Independent, open, falsifiable
FICSS studies the long-term dynamics of human societies by estimating probability distributions over possible futures. An estimate without a confidence interval does not leave the building.
Built for long-horizon research
The Foundations Institute of Computational Social Science (FICSS) is an independent research programme led by Stefano Angeli. Its core project, Computational Macrohistory (CMH), develops mathematical and statistical methods to model the structural conditions of large-scale sociopolitical transitions.
FICSS has no institutional affiliation, no staff, and no external funding. All working papers are single-author publications by Stefano Angeli, who conducts this research alongside his professional practice.
Earlier work in this series appeared under the anagrammatic pseudonym Galen Fontaise.
Computational Macrohistory
FICSS develops the Computational Macrohistory (CMH) framework: eight foundational axioms and a 25-dimensional state space spanning demographic, economic, political, social and collective-psychological variables. The mathematical architecture moves from static composite indices through stochastic dynamical systems to spectral decomposition methods.
Current empirical work covers post-Soviet transitions (1985-2000) and the Arab Spring across eleven MENA countries (2000-2012). Ongoing theoretical work integrates Koopman operator methods for the spectral analysis of historical panel data; their first pre-registered empirical test is reported in WP-2026-006.
The goal of Computational Macrohistory is to give the study of complex social dynamics the clarity of quantitative analysis. We look for patterns, and we state exactly how far each one can be trusted.
Stefano Angeli
Develops the CMH framework: stochastic nonlinear dynamical systems, statistical inference and operator-theoretic methods applied to socio-political transitions. Writes the FICSS working paper series and the introductory guides.
ORCID 0009-0007-6643-2307Calibration over prophecy
Computational Macrohistory rests on a distinction that matters: estimating structural conditions is a different problem from predicting events. The framework produces calibrated probability distributions, and the honest test of a macrohistorical model is calibration itself: whether its estimates held up across many cases over time.
This requires acknowledging uncertainty explicitly, publishing limitations alongside results, and accepting that some questions cannot be answered at any useful level of precision. FICSS treats those constraints as scientific commitments.