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Formal Epistemology and Philosophy of Science


Formal Epistemology and Philosophy of Science


Anno accademico 2020/2021

Codice attività didattica
Jan Michael Sprenger (Titolare del corso)
Corso di studio
laurea magistrale in Filosofia
Philosophy International Curriculum M.A.
1° anno, 2° anno
Caratterizzante - Ambito: Istituzioni di filosofia
SSD attività didattica
M-FIL/02 - logica e filosofia della scienza
Tipologia esame
Tipologia unità didattica

Elementary knowledge of logic and philosophy of science, interest in scientific reasoning. The course does not presuppose knowledge of the theory of probability, or other mathematics. Students without relevant background are welcome, but you may have to do extra work for filling the gaps. The course is also of interest for continentally oriented philosophers.


Sommario del corso


Obiettivi formativi

Is science a rational endeavor? Which role should it play in modern society? To what extent can scientific reasoning contribute to answering fundamental philosophical questions? And how can philosophy contribute to improving scientific reasoning?

The course explores these and other questions in a way that combines tutorials in uncertain reasoning (e.g., probabilistic and statistical inference) with a philosophical debate on how science works, why it is often successful and what are its limits. The course challenges the students both on a conceptual level – e.g., we debate various explications of complex concepts such as causation and theory confirmation – and on the level of formal reasoning, where they are familiarized with basic concepts of statistical inference and techniques for causal inference (e.g., causal Bayes nets).

The technical parts of the course are no "l'art pour l'art", but an essential prerequisite for understanding debates about the reliability and fruitfulness of modern science, and for being able to form a balanced judgment about these topics. Achieving this judgmental capacity is, ultimately, the main objective of this course.


Risultati dell'apprendimento attesi

After following the course, students will be able to answer the question what distinguishes science from other human endeavors, and to which degree (and in which respects) it is rational. They can also assess the scope and limits of scientific knowledge, they understand why science is often successful, and they appreciate the significance of scientific reasoning for other kinds of human endeavor. Students gain an uinderstanding of what scientific reasoning is like, and why understanding science is important for solving foundational epistemological questions in philosophy. They also learn about the place of science in modern society and the role science plays in guiding public policy and complex decision-making (e.g., economic decisions, climate policy, and so on).



The course introduces the students to central debates in philosophy of science, such as the rationality and progress of science, causation, induction, scientific objectivity, values in science, and so on, with a focus on contemporary debates. These topics are mainly approached from an epistemological perspective. Questions we discuss are:
– What distinguishes genuine, knowledge-generating science from pseudo-science such as astrology?
– Can the problem of induction (i.e., challenging the rationality of inferring from past to future experience) be solved?
– What are the standard models of causation developed in philosophy and the sciences?
– Can science be free of political, religious and social values? (Think of research on gender or race differences, for instance.) Would this value-freedom be desirable in the first place?
– Can science be trusted in spite of the large uncertainties it comes with? Should it guide policy decisions?
– Why is the epistemology of science so important for a philosophical study of human knowledge?

The course is structured as follows:

  • Week 1 and 2: Scientific Rationality. Recapitulation of classical contributions (Popper, Kuhn) and its application to "creation science". Exposition of the historical lines going back to logical empiricism (Carnap, Reichenbach).
  • Week 3 and 4: Causation and Bayes Nets. Discussion of David Lewis' counterfactual model of causation, contrast with probabilistic causality and introduction to tools for causal modeling with Bayes nets.
  • Week 5: Statistical Inference, Value-Freedom and Scientific Objectivity. Introduction to essential concepts of statistical reasoning and application to various ongoing debates, such as the replication crisis and scientific objectivity.
  • Week 6: The Incentive structure of science and a socio-epistemic perspective on scientific inference.


Modalità di insegnamento

Mixture of lectures and seminar. Active contribution from the participants is a prerequisite for passing the course.


