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Instructors: Sebastian Niklas Henßler; Marcel Alexander Schliebs
Event type:
Other
Org-unit: Studentische Forschung
Displayed in timetable as:
ZU Math&Poker
Hours per week:
2
Location:
Campus der Zeppelin Universität
Language of instruction:
German
Min. | Max. participants:
5 | 35
Priority scheme: Standard-Priorisierung
Course content:
With the goal to open the world of mathematics and statistical reasoning to aspiring students, we consider Poker to be an optimal applicative use case for a variety of reasons. Poker is an inherently mathematical game, in which luck is on the long term almost entirely eliminated by the symbiotic relationship between skill and the statistical properties of the law of large numbers. It is therefore of no surprise that famous mathematicians such as the founding fathers of probabilistic statistics, Pierre-Simon Laplace and Thomas Bayes developed early applications of their theories when being invested in then popular forms of gambling/card games. Furthermore, a simplistic three-player version of Poker has been used in John Nash’s later Nobel-prize crowned dissertation to illustrate his non-cooperative solution approach (Nash-equilibrium). Besides the scientific benefits those statistical reasoning strategies yield for academic applications, we believe that the key concepts of poker such as decision-making under incomplete information, reading other’s actions and being comfortable with self-assessment also transfer well to essential skills not only needed in management and leadership positions but also beneficial for many real-life situations in general.
The goal of this class is to introduce students to advanced mathematical and statistical concepts used in poker strategy and analysis as well as the basic psychologic and applied game-theoretic aspects of decision making. In fact, one of the objectives of the course is to establish a connection to the mathematical concepts of poker without the often inherently inevitable connection to gambling. It shall, in contrast, allow students to play poker competitively in an academically supervised and theoretically supported learning environment, in which lectures on statistical concepts behind strategic considerations are accompanied by the discussion of papers from the academic poker literature. It shall furthermore provide students interested in academic or management careers with the opportunity to develop a deeper understanding of the application of quantitative knowledge in restricted-information situations. Deriving strategies from said knowledge as well as disciplined strategic decision-making are crucial skills for careers in e.g. management or politics. The main objective is therefore help students develop the aforementioned abilities through a theoretic teaching that is supported by the practical learning approach of the game of poker at given stakes but without real risk.
The class will include both theoretical lectures as well as practical applications and is open to beginners as well as more experienced players.
The syllabus will include the following elements:
1.1. Why Poker? Why Math?
1.2. Basic Rules and Gameplay
1.2.1 Texas Hold’em Rules
1.2.2 Cash vs. Tournament
1.2.3 Online (Explanation of Pokerstars) vs. Real-Life
2.1. Basic Statistical & Mathematical Tools
2.2. Application to Poker
2.2.1 Cards-Out
2.2.2 Pot-Odds and Implied Odds
3.1 Analysis Techniques
3.2 Preflop-Play (Strength of Hands)
3.3. Continuation Play (Strength of Board)
4.1. Introduction to Game Theory
4.1.1 Chicken Dilemma etc..
4.1.2 Nash-Equilibrium
4.2. Application to selected Poker Situations
5.1 Supervised Live-Playing
Including Interruptions for collaborative Commenting
6.1 Player Style Analysis
6.1.1 TAG/LAG/etc. 2x2 Matrix
6.2. Adaptive Strategy
7.1. Introduction to PokerTracker
7.2. How to use PT for Self-Improvement
7.3. Homework until 2nd weekend: Play and Track
8.1. Recapitulation of the last session and discussion of intersessional play
9.1 Tournament Strategy
9.2 Bankroll Management
10.1 Introduction to Bayesian Statistics
10.2 Probabilistic Thinking
10.3 Bayesian Reasoning in Poker Strategy
11.1 Decision Making
11.2 Bluffing and Bluff Detection
12.1 Careers in Poker
12.2 Summary and Discussion
12.3 The future of Poker (Artificial Intelligence, Machine Learning, etc.)
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