STV9020G - Rational Choice in Empirical Political Science

Course content

In all parts of political science, scholars often use a game-theoretic model or some other rational choice framework to analyze their research questions and to develop empirically testable hypotheses about politics and policy making. Examples of political science topics being analyzed with this analytical tool include aspects of international cooperation, international and domestic conflict, democracy, institutional design (at all levels), voting, bureaucratic politics and more. This course aims to enable students to use a game-theoretic model or other rational choice framework in their own research.

The course consists of five main parts. The first part introduces the students to the relationship between institutions, rational choice theory, and political science in general. The second part deals with the construction, solving and evaluation of formal models. The third part explains how models can be confronted with empirical evidence in the form of case studies. The fourth part studies how formal models can be confronted with experimental evidence. Finally, the fifth part similarly explains how models can be confronted with empirical evidence in the form of large-n statistical analysis.

Learning outcome

Knowledge

Students will acquire knowledge about:

  • The use of rational choice theory in political science

  • Rational choice institutionalism as a methodology

  • How to construct and evaluate models

  • Various methods for solving models, including backward induction, forward induction, elimination of weakly dominated strategies, and equilibrium selection

  • How to use case studies to empirically assess various types of game-theoretic models, including static games, sequential games, and repeated games

  • How to conduct statistical evaluation of game-theoretic models

Skills

Students will be able to:

  • Critically read and evaluate rational choice based literature in political science

  • Construct and analyze their own models

  • Apply case studies to confront models with evidence

  • Apply various statistical tools and techniques to confront models with evidence.

Competences

Students will:

  • Enhance their competence in analyzing complex questions thoroughly, critically, and independently

  • Enhance their competence concerning the relationship between theoretical models and empirical evidence

Admission

This course is a combinded Master's and PhD course.

PhD candidates from UiO: Apply for the course in StudentWeb
Other PhD candidates: Application form

Teaching

7 lectures and 3 seminars.

Mandatory activities

  • Attend and participate actively in 2 out of 3 seminars
  • Submit a 3-5 page outline
  • Give a short oral presentation of the outline
  • Comment on at least one other student's outline

Examination

Written assignment (6 - 8000 ord)

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Explanations and appeals

Resit an examination

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Facts about this course

Credits

10

Level

PhD

Examination

Every autumn

Teaching language

English