Modalità di verifica dell'apprendimento

Essay and written exam. Students can choose a topic for the essay (ca. 3,000 words) in agreement with the teacher. Essay and sit-in exam count equally for the final grade and make up ca. 80-90%. The rest of the grade is determined by class partecipation and a homework exercise in causal modeling, to be done on the computer.


Testi consigliati e bibliografia

The extended list below covers most of the texts that we will read in class, or are suggested as background readings. However, it is not required to read them before the start of the course. For students who are not familiar with philosophy of science, I recommend the following introductions:

– Curd, M., and Cover, J.A. (1998, eds.): Philosophy of Science: The Central Issues. Norton, New York.

– Okasha, S. (2002): Philosophy of science. A very short introduction. Oxford: Oxford University Press.

– Staley, K. (2014): An introduction to the philosophy of science. Cambridge: Cambridge University Press. 

I recommend to buy one of these books in English so that you are already familiarized with the terminology that we use in class. That said, introductions to philosophy of science in Italian include:

  • Okasha, S., Il primo libro di filosofia della scienza, Einaudi. (translation of Okasha 2002)
  • Gillies, D. e Giorello, G., La filosofia della scienza nel XX secolo, Laterza.
  • Ladyman, J., Filosofia della scienza. Un’introduzione, Carocci.

Extended List of Readings:

  • Carnap, Rudolf (1930): Die alte und die neue Logik. Erkenntnis 1: 12--26.
  • Douglas, Heather (2000). Inductive Risk and Values in Science. Philosophy of Science 67, 559–579.
  • Ioannidis, John P. A. (2005). Why Most Published Research Findings Are False. PLoS Medicine 2.
  • Heesen, Remco (2018). Why the Reward Structure of Science Makes Reproducibility Problems Inevitable. Journal of Philosophy 115, 661–674.
  • Hempel, Carl G. (1965). Aspects of Scientific Explanation. New York: The Free Press.
  • Hitchcock, Christopher (2001). The Intransitivity of Causation Revealed in Equations and Graphs. Journal of Philosophy 98, 273–299.
  • Kitcher, Philip (1990). The Division of Cognitive Labor. Journal of Philosophy 87, 5–22.
  • Kuhn, Thomas S. (1977a). Objectivity, Value Judgment, and Theory Choice. In The Essential Tension, pp. 320–339. Chicago: University of Chicago Press.
  • Kuhn, Thomas S. (1977b). The Essential Tension: Selected Studies in Scientific Tradition and Change. Chicago: Chicago University Press.
  • Laudan, Larry (1982). Science at the Bar—Causes for Concern. Science, Technology and Human Values 7, 16–19.
  • Lewis, David (1973). Causation. Journal of Philosophy 70, 556–567.
  • McMullin, Ernan (1982). Values in Science. In Proceedings of the Biennal Meeting of the Philosophyof Science Association, pp. 3–28.
  • Open Science Collaboration (2015). Estimating the Reproducibility of Psychological Science. Science 349. Retrieved from
  • Pearl, Judea (2000). Causality. Cambridge: Cambridge University Press.
  • Popper, Karl R. (1959/2002). Logic of Scientific Discovery. London: Routledge. Reprint of the revised English 1959 edition. Originally published in German in 1934 as “Logik der Forschung”.
  • Popper, Karl R. (1963). Conjectures and Refutations: The Growth of Scientific Knowledge. London: Routledge.
  • Romero, Felipe (2017). Novelty vs. Replicability: Virtues and Vices in the Reward System of Science. Philosophy of Science 84, 1031–1043.
  • Rudner, Richard (1953). The Scientist qua Scientist Makes Value Judgments. Philosophy of Science 20, 1–6.
  • Ruse, Michael (1982). Creation Science is Not Science. Science, Technology and Human Values 7, 72–78.
  • Strevens, Michael (2003). The Role of the Priority Rule in Science. Journal of Philosophy 100, 55–79.

Moreover, students are advised to study the relevant entries of the Stanford Encyclopedia of Philosophy, especially for technical topics (e.g., probabilistic causation, causal models, Simpson's paradox, philosophy of statistics, Bayes' theorem).

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