systems thinking

Contemporary pipeline transportation theory is fragmented across disciplines and lacks integration, rendering it inadequate for addressing 21st-century infrastructure challenges related to systemic resilience and sustainability. To bridge this gap, this study proposes the General Theory of Pipeline Transportation (GTPT), introducing a novel “triple-domain coupling” paradigm integrating physical systems, full life-cycle dynamics, and socioeconomic dimensions—with resilience as the central design principle and the UN Sustainable Development Goals (SDGs) as quantifiable operational criteria. Through systematic literature synthesis, cross-domain dynamical modeling, SDG-aligned metric embedding, and quantitative resilience assessment, we establish the first axiomatized theoretical framework for pipeline transportation. GTPT provides unified guidance for planning, operation, maintenance, and governance of critical infrastructure, significantly enhancing long-term adaptive capacity and sustainability-oriented decision-making. It offers a transferable methodology for resilient infrastructure development across multiple sectors.

The United Nations Sustainable Development Goals (SDGs) exhibit strong interdependence and dynamic feedback, making the identification of systemic leverage points for resource allocation a critical challenge in policy optimization. Method: We develop the first dynamic network model grounded in an extended Lotka–Volterra framework, integrating a PCA-weighted coupling network with fourth-order Runge–Kutta (RK4) numerical integration to overcome limitations of static analyses. Sensitivity analysis and power-law relationship testing are employed to quantify cross-goal influences. Contribution/Results: Our analysis identifies SDG 4 (Quality Education) as the global keystone driver. Empirical application to Mexico demonstrates that prioritizing investment in SDG 4 generates the strongest positive spillover effects, significantly enhancing synergistic progress across multiple SDGs. The proposed framework establishes a computationally tractable and empirically verifiable paradigm for identifying high-impact policy levers in sustainable development planning.

Causal Loop Diagrams (CLDs) are qualitative and static, limiting dynamic analysis and effective intervention; existing quantitative approaches—such as network centrality analysis—often yield spurious inferences. To address this, we propose D2D: a method that automatically transforms CLDs into exploratory system dynamics models. Leveraging a variable-typing annotation protocol, D2D integrates link existence and polarity information to construct simulatable, intervention-capable dynamic models—even without empirical data. D2D identifies high-potential leverage points under uncertainty, provides quantitative uncertainty assessment, and guides targeted data collection. Experiments demonstrate that D2D significantly outperforms network centrality analysis in leverage-point identification accuracy and achieves higher consistency with data-driven models. We have open-sourced a Python package and a web application to advance CLDs toward computable, intervention-aware modeling paradigms.

This paper addresses the indirect environmental risks of artificial intelligence (AI) at the systems level—beyond conventional energy-consumption metrics—by examining its embeddedness in socioeconomic structures and physical infrastructure, and its cascading, nonlinear, and uneven impacts on climate systems, biodiversity, and freshwater resources. Methodologically, it develops a novel three-layer systemic risk framework: (1) structural enabling conditions, (2) risk amplification mechanisms, and (3) observable socioecological consequences. The framework is empirically validated through a narrative literature review, in-depth expert interviews across disciplines, and comparative case studies in agriculture, oil & gas, and solid waste management. As the first scalable analytical paradigm for AI-related environmental risks, it identifies latent ecological negative externalities, thereby establishing a theoretical foundation and anticipatory governance pathway for sustainable AI development. (149 words)

This paper addresses structural risks arising from the deep integration of AI into socio-technical systems—emergent threats (e.g., eroded trust, power asymmetries, decisional authority degradation) that transcend technical failures and malicious misuse, stemming instead from systemic coupling and feedback-driven evolution. Methodologically, it first systematically classifies three root causes: pre-existing societal structural vulnerabilities, inherent AI system design flaws, and their interaction-induced vicious feedback loops; it then develops a dynamic risk analysis framework integrating scenario mapping, system dynamics simulation, and exploratory foresight. The study identifies cross-level structural vulnerability points and proposes policy pathways to strengthen institutional resilience and adaptive governance. Its contributions include a novel, theoretically grounded yet operationally viable paradigm for global AI governance—one that advances both conceptual rigor and practical applicability in addressing AI’s systemic societal impacts.

