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Toward Model-Driven Sustainability Evaluation

glass Earth in human hands

Credit: Rangizzz /

Sustainability—the capacity to endure—has emerged as a concern of central relevance for society. However, the nature of sustainability is distinct from other concerns addressed by computing research, such as automation, self-adaptation, or intelligent systems. It demands the consideration of environmental resources, economic prosperity, individual well being, social welfare, and the evolvability of technical systems.7 Thus, it requires a focus not just on productivity, effectiveness, and efficiency, but also the consideration of longer-term, cumulative, and systemic effects of technology interventions, as well as lateral side effects not foreseen at the time of implementation. Furthermore, sustainability includes normative elements and encompasses multi-disciplinary aspects and potentially diverging views. As a wicked problem (see the sidebar "Wicked Problems"), it challenges business-as-usual in many areas of engineering and computing research.

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The complexity of these integrated techno-socioeconomic systems and their interactions with the natural environment is driving attention in several areas. These areas include means for understanding the emergent dynamics of these interactions and supporting better decision making through predictive simulation and system adaptation. At the heart of this is the notion of a model, an abstraction created for a purpose. Models are used throughout sustainability research (for example, for hydrology or pollution analysis) as well as software engineering (for example, for automated code generation). Models have a long history in research related to sustainability. The Global Modeling (GM) initiatives that started in 1960s and 1970s developed and used large mathematical dynamic global models to simulate large portions of the entire world.13 GM in general was applied to human decision-making in domains such as economics, policy, defense, minimization of poverty, and climate change. The goal of GM is to offer a prediction of the future state of the world, or parts of it, using (perhaps heavily) mathematical equations and assumptions. Mathematical models offer a framework of stability that is useful in domains such as climate modeling, but it may not be the same in the case of social sciences domains.


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