Algorithmic Game Theory for Beginners

How do self-interested agents interact in complex networks, markets, and computational systems? Algorithmic game theory bridges the gap between economics and computer science, providing the tools to analyze and design systems where participants act strategically. This course guides you through the essential mathematical models and algorithms needed to understand these strategic interactions. You will transition from a beginner to a confident practitioner capable of analyzing strategic environments, predicting agent behavior, and applying computational concepts to economic models. By studying foundational definitions first, you will build a solid base to tackle complex multi-agent scenarios. What you'll learn: - Understand foundational concepts of game theory, including strategic form games and utility theory - Compute Nash equilibria and analyze their existence in various strategic settings - Analyze the Price of Anarchy and Price of Stability to measure the efficiency of decentralized systems - Apply mechanism design principles to create truth-revealing auctions and voting systems - Explore modern computational complexity in game theory, focusing on the hardness of finding equilibria - Practice modeling real-world network routing and resource allocation problems as games This course begins with core definitions of players, strategies, and payoffs, before moving into equilibrium concepts, computational complexity, and mechanism design. You will progress through clear, written explanations and structured analytical exercises designed to reinforce your theoretical understanding. This course is designed for beginners in computer science, mathematics, or economics who want to learn how algorithmic thinking applies to strategic decision-making. No prior background in game theory is required. Start your journey into strategic computational analysis today.