Logic(s) in Defective Science 2022 (LiDS 2022)

This workshop is devoted to exploring connections between non-classical logics and the rational use of defective information in the sciences, as well as the inferential practices in the sciences—particularly, those which make use of defective information.

In recent years, there has been increasing interest in the logical constraints of scientific reasoning that make possible the rational use of defective—e.g. false, imprecise, conflicting, incomplete, inconsistent, partial, ambiguous, and vague—information in scientific contexts. On the one hand, for a variety of causes, scientific information is often inaccurate, poorly empirically supported, and not as relevant as it should be. As a matter of fact, the defective character of scientific data is not only ubiquitous but inevitable. Despite this, scientists have proven to be able to work with such defects and reach significant degrees of scientific success, such as accurate predictions, descriptions, and explanations. On the other hand, traditional formal approaches to scientific and—more broadly—human reasoning have not fully and properly explained why and how such success is achievable in defective contexts. However, recent works in philosophical logic have shown that any successful analysis of scientific reasoning must pay attention to: (i) the ways in which evidence and probability are actually employed in scientific practice, (ii) the logical connections that underlie different types of scientific explanations, as well as (iii) the historical evidence that shows that defective science is much more common in normal science than it is assumed in traditional approaches.

The purpose of this workshop is to discuss novel non-classical formal approaches to the use of defective information in the sciences. Particularly relevant for this assessment is the fact that different standpoints from logic and philosophy of science may provide novel methodological resources for providing fine-grained analyses of the scientific activity as well as heuristics for scientific practice.