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Building Theories: Heuristics and Hypotheses in Sciences

Cham: Springer International Publishing (2018)

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  1. The explanatory and heuristic power of mathematics.Marianna Antonutti Marfori, Sorin Bangu & Emiliano Ippoliti - 2023 - Synthese 201 (5):1-12.
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  • Mathematics and Finance: Some Philosophical Remarks.Emiliano Ippoliti - 2021 - Topoi 40 (4):771-781.
    I examine the role that mathematics plays in understanding and modelling finance, especially stock markets, and how philosophy affects it. To this end, I explore how mathematics penetrates finance via physics, constructing a ‘financial physics’, and I outline the philosophical backgrounds of this process, in particular the ‘philosophy of equilibrium’ and that of critical points or ‘out-of-equilibrium’. I discuss the main characteristics and a few weaknesses of these mathematizations of financial systems, notably econometrics and econophysics, and I compare the two (...)
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  • Scientific Discovery as a Topic for Philosophy of Science: Some Personal Reflections.Tom Nickles - 2020 - Topoi 39 (4):841-845.
    This is a brief, personal retrospective on developments in the treatment of scientific discovery by philosophers, since about 1970.
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  • The Antinomies of Serendipity How to Cognitively Frame Serendipity for Scientific Discoveries.Selene Arfini, Tommaso Bertolotti & Lorenzo Magnani - 2020 - Topoi 39 (4):939-948.
    During the second half of the last century, the importance of serendipitous events in scientific frameworks has been progressively recognized, fueling hard debates about their role, nature, and structure in philosophy and sociology of science. Alas, while discussing the relevance of the topic for the comprehension of the nature of scientific discovery, the philosophical literature has hardly paid attention to the cognitive significance of serendipity, accepting rather than examining some of its most specific features, such as its game-changing effect, the (...)
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  • Model-Based Inferences in Modeling of Complex Systems.Miles MacLeod - 2020 - Topoi 39 (4):915-925.
    Modelers are tackling ever more complex systems with the aid of computation. Model-based inferences can play a key role in their ability to handle complexity and produce reliable or informative models. We study here the role of model-based inference in the modern field of computational systems biology. We illustrate how these inferences operate and analyze the material and theoretical bases or conditions underlying their effectiveness. Our investigation reiterates the significance and centrality of model-based reasoning in day-to-day problem-solving practices, and the (...)
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