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Nicolaus Copernicus University in Toruń
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Abstract: 
For a variety of causes, scientific information is often inaccurate, poorly empirically supported, and not as relevant as it should be. And although there are good reasons for still aiming for accurate, empirically supported and relevant information in the sciences, the defective character of scientific data is not only ubiquitous, but inevitable.

However, while the presence of defective information in science tends to be naturally seen as part of the dynamics of scientific development, it is a fact that the larger the defectiveness of the information that scientists work with, the less justified they are in trusting such information.

This course aims at addressing the different ways in which defective data is used in the sciences as well as the epistemic benefits and dangers associated with such toleration.  
Three specific objectives of the course are: 
  • To provide a systematic overview of the different views in which defective data is tolerated, explained and understood in the sciences.
  • To explain under which circumstances defective data could be considered reliable in scientific contexts.
  • To discuss some case studies (from different scientific disciplines) that illustrate the above.
In order to do so, the course addresses three main questions: what is to tolerate defective data? Can we understand defective data? When is defective data reliable in scientific contexts? 
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Syllabus  and ​
Sessions
syllabus.pdf
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Session 1:
What is to tolerate defective data?
  1. Generalities of defective data
  2.    Unreliability of defective data
  3.   Toleration of defective data
  4.      Case studies: Anomaly in the perihelion of Mercury // Stagflation

​Session 2:
Can we understand defective data?
  1. Brief recap
  2. Scientific understanding
  3. Understanding of defective data.
  4. Case studies: Standard Solar Model // Dictator game.

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Session 3:
The reliability of defective data
  1. Recap.
  2. Trust and reliability.
  3. The uses of defective data: abstractions, idealizations, fictions.

Session 4:
​The reliability of defective data
  1. Recap.
  2. Trust and reliability.
  3. New technologies and their defective data.
  4. Case study: Big data implementation in observational cosmology.

Sessions 1 and 2

Slides

SLIDES
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Readings session 1

 Bueno, O. (2017): “Scientific pluralism, consistency preservation, and inconsistency toleration", HUMANA. MENTE Journal of Philosophical Studies, 10(32), 229–245.
Martinez-Ordaz, M. del R. (2020): “The ignorance behind inconsistency toleration" in S.I. Knowing the Unknown, Synthese.
Bueno2017.pdf
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MartinezOrdaz 2020.pdf
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Readings Session 2

De Regt, H. W. and C. Baumberger (2019): “What Is Scientific Understanding and How Can It Be Achieved?" in What Is Scientific Knowledge?: 66-81.
Elgin, C. Z. (2017): “Exemplification in Understanding”, in Explaining
Understanding: New Perspectives from Epistemology and Philosophy of
Science, Routledge: 76—91.
Complementary 
  • ​Grimm, Stephen, "Understanding", The Stanford Encyclopedia of Philosophy (Summer 2021 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2021/entries/understanding/>.
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  • Gordon, Emma C., 2017, “Understanding in Epistemology”, The Internet Encyclopedia of Philosophy.
    https://iep.utm.edu/understa/
References
  • Šešelja, D. (2017). Scientific Pluralism and Inconsistency Toleration. HUMANA.MENTE Journal of Philosophical Studies, 10(32), 1-29. https://www.humanamente.eu/index.php/HM/article/view/31

  • Vickers, P. (2013). Understanding Inconsistent Science, Oxford University Press https://global.oup.com/academic/product/understanding-inconsistent-science-9780199692026?cc=mx&lang=en&
  • Meheus, J. (2002): “How to reason sensibly yet naturally from inconsistencies” in J. Meheus (ed.) Inconsistency in Science, Kluwer Academic Publishers, Netherlands. https://link.springer.com/book/10.1007/978-94-017-0085-6
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  • Moore, G. E., 1942, “A reply to my critics”, The Philosophy of G. E. Moore, edited by P. A. Schilpp. Evanston, IL: Northwestern University. ​

methodological issues about formal reconstructions of reasoning in defective contexts
  • Harman, G. (1984). “Logic and Reasoning”, Synthese, Vol. 60, No. 1, Foundations: Logic, Language, and Mathematics, Part I:107-127.
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  • Martínez-Ordaz, M. del R. (2022). A Methodological Shift in Favor of (Some) Paraconsistency in the Sciences. Logica Universalis. https://doi.org/10.1007/s11787-022-00302-y 

  • Michael, M. (2016): “On a “most telling” argument for paraconsistent logic", Synthese 193 (10).


Readings on ignorance
  • Arfini, S. (2020): The diffusion of ignorance in on-line communities (first author with T. Bertolotti and L. Magnani) in Information Resources Management Association (USA) (Ed.) Information Diffusion Management and Knowledge Sharing: Breakthrough in Research and Practice, USA: IGI-Global, pp. 843-857
  • Arfini, S. , C. Casadio and L. Magnani (2018): Ignorance-preserving mental models: Thought experiments as metaphorical abductions, Foundations of Science, 24(2), pp. 391-409, 

  • Le Morvan, P. and Peels, R. (2016). The nature of ignorance: Two views. In The epistemic dimensions of ignorance. Cambridge: Cambridge University Press.
  • Nottelmann, N. (2016). The varieties of ignorance. In Rik Peels and Martijn Blaauw (eds) The epistemic dimensions of ignorance. Cambridge: Cambridge University Press.
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  • Pelis, M. (2017). Erotetic epistemic logic. Logic and Logical Philosophy, 26(3):357–381. ​

Readings on the toleration of defective data
  • Batens, D. (2018). Pluralism in Scientific Problem Solving. Why Inconsistency is No Big Deal. HUMANA.MENTE Journal of Philosophical Studies, 10(32), 149-177.

  • Bueno, O. (2017): “Scientific pluralism, consistency preservation, and inconsistency toleration", HUMANA. MENTE Journal of Philosophical Studies, 10(32), 229–245.
    ​
  • Martinez-Ordaz, M. del R. (2020): “The ignorance behind inconsistency toleration" in S.I. Knowing the Unknown, Synthese.
Digital resources

Session 3

Martinez-Ordaz, M. del R. (forthcoming): “Is there anything special about the ignorance involved in Big Data practices?”, in Lundgren, B. L. and N. Nuñez-Hernández (Eds.) Philosophy of Computing, Philosophical Studies Series, Vol. 143.
MartinezOrdaz2022
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Session 4

Complementary
  • ​Leonelli, Sabina, "Scientific Research and Big Data", The Stanford Encyclopedia of Philosophy (Summer 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2020/entries/science-big-data/>.
María del Rosario Martínez-Ordaz:  φ of Science & φLogic