Mario Andrés Chalita

Robustness analysis has become increasingly central to contemporary discussions on the reliability of scientific models and results. This study develops a thermodynamic-inspired framework for assessing robustness, drawing on thermodynamic principles to explore how methodological diversity, measurement precision, technological frameworks, and environmental constraints shape scientific outcomes. Applied to the experimental and computational study of asphaltene precipitation in a fluid thermodynamics laboratory, it examines how results obtained in a controlled, highly parameterised setting behave when tested in less technologically structured environments. The transition from an optimised laboratory framework to more variable field conditions reveals a dynamic interplay between methodological and environmental variability. A key conclusion is that, in the worked example considered, validation outside the laboratory contributes more significantly to the robustness of results than the introduction of a novel, independently developed method within the laboratory. Beyond this specific case, the proposed framework provides a tool for evaluating scientific practices when laboratory conditions do not accurately replicate the studied phenomenon, emphasizing the role of environmental variability in robustness analysis.

About three decades ago, AI theory underwent a sharp turn as a consequence of criticism that pointed out the problem of externalism in the cognitivist position. Hubert Dreyfus, undoubtedly the main exponent of this criticism, opened the possibility of a Heideggerian reading using the frame problem to bring to light obscurities that otherwise would have been very difficult to detect. However, the question still remains of whether or not Heidegger’s philosophy can serve as the source of a positive contribution to AI. In this paper, we argue that in the small measure in which such a task has been attempted, its orientation has been hampered by the omission of what, for Heidegger, was the central issue to be pondered: the question for being and the ontological difference. To propose a possible direction in which AI can be headed as a consequence of this novel perspective, we undertake a brief and schematic review of two published projects suggesting that they both manage to avoid the frame problem and also bear this Heideggerian outlook.