Quantifying AI’s Socio-Economic Futures: An Uncertainty-Aware Scenario Generation Framework

AI is increasingly understood as a general-purpose technology whose socio-economic effects depend on heterogeneous adoption paths, complementarities, and policy regimes; however, credible quantification remains hindered by deep epistemic, aleatory, and structural uncertainty. Building on this concept analysis, a unified uncertainty-aware scenario generation framework is advanced to connect micro-level mechanisms to macro outcomes. The central problem addressed is the brittleness of single-model projections that obscure distributional impacts and tail risks. The proposed methodology integrates structural causal models, agent-based/microsimulation, and macro-consistent aggregation (input–output/CGE linkages), and propagates uncertainty via Monte Carlo ensembles with global sensitivity diagnostics. Calibration and coverage are assessed using proper scoring rules and probability-integral transforms, with backcasting for external validation. Across illustrative economies, the framework yields probabilistic estimates for productivity, employment, wage dispersion, sectoral reallocation, and fiscal balances, and identifies key mediators (human-capital complementarity, diffusion speed, adjustment frictions). Results indicate that portfolio policies combining skills investment, targeted transition supports, and governance guardrails exhibit lower maximum regret than single-instrument approaches, while inequality spikes are most probable under rapid diffusion without adjustment. The impact is to replace point forecasts with decision-grade ensembles that transparently expose assumptions and uncertainty, thereby improving the robustness of economic policy, industrial strategy, and social protection design. Reproducible artifacts (code, schemas, diagnostics) are provided to support independent verification and iterative refinement. Keywords: artificial intelligence, socio-economic impacts, uncertainty quantification, scenario generation, structural causal models, agent-based modeling, input–output analysis, computable general equilibrium, Monte Carlo simulation, global sensitivity analysis, calibration and backcasting, robust decision-making, policy evaluation, inequality, productivity