Abstract
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 (a) demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and (b) characterizing adaptively rational learning for fast-and-frugal one-reason decision-making.
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Notes
That is, methods for which there are proofs of their asymptotic and/or short-run reliability.
In particular, the sums of probabilities within each partition element must be the same.
There are many ways to have “representations at the level of partition elements,” including both coarsened representations and inattention to particular distinctions in the world.
More precisely, for each representation r, let O r be the vector of “optimal responses” for the N goals (i.e., O r = < a 1,…,a N > where a i = D(r, V Gi )). It is straightforward to prove: if all partitions are equally costly, then r and s are in the same element of the optimal partition if and only if O r = O s .
R could be the space of possible cue validities or another similar representational structure.
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Acknowledgments
Thanks to participants at the 2013 workshop on Finding Foundations for Bounded and Adaptive Rationality for helpful comments and criticisms, particularly Stefan Herzog, Ralf Mayrhofer, and Peter Todd. Research for this paper was partially supported by a James S. McDonnell Foundation Scholar Award to Danks.
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Wellen, S., Danks, D. Adaptively Rational Learning. Minds & Machines 26, 87–102 (2016). https://doi.org/10.1007/s11023-015-9370-1
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DOI: https://doi.org/10.1007/s11023-015-9370-1