Minds and Machines 26 (1-2):87-102 (2016)

David Danks
Carnegie Mellon University
Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. Indeed, overall adaptive rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent’s goals. We then use this high-level formal framework on specific cases by demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and characterizing adaptively rational learning for fast-and-frugal one-reason decision-making.
Keywords Adaptive rationality  Bounded rationality  Ecological rationality  Heuristics  Learning  Representation  Cognition
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DOI 10.1007/s11023-015-9370-1
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References found in this work BETA

Simple Heuristics That Make Us Smart.Gerd Gigerenzer, Peter M. Todd & A. B. C. Research Group - 1999 - New York, NY, USA: Oxford University Press USA.
On the Reality of Cognitive Illusions.Daniel Kahneman & Amos Tversky - 1996 - Psychological Review 103 (3):582-591.

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Causal Feature Learning for Utility-Maximizing Agents.David Kinney & David Watson - 2020 - In International Conference on Probabilistic Graphical Models. pp. 257–268.

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