Decoding systemic risk: An orthogonal interaction framework integrating systemic vulnerability and system-wide disruption

Systemic risk has emerged as a key feature of modern society, reflecting the growing complexity and interconnectivity of socio-ecological-technical systems. The concept is widely understood as the probability that disturbances cascade within a system or across interconnected systems, generating disproportionate, system-wide disruption (e.g., Kaufman and Scott, 2003; Sillmann et al., 2022; Gambhir et al., 2025). An alternative perspective frames systemic risk through the manifestation of systemic vulnerability, which is defined as the enduring, cross-scalar core of vulnerability that persists over time despite societal and technological advancements, mitigation efforts, or changes in hazard regimes (Armaș et al., 2025). Although they address systemic risk from different but complementary angles, these perspectives remain largely disconnected in both theory and application.

This study advances an integrative analytical framework aiming to clarify how systemic risk emerges at the intersection of these two presented perspectives, also showing that interdependence, nonlinearity, and feedback processes fundamentally shape impact dynamics. The primary focus of analysis is the interaction type, which functions as the fundamental unit for diagnosing cascading and compounding dynamics. Interaction types are organised along three orthogonal dimensions: mechanism, topology, and timing.

We also argue that understanding systemic vulnerability is essential for diagnosing systemic risk. The Systemic Vulnerability Model illustrates how such vulnerabilities reinforce impacts, fuel feedback loops, constrain recovery, and shape the likelihood of systemic collapse. Complementing this, Self-Organised Criticality (SOC) provides a theoretical underpinning that explains why, in highly connected systems, the accumulation of systemic vulnerability lowers certain system thresholds and leads to critical states. In these states, minor perturbations can trigger disproportionately large, system-wide failures, producing heavy-tailed loss distributions that challenge linear assumptions about hazard magnitude and impact.

The proposed analytical framework is intended as a conceptual and diagnostic tool rather than a predictive model. We do not propose a new definition of systemic risk but address the research gap on harmonising the currently disjoint discourse on systemic risk, supporting clear foundations for future studies on this topic. To continue this work, we aim to operationalise and empirically evaluate this analytical framework across diverse domains.