Sustainability challenges often involve reasoning, managing and deciding on complex, interconnected global systems spanning across different sectors: examples include energy networks, financial markets, natural ecosystems, and cities.
In principle, using optimization methods to support high-level decision making activities in those contexts may lead to dramatically more sustainable and efficient policies. In practice, the kind of systems considered in computational sustainability can be an optimizer's worst nightmare: they typically involve complex infrastructures, organizations, laws and processes; they influence and are influenced by the environment, and are strongly perturbed by human behavior; they feature multiple actors, often self-interested and with conflicting objectives. The classical, expert-driven, modeling approach used in optimization has a very hard time coping with situations where the experts themselves are incapable of providing precise, non-ambiguous definitions of the problem constraints and goals.
We argue that dealing with systems of such a complexity calls for a strong integration of data science and optimization, raising interest in technique that try and bridge the gap between the two fields. Empirical Model Learning (EML) is one such technique: it is a methodology for learning model components directly from data and for actively using these components to prune the search space or guiding the solution process.
We basically have to learn relations between decidables (alternative decisions we can take) and a observables of interest. The data for the learning process can come from historical measurements or be collected by running simulations. These relations can be extracted in the form of classical Machine Learning models (e.g. Neural Networks, Decision Trees), and EML defines methods to cast such models into constraints and objective functions that can be readily incorporated into existing optimization technology.