Enhancing Market Efficiency with AI: A Game-Theoretic Approach to Mitigating Information Asymmetry in Digital Economies
- Author: Weijia Han, Majoring in Applied Math / Computer Science, Class of 2026, Duke Kunshan University
- Instructor: Professor Luyao Zhang, Duke Kunshan University
- Disclaimer: This is a submission for the Final Project in COMSCI/ECON 206 Computational Microeconomics, Spring 2024 Term (Seven Week), taught by Professor Luyao Zhang at Duke Kunshan University.
- Acknowledgements:I would like to express my gratitude to Prof. Luyao Zhang for the guidance and insights provided throughout the course of COMPSCI/ECON 206 Computational Microeconomics during the 2024 Spring Term (Seven Week - Second) at Duke Kunshan University. This submission to Final Project has been greatly influenced by the teachings and discussions facilitated in this class. Thank you for fostering an environment that enhances our learning and understanding of computational approaches in economic theory.
This repository encompasses all the assignments for the course: Computational Microeconomics. It delves into cutting-edge research topics within computational microeconomics. The directory named CSECON focuses on the evolving landscape of computational economics, emphasizing the integration of human aspects, AI, and computational technology. The section titled Advanced CSECON reviews the limitations of certain computational tools used in game theory experiments and offers a critique of existing studies on federated learning. Additionally, the directory proposal includes a comparative analysis exploring the gaming behaviors of generative AI and reinforcement learning models, addressing the gaps identified in existing research.
- C++ Language
- Multi-modality Large Language Model
- Machine Learning
- Cybersecurity
- Neural Networks
- Quantum Computing
- Anything related to my interests
- Computer Science Projects
"Python", "Java", "HTML", "CSS", "Javascript"
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Intellectual Growth: In my computer science journey, I've come to understand that my greatest intellectual growth emerges from applying abstract technology concepts to tangible societal issues. Within this course, I intend to immerse myself in case studies where machine learning has tackled social and economic challenges. By grappling with the real-world implications, ethical dilemmas, and the necessity for interdisciplinary collaboration, I aim to transform theoretical knowledge into impactful practices. I envision engaging in problem-based learning, interacting with industry experts, and partnering with peers from diverse academic backgrounds to deepen my understanding and application of machine learning.
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Professional Growth: My experience with GitHub, Colab, LaTeX, and various graph tools has laid down a solid technical foundation. To further my professional development in this course, I plan to embark on projects that demand the rigorous use of these tools, advancing from familiarity to mastery. I am also keen on contributing to open-source projects, pursuing internships, and facilitating workshops on these tools to refine my professional skills and prepare myself for the tech industry.
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Living a Purposeful Life: I aspire to be at the forefront of AI security, preventing security issues from obstructing the beneficial use of AI. I can almost visualize a future where I might be honored with recognitions like the Nobel or Turing Award, with a defining phrase to my name: “Pioneering safer AI for a secure future.” To realize this vision, I am committed to contributing to pioneering research in AI security, partnering with global security agencies, educating the public on AI risks, and developing accessible security resources. These endeavors will not only fulfill my personal aspirations but also serve to enhance the collective welfare of human civilization, aligning with a multi-objective perspective of progress and safety.