Philosophers, computer scientists, and experimental scientists all share a concern for determining what patterns — what natural laws — are learnable from finite data, and with identifying which methods of learning are reliable. Because these disciplines speak different languages, this overlapping interest is often missed, and results from one field that address the problems of another find one another with too little frequency. This workshop is intended to address this problem by teaching philosophers how to give their theories of inductive inference purchase on the empirical world, by showing practitioners of machine learning how their concerns fit into a broader scheme of scientific inference, and by helping researchers in the experimental sciences recognize how the abstract results in formal epistemology and machine learning can provide concrete practical benefits in the lab.


Topics to be covered include:
– the problem of induction
– the logic of discovery
– statistical learning theory
– algorithmic learning theory
– causal discovery algorithms
– the problem of natural kinds
– variable choice and model parameterization
– the history of automated discovery algorithms
– experimental design
– basic elements of programming in Python
– hardware interfacing with the Raspberry Pi, SPI, and I2C
– basic elements of student cognition and effective teaching


Participants in the workshop will…
(1) …conduct a series of experimental and programming tasks designed to provide insight into the nature and implementation of both historically important and cutting-edge algorithms for automated scientific discovery. In these exercises, data will be drawn exclusively and directly from measurements of the real world using a Raspberry Pi to host a collection of sensors and actuators.

(2) …engage in philosophical discussions designed to illuminate the shared objectives and complementary methods of philosophy, machine learning, and experimental science.

(3) …share what they’ve learned by assisting in a “Robot Scientist” outreach event for secondary-school students at the Western Virginia Museum of Science. In the final two days of the program, participants will travel to Roanoke to help young students learn about the nature of scientific investigation and the use of computers to probe the physical world.


Aside from the organizer, Dr. Jantzen (Philosophy, Virginia Tech), this year’s workshop will feature presentations from:

Nicolas Fillion, Simon Fraser University

Konstantin Genin, University of Toronto

Subhradeep Roy, Virginia Tech

Alex Tolbert, Virginia Tech

Tentative reading list

Participants will make use of readings from the following (partial and tentative) list of sources:

Eberhardt, F., 2017. Introduction to the foundations of causal discovery. Int J Data Sci Anal 3, 81–91. doi:10.1007/s41060-016-0038-6
Freno, A., 2009. Statistical Machine Learning and the Logic of Scientific Discovery. Iris: European Journal of Philosophy & Public Debate 1, 375–388.
Friedman, M., 1979. Truth and Confirmation. The Journal of Philosophy 76, 361–382. doi:10.2307/2025452
Hajek, A., Joyce, J.C., 2008. Confirmation, in: Psillos, S., Curd, M. (Eds.), The Routledge Companion to Philosophy of Science, Routledge Philosophy Companions. Routledge, London ; New York, pp. 115–128.
Hempel, C.G., 1966. Philosophy of natural science, Prentice-Hall foundations of philosophy series. Prentice-Hall, Englewood Cliffs, N.J.
Kelly, K.T., Schulte, O., Juhl, C., 1997. Learning Theory and the Philosophy of Science. Philosophy of Science 64, 245–267.
Kulkarni, S., Harman, G., 2011. An Elementary Introduction to Statistical Learning Theory. Wiley, Hoboken, US.
Laudan, L., 1980. Why  was the logic of discovery abandoned?, in: Nickles, T. (Ed.), Scientific Discovery, Logic, and Rationality, Boston Studies in the Philosophy of Science. D. Reidel Publishing Company, Dordrecht, Holland, pp. 173–183.
Popper, K.R., 2007. The logic of scientific discovery. International Society  for Science and Religion, Cambridge.
Rosenblatt, F., 1960. Perceptron Simulation Experiments. Proceedings of the IRE 48, 301–309. doi:10.1109/JRPROC.1960.287598
Schulte, O., 2012. Formal learning theory. The Stanford Encyclopedia of Philosophy (Summer 2012 Edition).
Schulte, O., 1999. Means-Ends Epistemology. The British Journal for the Philosophy of Science 50, 1–31.
Spirtes, P., Glymour, C.N., Scheines, R., 2000. Causation, prediction, and search, 2nd ed. ed, Adaptive computation and machine learning. MIT Press, Cambridge, Mass.
Vapnik, V.N., 2000. The Nature of Statistical Learning Theory. Springer New York, New York, NY.



The  2019 schedule can be found here: