Einschreibeoptionen

**This course, conducted in English, is complementary to "Agent-based Simulations in Philosophy" course (winter semester).

  In recent years, many philosophical developments have made use of heavy computer simulations and gigantic data sets. However, it is a big challenge for philosophy students to engage in such studies, especially for those who lack the required foundations, such as computer programming or probability theory. This course aims to equip students in philosophy with these foundational tools in programming and math, thus empowering them to engage in contemporary philosophical literature.

  Thanks to the advances in modern technology and measurement techniques, scientists can carry out theoretical analyses that involve intense computations. Yet, these tools use large data sets and computer calculations and therefore come with the burden of mathematics and computer programming skills. Philosophers, too, have started to adopt methods relying on computers. For instance, epistemologists have started using computer simulation tools to examine knowledge in a social context where multiple agents interact with each other. The main points made in these works are accessible for a broader philosophical audience. But still, they require basic understanding of math and coding for a good comprehension, and furthermore replicating their arguments. This course aims to provide some of those basic requirements.

  Participants are not expected to have taken prior math courses. We plan to proceed step-by-step by starting with some seminal papers in the discipline of network epistemology. From then on, we go through matrix algebra, calculus, statistics, and graph theory. An introduction to Julia programming and practices will be included as we conclude each section.

Evaluation (both graded and non-graded credits) is done by an exam focusing on key concepts: eigenvalues, differentiation, probability distribution, and centrality measures. Participants can earn extra exam points by submitting their Julia coding practices on these key concepts by generating, controlling, and visualizing data structures.

 

Reference

(Introduction)

Page, S. E. (2018). The Model Thinker: What You Need to Know to Make Data Work for You. Basic Books, Ch. 2.

Grim, Patrick and Daniel Singer, "Computational Philosophy", The Stanford Encyclopedia of Philosophy (Fall 2022 Edition), Edward N. Zalta & Uri Nodelman (eds.), URL = <https://plato.stanford.edu/archives/fall2022/entries/computational-philosophy/>.

O’Connor, C., & Weatherall, J. O. (2019). The Misinformation Age: How False Beliefs Spread. Yale University Press, ch.2, pp 46-92.

 

(Programming Julia)

Lauwens, B., & Downey, A. (2019). Think Julia: How to Think Like a Computer Scientist. O’Reilly Media. https://benlauwens.github.io/ThinkJulia.jl/latest/book.html

Kalicharan, N. (2021). Julia - Bit by Bit: Programming for Beginners. Springer International Publishing.

Sherrington, M. (2015). Mastering Julia. Packt Publishing.

 

(Matrix Algebra, Calculus, Statistics)

Knut S., Peter H., Arne S., Andrés C. (2022). Essential Mathematics for Economic Analysis (6th ed.). Pearson

Chiang, A. C., & Wainwright, K. (2005). Fundamental Methods of Mathematical Economics (4th ed.). McGraw-Hill Education.

Burden, R. L., & Faires, J. D. (2011). Numerical Analysis (9th ed.). Cengage Learning.

Strang, G. (2009). Introduction to Linear Algebra (4th ed.). Wellesley-Cambridge Press.

Calculus for Beginners (mit.edu) [https://math.mit.edu/~djk/calculus_beginners/]

Dennis D. Wackerly, William Mendenhall, Richard L. Scheaffer - Mathematical Statistics with Applications-Cengage Learning (2008)

 

(Graph Theory)

Barabási, A.-L. (2016). Network Science. Cambridge University Press. http://networksciencebook.com/

Menczer, F., Fortunato, S., & Davis, C. A. (2020). A First Course in Network Science. Cambridge University Press. https://doi.org/10.1017/9781108653947

Jackson, M. O. (2010). Social and Economic Networks. Princeton University Press. https://doi.org/10.2307/j.ctvcm4gh1

Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets. Cambridge University Press. https://doi.org/10.1017/CBO9780511761942

 

(Computational Epistemology)

 Rubin, H. (2022). Structural causes of citation gaps. Philosophical Studies, 179(7), 2323–2345. https://doi.org/10.1007/s11098-021-01765-3

Weatherall, J. O., O’Connor, C., & Bruner, J. P. (2020). How to Beat Science and Influence People: Policymakers and Propaganda in Epistemic Networks. The British Journal for the Philosophy of Science, 71(4), 1157–1186. https://doi.org/10.1093/bjps/axy062

Weatherall, J. O., & O’Connor, C. (2021). Conformity in scientific networks. Synthese, 198(8), 7257–7278. https://doi.org/10.1007/s11229-019-02520-2

Zollman, K. J. S. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587. https://doi.org/10.1086/525605


Semester: SoSe 2024
Selbsteinschreibung (Teilnehmer/in)
Selbsteinschreibung (Teilnehmer/in)