📅 2024-12-04 — Session: Game Theory and Probability Analysis
🕒 20:50–21:20
🏷️ Labels: Game Theory, Probability, Decision-Making, Python, Strategy
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to analyze decision-making strategies and probability calculations in game theory and programming contexts.
Key Activities
- Explored the practical relevance of naive strategies in decision-making under uncertainty.
- Identified and resolved an argument mismatch error in the
f_uniform()function. - Analyzed optimal strategies in a three-player game scenario to maximize expected payoff.
- Conducted a detailed analysis of winning probabilities and expected values in game scenarios.
- Developed Python functions to calculate and plot probabilities of winning and losing based on game conditions.
Achievements
- Gained insights into the effectiveness of naive strategies as benchmarks and their robustness in low-data scenarios.
- Successfully fixed a programming error in the
f_uniform()function. - Enhanced understanding of game theory strategies and probability calculations.
Pending Tasks
- Further refine the Python functions for broader applicability in different game scenarios.
- Continue exploring Bayesian reasoning applications in decision-making strategies.