Rationality and Relevance Realization

Anna Riedl and John Vervaeke

No vymwu, OSF Preprints from Center for Open Science

Abstract: Among the disciplines focusing on the rationality question and ultimately the phenomenon of intelligence in minds, brains, and machines, the Great Rationality Debate 2.0 is taking place between the axiomatic approach to optimality modeling on one side and ecological rationality on the other. The divide between the two stances can be reduced by integrating advancements of each tradition into the other. Traditionally, it is held that taking computational constraints of cognition into account, rational agents face a speed-accuracy trade-off. The resulting lowered normative ceiling of resource-rational optimality is often met by heuristics. We will modify the conception of this trade-off because it lacks a crucial element: Herbert Simon’s scissors analogy indicates that bounded rationality is limited both by internal cognitive constraints as well as the task environment. Examining heuristics through the bias-variance dilemma an organism faces in an unknown territory adds an efficiency-robustness trade-off. These two conflicts cannot be optimized a priori, but have to be negotiated in an emerging bottom-up manner by continuously resolving the frame problem. This process of overcoming the frame problem is referred to as ‘relevance realization’ and it rests on problem transformation, sense-making, abductive reasoning, or insight. The main question of rationality, therefore, changes from a priori optimality to an ongoing optimal fittedness of an organism-environment system. This implies a non-propositional perspective on cognition and a shift of the paradigm to enacted and embodied rationality. Our argument relocates the importance of the axioms of rationality into a sociocultural tool for “small worlds” once the statistical requirements of the large world have been transformed by relevance realization. These cognitive findings have implications for categorization and perception, philosophy of science, economic theory, as well as machine learning, and applied mathematics.

Date: 2022-03-31
New Economics Papers: this item is included in nep-hpe
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:vymwu

DOI: 10.31219/osf.io/vymwu

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