Planning long-term national development under uncertainty is difficult for small states, where indicators are fragmented, frequently revised, and span tightly coupled social, economic, and environmental domains. Scenario tools can support such decisions without committing to a single forecast, but they often lack historical grounding, cross-domain consistency, reproducible update pipelines, and explicit links between policy choices and outcomes. We present the Andorra Scenario Engine, a transparent, data-grounded framework that harmonizes multi-source national indicators into a consistent state vector and propagates four contrasting pathways—Continuity, Overgrowth, Degrowth, and Density—to 2049 through bounded, coupled update rules. The engine belongs to the exploratory-modelling tradition: an upstream, auditable layer toward a national digital twin rather than a forecasting system, whose outputs are internally consistent conditional futures. A central structural modelling choice is to derive population endogenously from GDP growth via a lagged labour-immigration elasticity (0.40 at a one-year lag, 0.10 at two years), grounded in IMF (2025) and World Bank data. A back-cast benchmark quantifies the accuracy cost of this choice (9.5% population RMSE against 1.7% for a linear trend), accepted in exchange for a policy-relevant causal lever. Three macroeconomic parameters and a housing-affordability anchor are calibrated to 2010–2024 official statistics (Nelder–Mead; RMSE = 1.77% on real GDP per capita). Scenario endpoints for 2049 range from 70,049 (Degrowth) to 174,002 (Overgrowth); a sensitivity audit identifies the housing-affordability threshold and the lag-1 elasticity as the load-bearing assumptions on which the headline signals depend. The framework provides a reproducible, human-in-the-loop baseline for comparing policy-relevant trade-offs and binding constraints in data-scarce microstates.
Ramentol et al. (2026) studied this question.