Modern Algoritmic Game Theory - NOPT021
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The objective of this course is to bridge theoretical concepts with practical algorithm implementations. Students
explore fundamental solution concepts such as Nash equilibrium and minimax strategies, alongside learning
dynamics including fictitious play, regret minimization, and replicator dynamics. The course systematically covers
both normal-form and extensive-form games. The course is part of the inter-university programme prg.ai Minor
(https://prg.ai/minor).
Last update: Maxová Jana, RNDr., Ph.D. (06.03.2025)
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Oral exam. Last update: Kynčl Jan, doc. Mgr., Ph.D. (31.05.2019)
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G. Owen: Game Theory, London 1971 nebo kterékoliv pozdější vydání (or any later edition).
E. Mendelson: Introducing Game Theory and Its Applications,CRC Press LLC,ISBN 1-58488-300-6, 2004
M. Chobot, F. Turnovec, V. Ulašin: Teória hier a rozhodovania, Alfa Bratislava, 1991.
M. Maňas: Teorie her a její ekonomické aplikace, SPN Praha 1983 nebo pozdější vydání. Last update: Maxová Jana, RNDr., Ph.D. (22.05.2025)
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Oral exam, requirements according to the sylabus of the lecture. Last update: Kynčl Jan, doc. Mgr., Ph.D. (31.05.2019)
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Week 1 Course
Practice
Week 2 Course
Practice
Week 3 - Regret Course
Practice
Week 4 - Sequential Decision Making Course
Practice
Week 5 - Sequence Form Course
Practice
Week 6 - Counterfactual Regret Minimization Course
Practice
Week 7 - Monte Carlo Methods Course
Practice
Last update: Hubička Jan, doc. Mgr., Ph.D. (14.02.2022)
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