Characterizing the interdependent nature of Anthropocene systems of systems is fundamental to making informed decisions to address challenges across complex ecological, environmental, and coupled human-natural systems. This paper presents the first application of Model-Based Systems Engineering (MBSE) and Hetero-functional Graph Theory (HFGT) to economic systems, establishing a scalable and extensible methodology for integrating economic input-output (EIO) models within a unified system-of-systems modeling framework. Integrating EIO models into the MBSE-HFGT workflow demonstrates how the structural form and function of economic systems can be expressed through SysML's graphical ontology and subsequently translated into the computational structure of HFGT. Using a synthetic Rectangular Choice of Technology (RCOT) example as a pedagogical foundation, the study confirms that the dynamics captured by basic EIO models, as well as other complex economic models grounded in EIO theory, can be equivalently reproduced within the MBSE-HFGT framework. The integration with MBSE and HFGT thus preserves analytical precision while offering enhanced graphical clarity and system-level insight through a shared ontological structure. By integrating modeling languages and mathematical frameworks, the proposed methodology establishes a foundation for knowledge co-production and integrated decision-making to address the multifaceted sustainability challenges associated with Anthropocene systems of systems.
Humans are the primary drivers of profound changes in Earth's systems, giving rise to myriad interconnected societal challenges that characterize the Anthropocene [1]- [3]. The concept of the Anthropocene describes the current stage of Earth system change in which human activities have emerged as a dominant driving force [4]- [8], influencing geophysical, biophysical, socioeconomic, sociocultural, and socio-technical processes [1]. Rapid population growth, widespread urbanization, and intensified resource exploitation are among human-induced pressures [9] that reshape the Earth's system trajectories and exacerbate intertwined social, ecological, and technological challenges [10], [11].
These challenges are inherently interconnected, as disturbances in one system can cascade across the entire system of systems (SoS), generating complex feedback and interactions [12]- [14]. For example, rapid economic growth contributes to climate change by requiring mass production, destabilizing local hydrological cycles, and changing energy-use regimes [15], [16]. The spectrum of Anthropocene challenges is extensive, encompassing global warming, overexploitation of natural resources, freshwater scarcity, habitat degradation, and widespread environmental pollution [6], [17]. These pressures threaten numerous endangered species and compromise human well-being [18]. Nonetheless, these challenges are frequently addressed in isolation, with relatively few studies adopting integrative approaches that explicitly account for their interdependencies [1], [18]- [21]. Although recent research acknowledges the interconnected nature of Anthropocene challenges [5], [22], [23], capturing synergies and trade-offs across systems remains complicated due to limited understanding [24], [25] and the absence of analytical frameworks capable of quantifying these complex relationships [1], [23].
The economic system constitutes a foundational and inseparable element of the Anthropocene SoS [26], [27]. It functions as a primary driver of global change while remaining intrinsically interwoven with and biophysically constrained by social and natural systems [28], [29]. This relationship is bidirectional [30], as the economic system exerts profound impacts on and is significantly influenced by the natural and social spheres, constituting a coupled social-economic-natural SoS [31], [32]. One manifestation of this is economic growth in the Anthropocene, particularly fossil-fuel-driven growth, which is responsible for the deterioration of Earth systems and the transgression of planetary boundaries [33], [34]. For example, studies indicate that projected biodiversity loss exceeds safe thresholds globally and rises significantly with GDP per capita, suggesting that continued economic expansion beyond planetary boundaries undermines the planet’s carrying capacity [35].
In contrast, the economic system is significantly influenced by the other systems through feedback mechanisms [2], [36]. For example, resulting environmental changes-such as global warming and extreme events-can suppress economic growth by inducing climate-related damages that compromise water resources, food production, and human health [37], [38]. Successfully anticipating the consequences of these interactions between the economic system and other interlinked systems, and effectively addressing sustainability challenges, requires models that go beyond traditional approaches, which often treat anthropogenic drivers as exogenous to the economic system [26], [39], [40]. Consequently, there is a critical need to develop SoS models that explicitly capture nonlinear, complex dynamics and interdependencies among the economic system and other interconnected systems [32].
Numerous studies have examined unidirectional and bidirectional causal relationships between the economy and hydrological [41]- [44], energy [45]- [48], food [49], and other interconnected systems [50]- [53] to assess tradeoffs and synergies. However, only a limited body of research explicitly conceptualizes the economy as an integral component of the broader complex SoS in the Anthropocene, in which multidirectional causalities link economic dynamics to other interwoven systems. In most of these studies, this lack of explicit integration is reflected in prevailing modeling practices [54], [55]. This highlights a predominant reliance on exogenously defined drivers, constraining the ability to capture the full complexity of cross-system interdependencies. Such limitations arise because modeling approaches for coupling the economic system with other entangled systems often: (i) lack the capacity to represent the full detail of multiple interacting domains, including economy [2], (ii) require simplification of key elements to reduce complexity [56], and (iii) face technical and interoperability barriers, as linking distinct models typically demands specialized interfaces [23]. Effective coupling strategies must balance complexity
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