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Thinking Master’s courses

Courses:

Thinking and Scientific Rationality (in Russian)

More information you can find here Мышление и научная рациональность 


Teachers:


Goncharko Oksana

Latypova Alina

Lenkevich Alexander

Mavrinsky Ilya

Chirva Darya

Yakovleva Lyubov

Key Concepts in Contemporary Political Thinking (in Russian)

More information you can find here  Основные проблемы современного политического мышления


Teachers:


Koretko Sergey

Magun Artemiy

Glukhovsky Andrey

Serebryakov Artem

Syutkin Anton

Economic Foundations of the Modern Age (in Russian)

More information you can find here Экономические основания современности


Teachers:


Kartseva Anastasia

Latypova Alina

Lenkevich Alexander

Pogrebnyak Alexander

Scientific Reasoning and its Relevance for Society

What to expect from the course

You will be trained to be able to decide on these kinds of problems:

Which source of information should we trust in the era of social media? Which claims, values and political goals are backed-up by science? Whom should we believe about these kinds of things? Who are the relevant experts? Why are we to trust the experts? What are the types of scientific reasoning and explanation? How and why do they work?

Main topics. 

1.   The Problem of Demarcation and Values in Science and Science Communication

Description: At the beginning of the 20th century, particularly scholars of social science discussed, propagated, and pursued the so-called “value-free ideal” of science. In the course of unprecedented technological and historical developments, the problem swapped over to the realm of the empirical sciences. In recent years, with the increasingly public-oriented role of science, the discussion has received some new twists towards the communicative role of individual scientists as well as scientific institutions. In this lecture, we outline and investigate the theoretical foundation of the value-free ideal. First, we discuss different approaches to demarcate scientific from non-scientific endeavours; afterwards, we zoom in a bit and discuss the different scientific contexts and their role with respect to the value-neutrality postulate; finally, we outline implications concerning the communication of scientific results.

2.   Evidence-Based Policy

Description: In this lecture we tackle the questions of what counts as good evidence for making predictions about the efficacy of social policy and of which basic forms of inference efficacy predictions in social policy should be based on. We will discuss the classical approach based on randomized controlled trials and contrast it with armchair policymaking and Cartwright and Hardie’s (2012) recent approach based on causal roles and support factors. We will see that causal information is crucial for evidence-based policy and that also less classical inference forms such as analogical inference play a central role for robust efficacy predictions.

3.   Explainable AI and the Right for an Explanation

Description: Methods of artificial intelligence (AI) are heavily employed not only for commercial purposes, but also increasingly used in the field of policy making and other social areas such as that of science, law, education etc. Although the implementation of such methods in the social realm is already very successful and has even much greater prospects, opacity that comes with such methods triggered a demand for and an explicit voicing of a “right to explanation”. A very common possibility to address the problem of how to account for this demand is that of developing new forms of transparent or white-box AI. This approach of so-called “explainable AI” is, of course, a gradual matter. In this lecture, we distinguish several features of explainable AI such as that of transparency, interpretability, and explainability in the narrow sense, which come at different strengths. Based on a thorough philosophy of science discussion of different notions of explanation, we outline a spectrum of (explainable) AI that has classical black-box approaches on the one end, and new forms of AI that are able to straight away produce explanations with their decisions at the other end.

 4.   Rationality and Bias in Belief Dynamics

Description: In this lecture we discuss the different possibilities of how individuals and groups of individuals up to whole societies do and should change their beliefs in the face of new evidence coming in. We highlight classical biases and challenges such as the base rate fallacy, Simpson’s paradox, and double counting. We also discuss possible biases and rationality standards underlying social phenomena such as peer disagreement, judgement aggregation, trust in expert testimony, and belief polarization.

If you want to learn more, please, watch the video:

Language of instruction: English.

You will have 4 lectures and 8 seminars (every lecture lasts for three hours with one 10 minutes break).

Seminar formats: discussions and debates, individual and group in-class tasks. We will provide you with all the materials for homereading.

Exam format: essay (up to 1000 words).


Teachers:


Gebharter Alexander

PhD in Philosophy , principal investigator at the Munich Center for Mathematical Philosophy (MCMP) at the Ludwig Maximilian University of Munich (LMU), Germany. Main Research Area - Philosophy of Science: Scientific Reasoning, Causation and Modelling, Relation of the Mental to the Physical, Human Agency and Free Will, Evidence-based Policy.

J. Feldbacher-Escamilla Christian

PhD in Philosophy, lecturer at the University of Cologne, Germany. Main Research Area - Epistemology and Philosophy of Science: Social Epistemology, Machine Learning, Scientific Inference.

Chirva Daria

 Lecturer at ITMO University, the head of the Thinking Module at ITMO University. Main Research Area – Ontology and Epistemology, Philosophy of Science, Ethics of Science and Technology.

Andreoletti Giacomo

PhD in Philosophy, assistant professor at the School of Advanced Studies (SAS) University of Tyumen. Main Research Area - Analytic Philosophy, Formal Logic, Modern and Contemporary Philosophy, Metaphysics, Philosophy of Time.

Goncharko Oksana

PhD in Philosophy, Associate Professor at ITMO University. Main Research Area - Non-Classical Logic, Philosophy of Science, the History of Logic in Byzantium, Ancient Greek and Modern Greek Philology.

Visual Forms of Thinking and Cinema (in Russian)

More information you can find here Визуальные формы мышления и кинематограф


Teachers:


Verkhoglyadov Ilya

Gribova Maria

Davydova Olga

Kapelchuk Ksenia

Petrin Ivan

Radeev Artem

Daria Chirva

The head of the Thinking Module