Statistical foundations of ecological rationality

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If we reassess the rationality question under the assumption that the uncertainty of the natural world is largely unquantifiable, where do we end up? In this article the author argues that we arrive at a statistical, normative, and cognitive theory of ecological rationality. The main casualty of this rebuilding process is optimality. Once we view optimality as a formal implication of quantified uncertainty rather than an ecologically meaningful objective, the rationality question shifts from being axiomatic/probabilistic in nature to being algorithmic/predictive in nature. These distinct views on rationality mirror fundamental and long-standing divisions in statistics.
Original languageEnglish
Article number20202
Pages (from-to)1-32
Number of pages32
JournalEconomics: The Open-Access, Open-Assessment E-Journal
Publication statusPublished - 27 Jan 2020


  • Cognitive science
  • rationality
  • ecological rationality
  • bounded rationality
  • bias bias
  • bias/variance dilemma
  • Bayesianism
  • machine learning
  • pattern recognition
  • ecision making under uncertainty
  • unquantifiable uncertainty


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