My work focuses on understanding and appraising uncertainties in contentious phenomena, especially those related with agriculture, climate and water resources. My research aims at providing insights for sustainability and makes use of mathematical models, uncertainty and sensitivity analysis, post-normal science and ethics of quantification. I am currently the PI of the project Illuminating Deep Uncertainties in the Estimation of Irrigation Water Withdrawals (DAWN), funded by the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee.

Mathematical models can be powerful tools for scientific inquiry. They can also misguide us into an illusion of knowledge if they artificially compress fundamental uncertainties in the system of interest. This paradox lies at the heart of modelling: while models strive to capture real-world phenomena with mathematical rigour, the very nature of these phenomena often resists such formalisation. We should therefore know how (not) to model as well as know when (not) to model. In our current context of socio-environmental challenges and conflicting values, where stakes are high, decisions urgent and uncertainty deep, our approach to models should be one of care, humility and critical reflexivity to avoid being misled by unwarranted precision.

Having uncertainty as the foundation of my work, my approach to research is one of via negativa, where understanding is best achieved by removing what does not work or is faulty rather than at pursuing positive truths. In model-based research, this implies using models mostly as tools to falsify or disprove, or as maps to explore how what we know  – and what we do not know – shapes our perception of a given phenomena. This approach understands model-based science à la Mertonian; that is, as a system of organised skepticism to pursue rigour, avoid dogmatism and ensure that only the most robust concepts and explanations enter the body of knowledge –until they, too, are ultimately replaced.