Ants learn to rely on more informative attributes during decision-making
Author(s) -
Takao Sasaki,
Stephen C. Pratt
Publication year - 2013
Publication title -
biology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.596
H-Index - 110
eISSN - 1744-957X
pISSN - 1744-9561
DOI - 10.1098/rsbl.2013.0667
Subject(s) - weighting , heuristics , ranking (information retrieval) , biology , selection (genetic algorithm) , set (abstract data type) , nest (protein structural motif) , machine learning , artificial intelligence , computer science , medicine , biochemistry , radiology , programming language , operating system
Evolutionary theory predicts that animals act to maximize their fitness when choosing among a set of options, such as what to eat or where to live. Making the best choice is challenging when options vary in multiple attributes, and animals have evolved a variety of heuristics to simplify the task. Many of these involve ranking or weighting attributes according to their importance. Because the importance of attributes can vary across time and place, animals might benefit by adjusting weights accordingly. Here, we show that colonies of the ant Temnothorax rugatulus use their experience during nest site selection to increase weights on more informative nest attributes. These ants choose their rock crevice nests on the basis of multiple features. After exposure to an environment where only one attribute differentiated options, colonies increased their reliance on this attribute relative to a second attribute. Although many species show experience-based changes in selectivity based on a single feature, this is the first evidence in animals for adaptive changes in the weighting of multiple attributes. These results show that animal collectives, like individuals, change decision-making strategies according to experience. We discuss how these colony-level changes might emerge from individual behaviour.
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