Games and Decisions in Data Analysis and Modelling
- familiarize students with modern models of game theory and decision theory, their applications in modeling and analyzing socio-economic problems, as well as their use in analytical and decision support systems
- Knows the models of individual preferences
- Knows the main voting procedures and voting properties
- Knows the main centrality measures in networks
- Knows how to find Nash equilibtia in normal form games
- Knows the main solution concepts in cooperative games
- Preference modellingClassical utility theory, ordinal and cardinal models. Threshold utility. Binary relations of preferences. Rational choice, revelation of preferences. The problem of multi-criteria choice and ranking.
- Social choice theoryVoting as a way of making decisions in group. Rationality in forming a collective decisions. Various voting procedures and their properties. Properties of voting procedures. The paradoxes of voting.
- NetworksNetwork and graphs. Models of network formation. Networks as a way to model constraints on information exchange and interaction. Dissemination of information and influence in networks. Decision-making and strategic behavior of players in network interaction. Centrality indices. Applicaitions in different models.
- Basics of game theoryGames of models of players interactions. Different solutions concepts. Nash equilibria in pure and mixed strategies.
- Mechanism designThe aim of mechanism design is to develop the rules of games that lead to a desired result. Examples. The principle of detection. Auctions.
- Cooperative game theoryClassic cooperative games: problem statement and basic concepts. Core and Shapley value. Limitation of cooperation: examples and methods of modeling. Games with restricted cooperation given in the form of a priori unions.
- Dynamics in gamesGames with consecutive moves. Extensive form of the games. Iterated games with observable actions. Markov strategies and Markov perfect equilibrium in iterative games.
- mid-term examThe mid-term is a written test in StartExam platform with asynchronous proctoring by Examus. The rules of the mid-term are available at https://elearning.hse.ru/en/student_steps/ The mi-term consists of several questions. In some of them students should provide a short answer, in others they have to do a matching or answer the multiple choice questions. Students are not allowed to use a mobile phone or any other devices and communicate with classmates and any other people during the mid-term.
- Home assignmentsHome assignments should be done by students individually
- Final examinationThe examination shall be held in writing (test) with the use of asynchronous proctoring on the StartExam platform. StartExam is an online platform for conducting test tasks of various levels of complexity. The link to pass the exam task will be available to the student in the RUZ. Asynchronous proctoring means that all the student's actions during the exam will be “watched” by the computer. The exam process is recorded and analyzed by artificial intelligence and a human (proctor). Please be careful and follow the instructions (https://elearning.hse.ru/en/student_steps/) clearly!
- Interim assessment (4 module)0.5 * Final examination + 0.2 * Home assignments + 0.3 * mid-term exam
- Fuad Aleskerov, Denis Bouyssou, & Bernard Monjardet. (2007). Utility Maximization, Choice and Preference. Post-Print. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.p.hal.journl.halshs.00197186
- Maschler,Michael, Solan,Eilon, & Zamir,Shmuel. (2013). Game Theory. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781107005488
- Aleskerov, F., Meshcheryakova, N., & Shvydun, S. (2016). Centrality measures in networks based on nodes attributes, long-range interactions and group influence.