Algorithmic Game Theory

Undergraduate course, Giresun University, Department of Computer Engineering, 2026

algorithmic game theory Algorithmic game theory studies strategic interaction through the lens of computation. It combines game-theoretic models with algorithms and complexity analysis to examine equilibria, auctions, market mechanisms, routing, online platforms, fairness, and the behavior of self-interested agents.

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Ders Öğretim Planı (PDF)

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✍️ 2025 Vize & Final | Proje Listesi

Resources

Preliminary Materials:

  • Prerequisites for preparation pdf
  • Key figures who have shaped the field pdf

Chapter 1: Game Theory Fundamentals

Game theory is a mathematical framework used to analyze and understand strategic interactions between rational decision-makers. It provides a systematic way of studying situations where the outcome of one person’s decision depends on the actions of others.

Chapter 2: Introduction to Algorithmic Game Theory

Algorithmic game theory is an interdisciplinary field that combines concepts from game theory and computer science. It focuses on the study of strategic interactions in computational settings and aims to design efficient algorithms and computational models for analyzing and solving games.

Chapter 3: Nash Equilibrium and Strategic Behavior

Nash equilibrium is a central concept in game theory that captures the notion of strategic behavior and stable outcomes in games. It refers to a set of strategies, one for each player, where no player has an incentive to unilaterally deviate from their chosen strategy, assuming all other players stick to their strategies. A Nash equilibrium may involve pure strategies or probability distributions over strategies, known as mixed strategies.

Chapter 4: Mechanism Design and Incentives

Mechanism design is a field of study within game theory and economics that focuses on designing rules or mechanisms to achieve desired outcomes in strategic settings. It involves designing incentive-compatible mechanisms that align the self-interests of individual participants with the desired collective goals. Mechanism design asks how rules can be constructed so that self-interested participants reveal useful information and produce desirable outcomes. Common objectives include incentive compatibility, individual rationality, efficiency, revenue, and social welfare.

Chapter 5: Auctions and Market Design

Auctions and market design are closely related fields that utilize principles from economics, game theory, and mechanism design to study the allocation of goods, resources, and services in various settings. Both areas aim to create efficient and fair mechanisms for matching buyers and sellers, determining prices, and optimizing resource allocation.

Chapter 6: Social Choice and Voting Systems

Social choice theory is a branch of economics and political science that studies methods for aggregating individual preferences or opinions into a collective choice. It explores the challenges and possibilities of making decisions on behalf of a group or society based on the preferences of its members. Voting systems are one of the key tools analyzed in social choice theory.

Chapter 7: Multi-Agent Systems

Multi-agent systems refer to scenarios where multiple self-interested agents interact and make decisions in a shared environment. Multi-agent systems are analyzed to understand the strategic behavior of agents, optimize outcomes, and design mechanisms that align individual incentives with system objectives.

Chapter 8: Network Design and Routing

In algorithmic game theory, network design and routing are studied with a focus on understanding the strategic behavior of self-interested agents and optimizing network efficiency and fairness. Selfish routing games illustrate how individually optimal route choices can produce globally inefficient traffic patterns.

Chapter 9: Online Platforms

In algorithmic game theory, online platforms are studied as environments where self-interested agents interact and make decisions in the pursuit of their own objectives. Online platforms encompass various digital platforms, such as e-commerce platforms, social media platforms, ride-sharing platforms, and online auctions.

Chapter 10: Algorithmic Fairness and Ethics

Algorithmic fairness studies how decisions and resource allocations affect different individuals or groups. Algorithmic ethics considers the broader responsibilities, harms, transparency, accountability, and societal consequences of computational mechanisms.

Chapter 11: Price of Anarchy and Efficiency

Price of Anarchy (PoA) is a measure that quantifies the loss of efficiency in a system due to the selfish behavior of individual agents. It evaluates the impact of strategic decision-making on the overall performance or welfare of a system. The Price of Anarchy compares the social welfare or cost of the worst equilibrium with that of an optimal centrally coordinated outcome.

Chapter 12: Learning and Adaptation

Learning and adaptation play a significant role in algorithmic game theory by addressing how self-interested agents can improve their decision-making strategies over time and adapt to changing environments.

Chapter 13: Decision-Making

Decision-making is a central concept that focuses on how self-interested agents make choices or decisions in strategic situations. Decision-making in algorithmic game theory involves analyzing the strategic interactions among agents, understanding their preferences and objectives, and designing mechanisms and algorithms to optimize outcomes.

Chapter 14: Future Directions

Algorithmic game theory is a rapidly evolving field that continues to explore new directions and address emerging challenges.