At least since the scientific revolution, interpretable mathematical models have been instrumental for advancing our understanding of the world. The “big data” era held the promise of facilitating the discovery of similarly interpretable mathematical models of natural and socio-economic systems that were previously not amenable to quantitative analysis. Yet, so far we have not seen such an explosion of new interpretable mathematical models. This is in part because machine learning models are de facto taking their place. However, because most machine learning algorithms are not interpretable, an uncontrolled use of such approaches can have unwanted consequences when model outcomes are directly linked to decisions.
Statistical physics approaches precisely rely on using interpretable micro-scale models to understand macro-scale behavior and as such they are uniquely positioned to lay the foundations of alternative algorithms for interpretable model selection and validation that will learn from data but that will significantly differ from the machine learning we know today.
A particular setting in which the need of better interpretable models is critical is that of socio-economic systems, and especially cities, where understanding the micro-motives of human behavior is necessary to explain the macro-behavior of those systems, and to inform policy-making decisions. Unfortunately, despite the fact the statistical physics contributions to modeling urban phenomena, most of the used tools do not go beyond the “bottom-up” theoretical metaphor. However, because of the expected growth of cities at a global scale in the next decade and the fact that more urban data is available, there is a pressing need to be able to obtain interpretable models for urban social contexts which are informed by data and that can be validated within an urban setting.
StatPhys4Cities will take on these challenges in a coordinated effort that will contribute and advance the research of urban-related problems from a statistical physics approach that combines models and methods from network theory, stochastic processes, and critical phenomena, among others with a data-driven approach. Specifically, StatPhys4Cities has two overarching goals:
1) To develop interpretable model selection and validation tools using statistical physics principles. The tools should also inform the process of obtaining further data to answer a specific research questions.
2) To gain a better understanding about mobility, welfare and inequalities within cities through the analysis/modeling/interpretation of existing data and the acquisition of new data specific to these problems.
Our developments are expected to cross disciplinary boundaries because of the pressing need of scientists in life and social sciences to exploit the large amounts of data they have. StatPhys4Cities will spearhead the scientific community working on cities in the adoption of powerful state-of-the-art methodologies for model building from data. Results from StatPhys4Cities will also have a deep impact on citizens concerned about specific urban issues in mobility, welfare and inequality by enabling a common participatory and inclusive research (including gender issues and vulnerable groups). Policy makers will also receive novel approaches to accurately model and understand the effect of their policies and have the capacity to anticipate future scenarios based on scientific grounds.
