 Algorithmic game theory

Algorithmic game theory studies the interaction of algorithms with strategic behavior. It asks both how hard equilibria are to compute and how mechanisms should be designed when participants choose actions to improve their own utility.

For example, a shortest-path algorithm optimizes travel time for one specified traveler, while a routing game asks what happens when many travelers each choose a route. A Nash equilibrium can have a larger total delay than a centrally chosen allocation. Quantifying that loss by a price of anarchy links incentives with computational optimization. The input representation, equilibrium concept and allowed running time must all be specified.

Algorithmic game theory is an interdisciplinary field that combines concepts from computer science, game theory, and economics to study and design algorithms and computational systems that can solve problems related to strategic interactions among rational agents. The focus is on understanding how these agents make decisions, how to predict their behavior, and how to design mechanisms and systems that can lead to desirable outcomes.