Optimizing Auction Strategies Using Game Theory and Machine Learning

Overview:
Designed and implemented a bot for auction-based games to analyze competitor behavior and optimize bidding strategies. The bot employed advanced algorithms and data-driven decision-making techniques to maximize success while minimizing expenditure. The project incorporated elements of game theory, opponent analysis, and experimental evaluation to develop a dynamic, competitive bidding strategy.

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Key Responsibilities:

1. Data Analysis and Insights:

   • Analyzed auction data to identify patterns in bidding behavior and calculated competitor budgets using Python.

   • Applied statistical methods to evaluate opponent strategies and dynamic auction scenarios.

2. Algorithm Development:

   • Developed and implemented machine learning-inspired algorithms to dynamically adjust bidding strategies.

   • Utilized game theory concepts, including Nash equilibrium, to optimize decision-making under competitive conditions.

3. Workflow Design and Visualization:

   • Designed efficient workflows for real-time data processing and strategy adjustment.

   • Created actionable visualizations using Python and Power BI to showcase performance metrics and insights.

4. Experimental Evaluation:

   • Conducted rigorous testing under varied auction scenarios (e.g., first-price, second-price auctions).

   • Benchmarked the bot’s performance against baseline strategies, demonstrating a win rate of 70% across multiple simulations.

5. Documentation and Communication:

   • Authored a detailed dissertation (18,000 words) documenting the methodologies, findings, and practical applications of the bot.

   • Communicated complex strategies and findings effectively through well-structured reports and presentations.

Results and Impact:

• Developed a scalable framework for analyzing and optimizing auction strategies in competitive environments.

• Achieved high efficiency in budget utilization and decision-making accuracy, with significant improvements over baseline approaches.

• Provided a foundation for integrating machine learning and game theory principles into real-world auction systems.

Skills and Technologies:

• Programming & Tools: Python, Power BI, Excel

• Machine Learning & Optimization: Game theory, dynamic optimization, algorithm development

• Data Analysis: Statistical analysis, behavioral modeling, performance benchmarking

• Visualization & Reporting: Power BI, Python libraries, technical documentation

Relevance for Data Roles:
This project demonstrates expertise in analyzing large datasets, deriving actionable insights, applying optimization algorithms, and creating impactful visualizations skills essential for data analyst, data science, and machine learning roles.