Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
2° Year activated in the A.Y. 2022/2023
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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4 modules among the following
2 modules among the following
3 modules among the following
Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Algorithmic Game Theory (2021/2022)
Teaching code
4S008905
Teacher
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Secondo semestre dal Mar 7, 2022 al Jun 10, 2022.
Learning outcomes
Many problems in computer science involve settings where multiple self-interested parties interact, e.g., resource allocation in large networks, online advertising, managing electronic marketplaces and networked computer systems. Computational (algorithmic) game theory complements economic models and solution concepts, to reason about how agents should act when the actions of other agents affect their utilities, with a focus to discuss computational complexity issues, and the use of approximation bounds for models where exact solutions are unrealistic. The course aims to give students an introduction to the main concepts in the field of computational game theory with representative models and (algorithmic) solution chosen to illustrate broader themes. Students will acquire the basic skills to design models and computer systems that performs optimally/well in some paradigmatic multiagent settings; and to reason about the design of mechanisms to incentivate self-interested users to behave in a desirable way.
Program
1. Introduction to strategic games, costs, payoffs; basic solution concepts; equilibria and learning in games; Nash equilibrium; repeated games; cooperative games; markets and algorithmic issues. 2. Basic computational issues of finding equilibrium. 3. Repeatedly making decisions with uncertainty; learning, regret minimization and equilibrium. 3. Graphical games and connections to probabilistic inference in machine learning. 4. Elements of Mechanism Design; Auctions; distributed mechanism design.
Examination Methods
The exam verifies that the students have acquired sufficient confidence and skill in the application of the basic game thoretic models and their solutions, and are able to contextualize them in novel multiagent scenarios.
The exam consists of a written test with open questions and multiple choice questions. The test includes some mandatory exercises and a set of exercises among which the student can choose what to work on. The mandatory exercises are meant to verify a straightforward application of the elements studied in class. The "free-choice" exercises test the ability of students to re-elaborate these notions in "new" scenarios.