📅 2024-09-06 — Session: Analyzed Game Theory for Competitive Pricing
🕒 22:50–23:20
🏷️ Labels: Game Theory, Competitive Pricing, Profit Functions, Python, Data Visualization
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The objective of this session was to set up and analyze game theory models for competitive pricing between two companies, focusing on profit functions and pricing strategies.
Key Activities
- Game Theory Setup: Established the foundational game setup for two competing companies, detailing price ranges and associated probabilities.
- Profit Function Analysis: Outlined profit functions for different pricing scenarios, including winning, losing, and tying, and explored mixed strategy approaches.
- Expected Profit Calculation: Developed methodologies for calculating expected profits using mixed strategies, considering pricing distributions and operational costs.
- Numerical Integration for Profit: Calculated expected profits for low-cost and high-cost companies through numerical integration over price ranges.
- Probability Function Correction: Corrected Python implementations of probability functions for uniform distribution, ensuring accurate behavior.
- [[Data Visualization]]: Implemented Python code to plot probability functions and expected profits, providing visual insights into pricing strategies.
Achievements
- Successfully set up a comprehensive game theory model for competitive pricing.
- Developed and corrected Python code for probability functions and data visualization.
- Analyzed expected profits using both theoretical and numerical methods.
Pending Tasks
- Further refinement of the game theory model to include additional market factors.
- Exploration of alternative pricing strategies beyond the current mixed strategy approach.