📅 2024-12-04 — Session: Analyzed Game Theory Strategies and Probabilities
🕒 20:50–21:20
🏷️ Labels: Game Theory, Decision-Making, Probability, Strategy, Bayesian Reasoning
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and analyze decision-making strategies and probabilities in game theory, focusing on naive strategies, error resolution in code, and expected value calculations.
Key Activities
- Discussed the practical relevance of naive strategies in decision-making under uncertainty, emphasizing their effectiveness in quick decision-making and robustness in low-data scenarios.
- Resolved an argument mismatch error in the
f_uniform()function, ensuring all required arguments are passed in future calls. - Analyzed optimal strategies in a three-player game, highlighting the selection of maximum value of x (1.0) for maximizing expected payoff.
- Conducted detailed analysis of winning probabilities and expected values in game scenarios, focusing on player choices and expected payoffs.
- Explored expected value calculations in two-agent scenarios, addressing key issues and proposing fixes for clarity and accuracy.
- Analyzed win/lose probabilities and expected values, discussing strategic trade-offs based on player choices.
- Implemented correct logic for calculating win/lose probabilities using Python, including plotting corrected probability curves.
Achievements
- Clarified the role of naive strategies in decision-making and their application as benchmarks.
- Resolved coding issues in the
f_uniform()function, improving error handling. - Identified optimal strategies and calculated expected values in game theory scenarios.
Pending Tasks
- Further refine the probability calculations and explore additional strategies in multi-agent scenarios.
- Continue improving the clarity and accuracy of expected value representations.