Modelling and Inferring in Science -- On ‘The Unreasonable Effectiveness of Mathematics in the Natural Sciences’ -- Fast and Frugal Heuristics at Research Frontiers -- Scientific Realism, the Semantic View and Evolutionary Biology -- Models of the Skies -- Models of Science and Models in Science -- Mechanistic Models and Modeling Disorders -- Chaos and Stochastic Models in Physics -- Ways of Advancing Knowledge. A Lesson from Knot Theory and Topology -- Models, Idealisations, and Realist Commitments -- Modelling Non-Empirical Confirmation -- Mathematics as an Empirical Phenomenon, Subject to Modeling -- Scientific Models Are Distributed and Never Abstract. A Naturalistic Perspective -- The Use of Models in Petroleum and Natural Gas Engineering.

The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science.