Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/213846
Authors: 
Brighton, Henry
Year of Publication: 
2020
Citation: 
[Journal:] Economics: The Open-Access, Open-Assessment E-Journal [ISSN:] 1864-6042 [Volume:] 14 [Year:] 2020 [Issue:] 2020-2 [Pages:] 1-32
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.
Subjects: 
cognitive science
rationality
ecological rationality
bounded rationality
bias bias
bias/variance dilemma
Bayesianism
machine learning
pattern recognition
decision making under uncertainty
unquantifiable uncertainty
JEL: 
A12
B4
C1
C44
C52
C53
C63
D81
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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