Algorithmic game theory

Algorithmic game theory (AGT) is an area in the intersection of game theory and computer science, with the objective of understanding and design of algorithms in strategic environments. Typically, in Algorithmic Game Theory problems, the input to a given algorithm is distributed among many players who have a personal interest in the output. In those situations, the agents might not report the input truthfully because of their own personal interests. We can see Algorithmic Game Theory from two perspectives: Analysis: given the currently implemented algorithms, analyze them using Game Theory tools (e.g., calculate and prove properties on their Nash equilibria, price of anarchy, and best-response dynamics) Design: design games that have both good game-theoretical and algorithmic properties. This area is called algorithmic mechanism design. On top of the usual requirements in classical algorithm design (e.g., polynomial-time running time, good approximation ratio), the designer must also care about incentive constraints. In 1999, the seminal paper of Nisan and Ronen drew the attention of the Theoretical Computer Science community to designing algorithms for selfish (strategic) users. As they claim in the abstract: We consider algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest. As such participants, termed agents, are capable of manipulating the algorithm, the algorithm designer should ensure in advance that the agents’ interests are best served by behaving correctly. Following notions from the field of mechanism design, we suggest a framework for studying such algorithms. In this model the algorithmic solution is adorned with payments to the participants and is termed a mechanism. The payments should be carefully chosen as to motivate all participants to act as the algorithm designer wishes. We apply the standard tools of mechanism design to algorithmic problems and in particular to the shortest path problem.

Game-theoretic Mechanisms for Eliciting Accurate Information

Distributional Robustness in Mechanism Design

Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd

Game-theoretic Mechanisms for Eliciting Accurate Information

Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd

Distributional Robustness in Mechanism Design