Data Science and Artificial Intelligence

Decision theory and game theory

Integrated course, 2.50 ECTS

 

Course content

Part 1: Preferences and Mechanism Design Theory
- Binary relations and preference orders
- Theory of disclosed preferences and conjoint analyzes
- Preference aggregation method and Arrow's impossibility theorem
- Gibbard-Satterthwaite theorem
Part 2: Decision Theory
- Decision-theoretical basic concepts
- Risk awareness and risk tendency
- Solution concepts for risk decisions
- Solution concepts for decisions in the event of uncertainty
Part 3: Non-cooperative game theory
- Basic concepts of non-cooperative game theory
- Static games with complete information
- Dynamic games with complete information
- Static games with incomplete information
- Dynamic games with incomplete information
- Auctions and auction theory
Part 4: Cooperative game theory
- Basic concepts of cooperative game theory
- Important solution concepts for cooperative games

Learning outcomes

Students have in-depth knowledge in the areas of preferences and mechanism design theory, decision theory as well as non-cooperative and cooperative game theory. They are able to differentiate the various solution concepts presented and to apply them in practice. They also understand the basics of auctions or auction theory.

Recommended or required reading and other learning resources / tools

Recommended literature or books:
- Binmore, K. G. (1991). Fun and Games: A Text on Game Theory. Great Source Education Gr, 1st edition.
- Engelbrecht, A. P. (2007).Computational Intelligence: An Introduction. Wiley, 2nd edition.
- Fudenberg, D., Tirole, J. (1991). Game Theory (Mit Press). The MIT Press, 1st edition.
- Gaertner, W. (2009). A Primer in Social Choice Theory (LSE Perspectives in Economic Analysis). Oxford University Press, 1st edition (revised edition).
- Gibbons, R. (1992). Primer In Game Theory. Financial Times / Prentice Hall, 1st edition.
- Gilbert N. (2008). Agent-based Models. Series: Quantitative Applications in the Social Sciences 153. Sage Publications, 2nd edition.
- Kelly, J. (2012). Social Choice Theory. Springer, 1st edition (softcover reprint).
- Kreps, D. M. (1990). Course Microeconomic Theory. Financial Times Prent.Int, 1st edition.
- Kruse, R., Borgelt, C. et al (2015). Computational Intelligence: Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze. Springer Vieweg, 2. Auflage.
- Luce, R. D., Raiffa, H. (1990). Games and Decisions: Introduction and Critical Survey (Dover Books on Mathematics). Dover Publications Inc., 1st edition (New edition).
- Mas-Colell, A. (1995). Microeconomic Theory. Oxford University Press, 1st edition.
- Railsback S. F., Grimm V. (2012). Agent-Based and Individual-Based Modeling. Princeton University Press, 2nd edition.
- Salanie, B. (2017). The Economics of Contracts: A Primer, 2nd Edition (Mit Press). The MIT Press, 2nd edition.
- Sayama, H. (2015). Introduction to the Modeling and Analysis of Complex Systems. Open SUNY Textbooks, 1st edition (print edition).
- Watson, J. (2013). Strategy: An Introduction to Game Theory. W W NORTON & CO, 1st edition (revised).
- Weicker, K. (2015). Evolutionäre Algorithmen. Springer Vieweg, 3rd edition.
- Wilensky U., Rand W. (2015). An Introduction to Agent-Mased Modeling. Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press, 1st edition.
Recommended journals or selected articles:
- Diamond, J. M. (2002): Life with the artificial Anasazi. Nature, Vol. 419, 10 October 2002, S. 567-569.
- Grimm V. et al. (2006): A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198, Elsevier, S. 115-126.
- Grimm V. et al. (2010): The ODD protocol: A review and first update. Evological Modelling 221, Elsevier, S. 2760-2768.
- Janssen, M. A. (2009): Understanding Articial Anasazi. In: Journal of Artificial Societies and Social Simulation, 12/4/13, 2009.
- Railsback, S., Ayllon, D. et al. (2017): Improving Execution Speed of Models Implemented in NetLogo. In: Journal of Artificial Societies and Social Simulation20(1) 3, 2017
- Stonedahl, F. and Wilensky, U. (2010): Evolutionary Robustness Checking in the Artificial Anasazi Model. In: Association for the Advancement of Artificial Intelligence, 2010.

Other relevant journals and articles will be announced in the courses.

Typical software for this module:
Python/Spyder/PyCharm, Matlab/Octave/Scilab, Gambit, NetLogo/BehaviorSearch, R/RStudio etc.

Mode of delivery

1,25 ECTS Lecture, 1,25 ECTS Exercise

Prerequisites and co-requisites

Module 9, 10 and 12

Assessment methods and criteria

Lecture: final exam; Exercise: examination character