Programme

Instructors.
Elke Brendel (University of Bonn) & Filippo Ferrari (University of Bonn)

Short Description.
In the first part of this course we will give a rough overview of various classical and non-classical logics, such as classical first-order logic, systems of modal logic, free logic, paracomplete and paraconsistent logics. We will then discuss the prospects and limits of logic as a tool of philosophical inquiry. In particular, we will examine how logic can help to formalize and analyse key philosophical questions, as, for example, certain questions concerning necessity, existence, causality and truth, and how logic can help us to detect reasoning fallacies and paradoxes.

The second part focuses on recent debates in the philosophy of logic. We first review the debate between logical monists—who believe that there is only one correct logic—and logical pluralists—who believe that there is a plurality of correct logics. We then discuss the so-called anti-exceptionalist view about logic according to which logic doesn’t enjoy an exceptional methodological and epistemological status among the sciences. In so doing, we explore different ways of being anti-exceptionalists with the aim of critically discuss some views that have been advanced in the literature. Particular emphasis will be given to the question whether and to what extent logic is a normative discipline.

Main Sources (non-obligatory pre-reading).

• Hjortland, Ole T. (2017): “Anti-Exceptionalism about Logic”. Philosophical Studies 174, pp.631-658, DOI: 10.1007/s11098-016-0701-8.
• Priest, Graham (2008): An Introduction to Non-Classical Logic. New York: Cambridge University Press, DOI: 10.1017/CBO9780511801174.
• Russell, Gillian (2019): “Logical Pluralism”. In: Edward N. Zalta (ed.) The Stanford Encyclopedia of Philosophy (Summer 2019 Edition), URL:<https://plato.stanford.edu/archives/sum2019/entries/logical-pluralism/>.

Further Sources (for independently deepening one’s understanding after the summer school).

• Beall, JC & Restall, Greg (2006): Logical Pluralism. Oxford: Oxford University Press.
• Cohnitz, Daniel & Estrada-González, Luis (2019): An Introduction to the Philosophy of Logic. Cambridge: Cambridge University Press, DOI: 10.1017/9781316275573.
• da Costa, Newton & Arenhart, Jonas R. (2018): “Full-Blooded Anti-Exceptionalism about Logic”. Australasian Journal of Logic 15, pp.362-380, DOI: 10.26686/ajl.v15i2.4865.
• Sainsbury, R. Mark (2002): “What Logic Should We Think With?” Royal Institute of Philosophy Supplements 51, pp.1-17, DOI: 10.1017/S1358246100008055.
• Williamson, Timothy (2018): Doing Philosophy: From Common Curiosity to Logical Reasoning. Oxford: Oxford University Press.

Software.
N/A

Instructor.
Leander Vignero (KU Leuven)

Short Description.
Probability plays an important role in many aspects of our lives and has generated a considerable body of literature in philosophy over the years. Recently however, the literature has started to take a computational turn, with simulations and computational models taking on ever greater importance. In this course, we will connect the philosophical literature to empirical and computational work. The first session will cover some of the philosophical work relating to probability theory from a broadly Bayesian perspective. The second session will familiarize the participants with some relevant empirical work and the upcoming computational approach in philosophy. We will also consider some of the basic techniques behind these models. Finally, in the third session we will do an extended case study: the Rational Speech Act (RSA) framework. Put briefly, RSA implements Gricean ideas from linguistics and philosophy of language probabilistically. No programming skills are required for this course. But supplementary material will be provided for those who have any such skills.

Main Sources (non-obligatory pre-reading).

• Easwaran, Kenny (2011). “Bayesianism I: Introduction and arguments in favor”. Philosophy Compass 6(5), pp.312-320, DOI: 10.1111/j.1747-9991.2011.00399.x.
• Easwaran, Kenny (2011). “Bayesianism II: Applications and criticisms”. Philosophy Compass 6(5), pp.321-332, DOI: 10.1111/j.1747-9991.2011.00398.x.

Further Sources (for independently deepening one’s understanding after the summer school).

