A curvature-based structural complexity measure for improving decision quality in project management analytics

Project managers routinely rely on key performance indicators (KPIs) and statistical process control tools — such as Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) — to monitor project dynamics. Yet evidence on whether structural complexity metrics improve strategic decision quality, rather than merely deviation detection, remains scarce. This paper presents an exploratory controlled between-subjects experiment evaluating the decision-quality impact of the Systemic Complexity Index (SCI), a curvature-based metric derived from Riemann–Christoffel​ tensor formalism, against KPI-only, KPI+EWMA, and KPI+CUSUM information conditions. Thirty-two human professionals and 8 large language models (LLMs) evaluated four decision scenarios derived from real project data (256 observations total), with decision quality operationalized through the Decision Quality Index (DQI). KPI+SCI was the only condition producing a statistically distinguishable improvement, with large effect sizes in both LLMs (d=2.716) and humans (d=1.055, DQI=＋0.172 humans, ＋0.155 AI), driven exclusively by Causal/Structural Alignment and operating through a mediated pathway — systemic comprehension fully mediates the effect in humans and partially in LLMs. Fisher Z meta-analysis across five independent projects yields r=0.767 (95% CI [0.44, 0.91], I ), establishing the preliminary SCI’s predictive evidence and positioning it as a potential structurally unique complement to conventional monitoring tools, offering decision-making value precisely where statistical process control methods are limited.