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Betting Blind: Coping with Uncertainty through Redundancy

Pedroso, Makmiller (2022) Betting Blind: Coping with Uncertainty through Redundancy. [Preprint]

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Abstract

Multiple biological groups, such as ant colonies, appear to have a noteworthy inefficiency: they contain vast amounts of redundant members that are not strictly needed to maintain the group. Philosophers and biologists have proposed that such inefficiency is illusory because redundancy enhances the resilience of groups when living under harsh conditions. Still, this proposal is unsatisfactory in different respects. First, it is too vague to account for when redundancy is selectively advantageous. Furthermore, it overlooks cases in which redundancy fails to increase the resilience of groups. This paper offers an account of group redundancy that addresses these difficulties. Specifically, it advances the hypothesis that the mere presence of harsh conditions is not what drives the evolution of redundancy; rather, it is the fact organisms are often unable to predict when they will face harsh conditions. Redundancy enables groups to properly respond to unpredictable circumstances without resorting to the unreliable detection systems of their members. A better understanding of the phenomenon of redundancy is likely to impact other key issues in philosophy of science, including the evolution of cooperation and transitions in individuality, and the role of redundancy in complex systems, be they biological or not.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Pedroso, Makmillermpedroso@towson.edu
Keywords: redundancy; degeneracy; uncertainty; evolution; cooperation; complex systems.
Subjects: Specific Sciences > Biology > Evolutionary Theory
Specific Sciences > Complex Systems
Specific Sciences > Engineering
General Issues > Game Theory
General Issues > Models and Idealization
Depositing User: Makmiller Pedroso
Date Deposited: 24 Apr 2022 18:13
Last Modified: 24 Apr 2022 18:13
Item ID: 20501
Subjects: Specific Sciences > Biology > Evolutionary Theory
Specific Sciences > Complex Systems
Specific Sciences > Engineering
General Issues > Game Theory
General Issues > Models and Idealization
Date: 2022
URI: http://philsci-archive.pitt.edu/id/eprint/20501

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