• Bayesianism.
  • In General.
    • See the references in Easwaran (2011a, 2011b) as provided above.
  • Interpretation of Probability.
    • Hájek, Alan (2011): “Interpretations of Probability”. In: Edward N. Zalta (ed.) The Stanford Encyclopedia of Philosophy (Winter 2012 Edition), URL:<https://plato.stanford.edu/archives/win2012/entries/probability-interpret/>.
      (This contribution references classics like the work by Ramsey and de Finetti. It also provides an overview of alternative positions, propensity interpretations for instance, which are not covered in the session.)
  • Bayesianism in the Sciences.
    • Barber, David (2012): Bayesian Reasoning and Machine Learning. Cambridge: Cambridge University Press.
      (To acquire an advanced mathematical background; it is not a real statistics course, but it will give you a strong technical understanding.)
  • Bayesian Epistemology.
    • Talbott, William (2016): “Bayesian Epistemology”. In: Edward N. Zalta (ed.) The Stanford Encyclopedia of Philosophy (Winter 2016 Edition), URL:<https://plato.stanford.edu/archives/win2016/entries/epistemology-bayesian/>.
      (A good point of departure.)
    • Strevens, Michael (2017): “Notes on Bayesian Confirmation Theory”. Lecture Notes/Unpublished Manuscript, URL:<http://www.nyu.edu/classes/strevens/BCT/BCT.pdf>.
• Empirical Work.
  • Tetlock, Philip E. & Gardner, Dan (2016): Superforecasting: The art and science of prediction. Random House.
    (Philip Tetlock is one of the leading figures in this field; his work spans many years and many papers, but a good way to approach the literature is this popularizing book. It will familiarize you just enough to explore the vast technical literature on your own.)
• RSA.
  • Scontras, Gregory & Tessler, Michael H. & Franke, Michael (2018): Probabilistic Language Understanding: An introduction to the Rational Speech Act framework. Online Source, (retrieved 2019-07-01), URL: <https://www.problang.org/>.
    (A very good introduction to RSA; it is also a good introduction to the WebPPL language;)
  • Frank, Michael C. & Goodman, Noah D. (2012): “Predicting Pragmatic Reasoning in Language Games”. Science 336(6084), pp.998-998, DOI: 10.1126/science.1218633.
    (A classic;)
  • Goodman, Noah D. & Stuhlmüller, Andreas (2013): “Knowledge and Implicature: Modeling language understanding as social cognition”. Topics in Cognitive Science 5(1), pp.173-184, DOI: 10.1111/tops.12007.
    (Another classic;)

Software (for use after the summer school, will be not used in the course).

• Julia: https://julialang.org/learning/
  (There are many languages you can use to run simulations in philosophy. Python is one obvious candidate. However, Julia is built with probabilities in mind and is also used in philosophy. A great way to learn many things in one go is:
  • Klok, Hayden & Nazarathy, Yoni (2019, draft): Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Online Source, (retrieved 2019-07-01), URL: <https://people.smp.uq.edu.au/YoniNazarathy/julia-stats/StatisticsWithJulia.pdf>.

  This source touches on many of the key ideas which will be outlining in the course. Nevertheless, there are also many other good tutorials out there. A good place to start looking is the mentioned website.)

Instructor.
Simon Huttegger (University of California, Irvine)

Short Description.
This class introduces students to the main concepts and methods in game theory. We will cover games in normal and extensive form, Nash equilibrium and backward induction, and the basics of evolutionary game theory. Special attention is being paid to examples of philosophical interest.

Main Sources (non-obligatory pre-reading).

• Dixit, Avinash & Skeath, Susan & Reily, David (2015): Games of Strategy. London: W.W. Norton & Company.
• Hofbauer, Josef & Sigmund, Karl (1998): Evolutionary Games and Population Dynamics. Cambridge: Cambridge University Press, DOI: 10.1017/CBO9781139173179
• Osborne, Martin & Rubinstein, Ariel (1994): A Course in Game Theory. Cambridge, MA: MIT Press.
• Skyrms, Brian (1994): Evolution of the Social Contract. Cambridge: Cambridge University Press, DOI: 10.1017/CBO9780511806308
• Weibull, Jörgen (1995): Evolutionary Game Theory. Cambridge, MA: MIT Press.

Further Sources (for independently deepening one’s understanding after the summer school).
See above.

Software.
N/A

Instructor.
Eckhart Arnold (Bavarian Academy of Sciences and Humanities)

Short Description.
This course of the summer school is going to be an Introduction to Computer-Programming with Python. The course is meant to be for (absolute) beginners and does not assume any prior knowledge of either Python or computer programming. Python is an easy, yet powerful computer language that is particularly well suited for scientific programming.

During the course, we’ll develop a game-theoretical computer simulation of the Prisoner’s Dilemma as a (hopefully motivating) pet project. After finishing the course, you’ll be able to write small Python scripts and more importantly, you’ll have enough knowledge and understanding of Python to learn more by teaching yourself from web-tutorials and other sources.

If time permits, we will also have a bit of philosophical discussion on the epistemology of computer simulations. (Little spoiler: It’s the easy part to get the computer programming right. It’s the hard part to assess the theoretical scope and the empirical validity of a computer simulation. But just why this is the case, we will discuss in the summer school.)

Main Sources (non-obligatory pre-reading).

• Suitable for those who know nothing about computer programming, yet (in German): http://eckhartarnold.de/teaching/python_and_nengo_intro.html
• For people who already know some other programming language, like netlogo: https://docs.python.org/3/tutorial/index.html

Further Sources (for independently deepening one’s understanding after the summer school).

