Combining Quality of Life Indicators and AI Simulations for Human-Centric Governance
Abstract
Purpose
Although human-centric governance emphasizes subjective well-being, a structural gap persists between retrospective Quality of Life (QoL) measurement and anticipatory decision-making. This article addresses this gap by asking: how can validated QoL indicators and Large Language Model (LLM)-based simulations be systematically integrated within a coherent governance framework?
Design/methodology/approach
The study adopts a structured dual-track research design. Study 1 employs a quasi-experimental mixed-methods evaluation of a Norwegian-funded social innovation project using the “Impactometer,” grounded in OECD-aligned subjective well-being scales. Study 2 develops a proof-of-concept LLM-based simulation (Bielik model) applying the Ryff Psychological Well-Being Scale to synthetic professional personas in an organizational policy scenario. The two studies are analytically distinct but conceptually integrated within a governance cycle linking empirical grounding and anticipatory modeling.
Findings
Study 1 demonstrates an observed increase in life satisfaction (+0.8 points), accompanied by qualitative evidence of enhanced meaning, autonomy, and social connectedness. Study 2 shows that LLM-based simulation can generate differentiated and internally consistent well-being distributions across professional roles. However, simulation results establish plausibility rather than predictive validity and require empirical validation.
Originality/value
The article advances governance theory by proposing and empirically illustrating a hybrid model that bridges validated well-being measurement with structured AI-based simulation. This integration addresses the temporal mismatch in evidence-based policy and clarifies the methodological and ethical conditions under which AI can complement, rather than replace, human-centered governance.
© 2026 Katarzyna B. Wojtkiewicz, Igor Lyubashenko, published by Jagiellonian University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.