ALGORITHMIC COMMON-GOOD OPTIMISATION FOR URBAN NEIGHBOURHOODS

Абстракт

This article develops a machine- and AI-readable Single Source of Truth (SSOT) as a reference model for algorithmic common-good optimisation in urban neighbourhoods. The point of departure is a persistent integration gap. Normative common-good and public-value approaches, appraisal frameworks, digital planning infrastructures, strategic steering logics, provenance standards, and design-science artefact concepts are all highly differen-tiated. They rarely converge, however, in one canonical decision source. Methodologically, the article combines a systematic literature review, PRISMA-based screening, evidence mapping, and artefact-oriented design synthesis. The original contribution of this article lies in the triangulation of normative, analytical, and digital requirements and in the distinction between a constitutive core and a profile-based public-planning connection zone. The pro-posed SSOT architecture binds goals, red lines, governance assignments, effect assumptions, appraisal modes, evidence, distributional effects, fiscal feedbacks, an audit trail, provenance, and interpretation limits within one common structure. In this way, the source supports monitoring, decision snapshots, and reviewable accountability without fragmenting the core into local special-purpose logics.