This study addresses the absence of a unified framework in current research on systemic risks posed by artificial intelligence, which often overlooks complexity, emergent dynamics, and cross-domain externalities, thereby undermining effective governance. For the first time, it integrates concepts of emergence and collective action dilemmas from complex systems theory into AI risk analysis, synthesizing perspectives from institutional economics, complex systems science, and AI governance to establish a novel paradigm centered on the emergence of societal and global-scale harms. Through conceptual modeling and interdisciplinary theoretical analysis, the work proposes a systemic risk framework encompassing structural dominance, cascading failures, and information asymmetries, offering a robust theoretical foundation for AI policy formulation and risk governance.

This study proposes a rigorous, optimization-free definition of sustainability from a systems dynamics perspective, characterizing the sustainability of the coupled Earth–human–production system as a geometric property: namely, that system trajectories almost surely avoid collapse boundaries. By constructing a stochastic differential equation model with multiplicative noise and employing Feller boundary classification alongside stochastic Lyapunov methods, the work integrates a reflexive social evaluation functional and endogenous biodiversity. It identifies the sign of net cross-subsystem flows as a critical order parameter governing phase transitions. The analysis demonstrates that when net inter-system flows are negative in a neighborhood of zero, collapse occurs almost surely. Sustainable development requires the evaluation functional to be locally increasing and positively aligned with survival probability, yielding the first path-dependent welfare comparison mechanism that does not rely on optimization assumptions.

This study addresses the challenge of insufficient subsurface pipeline condition awareness in data-scarce regions, such as the U.S. Virgin Islands, which hinders effective inspection and maintenance decisions. The authors propose a repair-oriented decision-making framework for water distribution network maintenance, formulating the problem as a discounted Markov decision process coupled with high-fidelity hydraulic simulation. Relying solely on readily available system-level observations, the framework infers latent pipe conditions by establishing a unique mapping between observable system dynamics and failures in specific pipe segments, thereby enabling virtual sensing without segment-level instrumentation. The approach explicitly captures heterogeneous failure characteristics across pipe segments and generates state-dependent optimal maintenance policies, demonstrating the feasibility of dynamic-system-based, resource-efficient inspection planning under constrained conditions.

This study addresses the absence of formal frameworks for characterizing when public trust in algorithmic governance institutions stabilizes or collapses. By coupling Friedkin–Johnsen opinion dynamics with a Hawkes-type intensity process modeling AI controversy events, the authors construct a bidirectional interaction model and derive precise spectral stability criteria through spectral graph theory and dynamical systems analysis. The analysis uncovers counterintuitive phenomena—such as highly trusted systems exhibiting structural fragility and low-trust environments proving more stable—and demonstrates that self-excitation of events and persistent memory substantially narrow the parameter regime permitting stability. Although network topology can reshape equilibrium heterogeneity, its influence on spectral stability is provably bounded above in memory-dominated regimes. Crucially, the work establishes that system stability is not equivalent to fairness or legitimacy.

This work addresses the misalignment between capability boundaries and governance boundaries in current AI systems, which engenders uncontrolled risks and renders formal regulatory mechanisms ineffective. To resolve this, the paper introduces a “coterminous governance” framework that mandates strict alignment between these boundaries. Leveraging Rice’s theorem, it proves that behavioral governance is undecidable under Turing-complete architectures, thereby necessitating governance to be intrinsically embedded within system design rather than imposed ex post facto. The authors realize this principle through an architecture that decouples computation from effect, integrating governance checks directly into the execution pipeline instead of relying on a separate oversight layer. Using Coq-based formal verification—encompassing 454 theorems across 36 modules—the study establishes coterminous governance as a necessary criterion for verifiable AI governance systems.