• Jupyter Notebook Start Guide: https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html
• Matplotlib Beginner’s Guide: https://matplotlib.org/users/beginner.html
• Introductions to several of the most useful tool’s for scientific computing (warning: you’ll probably need to know Python quite well already; if not, go through the Python tutorial first): https://docs.scipy.org/doc/scipy/reference/tutorial/index.html
• A duly critical discussion of the limits of the Prisoner’s Dilemma as an explanatory model: Northcott, Robert & Alexandrova, Anna (2015): “Prisoner’s Dilemma doesn’t explain much”. In: Peterson, Martin (ed.): The Prisoner’s Dilemma. Cambridge: Cambridge University Press, pp.64-84, DOI: 10.1017/CBO9781107360174.005
• Just to give you an idea of why programming the model itself is not the hardest part when using computer simulations in the context of scientific explanations for actual empirical phenomena:
  Lee, Ju-Sung & Filatova, Tatiana & Ligmann-Zielinska, Arika & Hassani-Mahmooei, Behrooz & Stonedahl, Forrest & Lorscheid, Iris & Voinov, Alexey & Polhill, J. Gary & Sun, Zhanli & Parker, Dawn C. (2015): “The Complexities of Agent-Based Modeling Output Analysis” Journal of Artificial Societies and Social Simulation 18(4), DOI: 10.18564/jasss.2897

Software.
Please download and install this on your Laptop before the summer school:

• Python (≥3.0): https://www.python.org/
  (just this, if you use linux, install Python3 from your app-store or package manager)

Additional Software:

• Do yourself a favour and use a good development environment. This is the best one and the “community edition” is free: https://www.jetbrains.com/pycharm/download/
• Rather than pointing out the many great packages for scientific computing with Python, I suggest that you use this comprehensive distribution that includes them all already. Saves you a lot of
  time of figuring out and fiddling with the right packages: https://www.anaconda.com/distribution/
• For those who already know so much that they do not need this course, but who are just curious about how to implement a Prisoner’s Dilemma simulation. (Warning: This code is not maintained any more and requires and OLD version of Python and wxPython to run!): https://github.com/jecki/CoopSim/
• Jupyter: https://jupyter.org/

Instructor.
Corina Strößner (University of Duesseldorf)

Short Description.
In the current years, psychological methods became important in philosophy. For example, experimental philosophy (X-Phi) gathers empirical results on philosophical intuitions in a psychological way. Moreover, many philosophers are engaged in cognitive science where they have to understand and potentially apply psychological methodology. However, while philosophy students are usually equipped with solid background knowledge in formal logic, they rarely visit courses on statistics. With this event, I cannot and do not aspire to substitute for a propaedeutic (or advanced) statistics lecture. Instead, I will try to show you how to work out the prerequisites for your experimental work yourself. The first part will be quite general and applicable to different software (Excel, SPSS, and R). In the second part, we will focus on the free (and powerful) statistical software R. I will demonstrate how to learn and apply R for mixed effect modelling.
The course covers the following topics:

1. Understanding the basics of your software (and statistics)
2. Statistics first, ask questions later?
3. Exploiting the literature
4. R and its Packages
5. Mixed effect modelling with R and lme4

Introductory Sources.
In order to follow the course, you should get acquainted with the introductory sources. Do not try to read everything but please take three or four hours to get a feeling for how to use the sources. For example, have a look at the table of contents of the book Field et al. (2012), skim some chapters, and explore the online material.

• Introduction.
  Have a look at https://www.discoveringstatistics.com/ and, if possible, at the introduction:
  Field, Andy & Miles, Jeremy & Field, Zoë (2012): Discovering Statistics using R. London: Sage Publications.
• Mixed effect model tutorial by Bodo Winter.
  http://www.bodowinter.com/tutorials.html. The following files are especially relevant:
  http://www.bodowinter.com/tutorial/bw_LME_tutorial1.pdf
  http://www.bodowinter.com/tutorial/bw_LME_tutorial2.pdf

Further Sources (for independently deepening one’s understanding after the summer school).

• Baayen, R. Harald (2008): Analyzing Linguistic Data: A practical introduction to statistics using R. Cambridge: Cambridge University Press, DOI: 10.1017/CBO9780511801686.
• Barr, Dale J. & Levy, Roger & Scheepers, Christoph & Tily, Harry J. (2013). “Random Effects Structure for Confirmatory Hypothesis Testing: Keep it maximal”. Journal of Memory and Language 68(3), pp.255-278, DOI: 10.1016/j.jml.2012.11.001.
• Bates, Douglas & Maechler, Martin & Bolker, Ben & Walker, Steve (2015): “Fitting Linear Mixed-Effects Models Using lme4”. Journal of Statistical Software 67(1), pp.1-48 DOI: 10.18637/jss.v067.i01.

Software.

• R: http://www.r-project.org/
• Additional Packages: lme4, lmertest, Rcmdr