Computing Physical Reality. Philosophical Perspectives and Scientific Challenges

A special issue of Philosophies (ISSN 2409-9287).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 300

Special Issue Editors


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Guest Editor
Department of Human Science, University of Verona, Verona, 37129, Italy
Interests: philosophy of science

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Guest Editor
Department of Human Science, University of Verona, Verona, 37129, Italy
Interests: History and philosophy of science, historical epistemology of computation, history of physics

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Guest Editor
Department of Humanities, University of Napoli Federico II, Napoli, Italy
Interests: metaphysics and epistemology science; logic, and mathematics philosophical traditions

Special Issue Information

Dear Colleagues,

For centuries, humankind has attempted to decode and reconstruct the physical world. With the advent of digital computers—at once physical and formal systems—and their extraordinary computational power, the scope and breath of such an ambitious cultural project have increased enormously. An increasing number of realms of reality, including our brain and behaviour, now appear to be within the reach of computational analysis with an unprecedented degree of accuracy. This Special Issue of Philosophies focuses on a number of epistemological, historical, and conceptual issues concerning the interaction between this large computational effort and physics as a scientific and cultural phenomenon. The issues range from the problem of  demarcation between computing and non-computing systems to the question of new meanings indicated by logical and physical concepts, formal structures, and theories, and consequences on scientific practices (e.g., the relationship between theory and data; the epistemological value of computational modelling and simulation). More specifically, we invite contributions along the following three thematic lines:

1. Computation in physical systems and physics in (and for) computing systems.

If anything that computes is physical, whether or not anything physically computes is still open to debate. In fact, both possibilities raise a number of questions of different orders. If anything at all, what distinguishes physical computing systems such as laptops and nervous systems from other physical systems (stomachs, hurricanes, etc.) that we do not deem to be computing systems? How do the existing definitions of computation help us distinguish between non-computing and computing systems? Do our brains actually perform the computations that neuroscience and cognitive science say we do? What do we require of a physical system to perform reliable computation? How can computing systems—brain and cognition included—be modelled with the tools and concepts of physics (e.g., dynamical system theory)? What are the new meanings that some physical/computational concepts (e.g., predictability, uncertainty, randomness, information, and algorithmic complexity) acquire when applied to computer science/physics?

2. Computation in practice.

Besides the traditional data-scarce hypothesis-driven modelling of the world—which we employ anywhere from everyday situations to scientific theorising—a data-driven approach to modelling has emerged with machine learning. Are these approaches complementary or mutually exclusive? For instance, can a data-driven approach to problems be considered an exploratory tool in scientific research? How has that transformed the relationship between data (their collection, manipulation, and use) and theory in scientific practice? Does this new dimension add anything to the old debate on the conception of physical law, namely if laws express only synthetic regularities or instead causal relationship?

3. Epistemological value of simulation.

The increase in computational power has enhanced our capability to simulate a number of real-world situations and experiments that would otherwise be too expensive, hard or even impossible to explore and perform, respectively. Simulations are prima facie just graphical renderings of mathematical models, so what makes them more compelling? Where does their epistemological and methodological value stem from? How have advances in computation affected simulation and, in turn, the way we conduct science? What was the role of exploratory simulations (e.g., the Monte Carlo method, Fermi–Pasta–Ulam–Tsingou computational model, and Turing’s model of morphogenesis) in formulating new hypotheses or, more generally, in theory building?

Accepted contributions include original research articles, reviews, and short opinion articles, and may be directed to both specialists and the broader public of academics.

We look forward to receiving your contributions.

Prof. Dr. Massimiliano Badino
Dr. Rocco Gaudenzi
Prof. Dr. Rossella Lupacchini
Guest Editors

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Keywords

  • computation
  • physics
  • simulation
  • epistemology
  • scientific practices
  • approximation
  • computational systems
  • machine learning

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