1 across all scenarios, average Sharpe 1.67 vs 1.39 (+20%), lowest performance variability (CoV 0.041 vs 0.064)
Economic value: $72M normal-period + $93M stress-period benefits per $1B portfolio, benefit-cost ratio 31:1

Interactive Explorer

The interactive app lets you explore the full results: run the PFSE estimation algorithm live, compare methods under increasing contamination levels, examine computational scalability, and inspect S&P 500 cumulative returns across all four market regimes.

Network Topology Analysis for Systemic Risk Prediction

Sat, 10 Jan 2026 00:00:00 +0000

This project corresponds to my CESMA thesis on systemic risk prediction in U.S. equity markets. The underlying question is simple but important: can network topology say something useful about market stress before standard indicators do?

Using daily data from 210 S&P 500 constituents (2013-2025), the project combines dynamic correlation networks with machine learning models ranging from gradient boosting to Graph Neural Networks such as GraphSAGE and GAT. The goal is not just classification accuracy, but economic usefulness under realistic validation and backtesting constraints.

The most interesting result is that network-derived signals appear to carry genuine early-warning information, especially around severe and structurally fragile market states. In the strongest configurations, the framework improves both timing and trading performance relative to simpler baselines.

This project is paired with the related thesis page, where the same work is presented as a publication entry.

NLP & Semantic Network Analysis

Sat, 10 Jan 2026 00:00:00 +0000

This project is the technical implementation behind the publication “A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics” (JADT 2026, in review). It operationalizes the full analytical workflow used in the paper, from data preparation to cross-method validation and result comparison.

The pipeline combines R and Python modules over a large multilingual review corpus, including: preprocessing and TF-IDF, LDA topic modeling, LSA and Correspondence Analysis, lexicon- and model-based sentiment analysis, clustering, and co-occurrence network analysis. The repository also includes cross-platform validation scripts to compare method outputs and check structural stability across implementations.

The central objective is methodological robustness: verifying which findings remain consistent when methods, model families, and language-specific components vary. In this sense, the project is not a generic NLP demo, but a reproducible research pipeline designed for quantitative validation of text-analytic conclusions.