Mapping Technological Civilization in State Space: From Planetary Vital Signs to Multidimensional Risk Configurations

Abstract

Global-risk assessments are usually communicated as lists, rankings, likelihood–impact matrices or networks of interconnections. These instruments are useful, but they represent poorly a basic property of the world system: its condition is a simultaneous configuration of many interacting variables, and systemic risk is typically a property of that configuration rather than of any single variable. This paper proposes a state-space representation in which technological civilization is described by a vector X(t) of normalised macro-coordinates, global problems correspond to occupied regions, emerging threats to regions towards which the trajectory may move, and systemic risks to regions in which the response to perturbations changes qualitatively. The framework distinguishes causal levels, state space and the parameter space of quantities that shape its geometry. Interactions among megatrends are represented through a second-order pressure functional and an anisotropic metric based on an independently assessed interaction matrix. As a proof of concept, nine macro-coordinates are constructed from twelve public annual series for 2000–2025. The first principal component, representing a near-uniform common secular trend, accounts for 83.3% of the variance and is almost monotonic in time (ρS = 0.99); a second, financial-system component (9.7%) isolates the acute excursions of 2008–2011 and 2020. No consistently dominant common mode remains after first differencing or detrending. The displacement from the 2000–2004 baseline becomes progressively aligned with the dominant mode of the adopted interaction matrix, reaching an alignment cosine of 0.97 in 2025. However, trend-preserving null models show that most of this alignment is explained by common secular drift, and random relabelling of the coupling matrix cannot be distinguished from the observed result (p = 0.14). The main descriptive results are robust to normalisation, observation window and assumed perturbations of the input series. The framework is therefore offered as a representational and diagnostic tool rather than a forecasting model. It shifts the analytical question from “which risks matter most?” to “where is the global system, how is it moving, and which risk-generating regions is it approaching?”.