Twenty Lectures on Algorithmic Game Theory

Abstract

Computer science and economics have engaged in a productive conversation over
the past 15 years, resulting in a new field called algorithmic game theory or alternatively
economics and computation. Many problems central to modern computer
science, ranging from resource allocation in large networks to online advertising,
fundamentally involve interactions between multiple self-interested parties. Economics
and game theory oer a host of useful models and definitions to reason
about such problems. The flow of ideas also travels in the other direction, as recent
research in computer science complements the traditional economic literature in
several ways. For example, computer science oers a focus on and a language to
discuss computational complexity; has popularized the widespread use of approximation
bounds to reason about models where exact solutions are unrealistic or
unknowable; and proposes several alternatives to Bayesian or average-case analysis
that encourage robust solutions to economic design problems. The standard
reference in the field [3] is aimed at researchers rather than students and autodidacts,
and it predates the many important results that have appeared over the past
ten years