Randomness – A Computational Complexity Perspective

  • Avi Wigderson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5010)


Man has grappled with the meaning and utility of randomness for centuries. Research in the Theory of Computation in the last thirty years has enriched this study considerably. This lecture will describe two main aspects of this research on randomness, demonstrating its power and weakness respectively.

Randomness is paramount to computational efficiency: The use of randomness seems to dramatically enhance computation (and do other wonders) for a variety of problems and settings. In particular, examples will be given of probabilistic algorithms (with tiny error) for natural tasks in different areas, which are exponentially faster than their (best known) deterministic counterparts.

Computational efficiency is paramount to understanding randomness: We will explain the computationally-motivated definition of “pseudorandom” distributions, namely ones which cannot be distin- guished from the uniform distribution by any efficient procedure from a given class. Using this definition, we show how such pseudorandomness may be generated deterministically, from (appropriate) computationally difficult problems. Consequently, randomness is probably not as powerful as it seems above.

We conclude with the power of randomness in other computational settings, such as space complexity and probabilistic proof systems. In particular we’ll discuss the remarkable properties of Zero-Knowledge proofs and of Probabilistically Checkable proofs.

The bibliography contains several useful books and surveys in which material pertaining to the computational randomness may be found. In particular, we include surveys on topics not covered in the lecture, including extractors (designed to purify weak random sources) and expander graphs (perhaps the most useful “pseudorandom” object).


randomness complexity pseudorandom derandomnization 


  1. 1.
    Goldreich, O.: Modern Cryptography, Probabilistic Proofs and Pseudorandomness. Algorithms and Combinatorics, vol. 17. Springer, Heidelberg (1998)Google Scholar
  2. 2.
    Motwani, R., Raghavan, P.: Randomized Algorithms. Cambridge University Press, Cambridge (1995)zbMATHGoogle Scholar
  3. 3.
    Shaltiel, R.: Recent Developments in Explicit Constructions of Extractors. Bull. EATCS 77, 67–95 (2002)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Wigderson, A.: P, NP and Mathematics — A computational complexity perspective. In: Proceedings of the ICM 2006, Madrid, vol. I, pp. 665–712. EMS Publishing House, Zurich (2007), Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Avi Wigderson
    • 1
  1. 1.School of MathematicsInstitute for Advanced StudyPrincetonUSA

Personalised recommendations