Research Focus
Worked on Python programming applied to Game Theory, developing computational solutions for fair equilibrium problems in environments with competing incentive structures.
Key Contributions
- Built algorithms to find fair equilibria in multi-agent interaction scenarios
- Researched alternative approaches to traditional equilibrium methods for more equitable outcomes
- Processed and analyzed complex datasets related to game-theoretic scenarios using Pandas
International Collaboration
Participated in coordination with researchers from different countries, contributing to a shared theoretical framework for fair distribution mechanisms.