📅 2024-12-05 — Session: Enhanced Data Visualization and Game Theory Exploration
🕒 14:35–16:15
🏷️ Labels: Python, Data Visualization, Game Theory, Bayesian Reasoning, Investing
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to enhance data visualization techniques and explore foundational and advanced concepts in game theory, particularly focusing on Bayesian reasoning and its applications in finance and investing.
Key Activities:
- Updated a contour plot with a double-line effect using Python’s Matplotlib, improving the visualization of posterior probability.
- Enhanced the aesthetics of Seaborn plots by refining color palettes, grid lines, and typography for better clarity.
- Explored basic game theory concepts through a simplified two-player scenario, focusing on optimizing decisions for maximum expected payoffs.
- Analyzed a two-player game where the highest number chosen results in a loss, detailing optimization strategies.
- Modeled expectations in game theory, discussing pre-game and post-loss scenarios for Player A.
- Developed functions for probabilistic modeling in game theory, integrating code implementations to calculate expected payoffs.
- Enhanced visualization of opponent B’s choice distribution in game theory using vertical dashed lines.
- Curated resources for Bayesian inference and game theory applications in investing.
- Recommended readings on Bayesian reasoning and game theory for investing strategies.
- Explained Bayesian Nash Equilibrium in game theory, focusing on decision-making under uncertainty.
- Formalized a 3-player Bayesian game, detailing the structure and equilibrium concepts.
- Clarified the distinction between conditional and unconditional loss distributions in risk management.
Achievements:
- Improved data visualization techniques in Python for better clarity and professional presentation.
- Gained insights into game theory applications in finance, particularly in investing and risk management.
- Developed a deeper understanding of Bayesian Nash Equilibrium and its strategic implications.
Pending Tasks:
- Further exploration of Bayesian games and their applications in multi-player scenarios.
- Continued refinement of data visualization techniques for more complex datasets.