Beyond quantified ignorance: Rebuilding rationality without the bias bias - Henry Brighton (Kiel Institute for the World Economy (IfW), 2019)
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 rationalitymirror fundamental and longstanding divisions in statistics.
Keywords: A12; B4; C1; C44; C52; C53; C63; D18; 330; cognitive science; rationality; ecological rationality; bounded rationality; bias bias; bias/variance dilemma; Bayesianism; machine learning; pattern recognition; decision making under uncertainty; unquantifiable uncertainty