📅 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
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.