Systematic mistakes are likely in bounded optimal decision-making systems

Adi Livnat, Nicholas Pippenger

Research output: Contribution to journalArticlepeer-review

Abstract

Systematic mistakes can be distinguished from other types of mistakes in that they are repeatable and predictable within a given organism, and are not due to uncertainty or lack of information. Here we provide a mathematical definition for the concept of systematic mistakes, which captures the way this concept has been used in the behavioral sciences. We also provide an analytical model of information processing networks that are made of large numbers of components, in analogy to the brain being made of a large number of neurons. We show that, for almost all behavioral tasks, and for a wide range of limitations on the computational complexity of the decision-making network, the best possible decision-makers will make systematic mistakes. This result, together with available empirical evidence, suggests that violations of rationality in humans and animals are consistent with natural selection, as the latter operates under constraints.

Original languageEnglish
Pages (from-to)410-423
Number of pages14
JournalJournal of Theoretical Biology
Volume250
Issue number3
DOIs
StatePublished - 7 Feb 2008
Externally publishedYes

Bibliographical note

Funding Information:
We thank Simon Levin, Eric Maskin, Steve Pacala, Steve Pratt and Kim Weaver for invaluable comments on earlier versions of this manuscript. We thank Marissa Baskett, Jonathan Dushoff, Jim Gould, Henry Horn, Holger Krapp, John McNamara, Dan Rubenstein and Eldar Shafir for conversations during the course of the work. A.L. was supported by the Burroughs-Wellcome Fund, the Pew Charitable Trust, and the Miller Institute for Basic Research in Science. N.P. was supported by National Science Foundation Grant CCF-0430656.

Keywords

  • Behavioral ecology
  • Bounded rationality
  • Computational complexity
  • Decision-making
  • Heuristics and biases
  • Rules of thumb

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability
  • Modeling and Simulation

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