Algorithms, games, and evolution

Erick Chastain, Adi Livnat, Christos Papadimitriou, Umesh Vazirani

Research output: Contribution to journalArticlepeer-review


Even the most seasoned students of evolution, startingwith Darwin himself, have occasionally expressed amazement that the mechanism of natural selection has produced the whole of Life as we see it around us. There is a computational way to articulate the same amazement: "What algorithm could possibly achieve all this in a mere three and a half billion years?" In this paper we propose an answer: We demonstrate that in the regime of weak selection, the standard equations of population genetics describing natural selection in the presence of sex become identical to those of a repeated game between genes played according to multiplicative weight updates (MWUA), an algorithm known in computer science to be surprisingly powerful and versatile. MWUA maximizes a tradeoff between cumulative performance and entropy, which suggests a new view on the maintenance of diversity in evolution.

Original languageEnglish
Pages (from-to)10620-10623
Number of pages4
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number29
StatePublished - 22 Jul 2014
Externally publishedYes


  • Coordination games
  • Learning algorithms

ASJC Scopus subject areas

  • General


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