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Statistical foundations of ecological rationality

Henry Brighton

Economics - The Open-Access, Open-Assessment E-Journal, 2020, vol. 14, No 2020-2, 32 pages

Abstract: 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.

Keywords: cognitive science; rationality; ecological rationality; bounded rationality; bias bias; bias/variance dilemma; Bayesianism; machine learning; pattern recognition; decision making under uncertainty; unquantifiable uncertainty (search for similar items in EconPapers)
JEL-codes: A12 B4 C1 C44 C52 C53 C63 D81 (search for similar items in EconPapers)
Date: 2020
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http://dx.doi.org/10.5018/economics-ejournal.ja.2020-2
https://www.econstor.eu/bitstream/10419/213846/1/1689770554.pdf (application/pdf)

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