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Beyond the Worst-Case Analysis of Algorithms
  • Edited by Tim Roughgarden
  • Published online:
    17 December 2020
    Print publication:
    14 January 2021
  • There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.

  • Preface

  • Tim Roughgarden, Stanford University, California
  • Book:
    Twenty Lectures on Algorithmic Game Theory
    Published online:
    05 August 2016
    Print publication:
    30 August 2016, pp xi-xiv

  • Summary

    Computer science and economics have engaged in a lively interaction 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 offer 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 offers 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.

    This book grew out of my lecture notes for my course “Algorithmic Game Theory,” which I taught at Stanford five times between 2004 and 2013. The course aims to give students a quick and accessible introduction to many of the most important concepts in the field, with representative models and results chosen to illustrate broader themes. This book has the same goal, and I have stayed close to the structure and spirit of my classroom lectures. Brevity necessitates omitting several important topics, including Bayesian mechanism design, compact game representations, computational social choice, contest design, cooperative game theory, incentives in cryptocurrencies and networked systems, market equilibria, prediction markets, privacy, reputation systems, and social computing. Many of these areas are covered in the books by Brandt et al. (2016), Hartline (2016), Nisan et al. (2007), Parkes and Seuken (2016), Shoham and Leyton-Brown (2009), and Vojnović (2016).

    Reading the first paragraph of every lecture provides a quick sense of the book's narrative, and the “top 10 list” on pages 299–300 summarizes the key results in the book. In addition, each lecture includes an “Upshot” section that highlights its main points. After the introductory lecture, the book is loosely organized into three parts.

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