Statistical foundations of ecological rationality
Brighton,Henry
Brighton,Henry
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.
Description
Date
2020-01-27
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
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Journal Issue
Keywords
Cognitive science, rationality, ecological rationality, bounded rationality, bias bias, bias/variance dilemma, Bayesianism, machine learning, pattern recognition, ecision making under uncertainty, unquantifiable uncertainty, A12 - Relation of Economics to Other Disciplines, B4 - Economic Methodology, C1 - Econometric and Statistical Methods and Methodology: General, C44 - Operations Research ; Statistical Decision Theory, C52 - Model Evaluation, Validation, and Selection, C53 - Forecasting and Prediction Methods ; Simulation Methods, C63 - Computational Techniques ; Simulation Modeling, D81 - Criteria for Decision-Making under Risk and Uncertainty
Citation
Brighton, H 2020, 'Statistical foundations of ecological rationality', Economics: The Open-Access, Open-Assessment E-Journal, vol. 14, 20202, pp. 1-32. https://doi.org/10.5018/economics-ejournal.ja.2020-2
License
info:eu-repo/semantics/openAccess