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Mechanisms of collective learning: how can animal groups improve collective performance when repeating a task?
- Source :
-
Philosophical Transactions of the Royal Society B: Biological Sciences . 4/10/2023, Vol. 378 Issue 1874, p1-14. 14p. - Publication Year :
- 2023
-
Abstract
- Learning is ubiquitous in animals: individuals can use their experience to finetune behaviour and thus to better adapt to the environment during their lifetime. Observations have accumulated that, at the collective level, groups can also use their experience to improve collective performance. Yet, despite apparent simplicity, the links between individual learning capacities and a collective's performance can be extremely complex. Herewe propose a centralized and broadly applicable framework to begin classifying this complexity. Focusing principally on groups with stable composition, we first identify three distinct ways through which groups can improve their collective performance when repeating a task: each member learning to better solve the task on its own, members learning about each other to better respond to one another and members learning to improve their complementarity. We show through selected empirical examples, simulations and theoretical treatments that these three categories identify distinct mechanisms with distinct consequences and predictions. These mechanisms extend well beyond current social learning and collective decision-making theories in explaining collective learning. Finally, our approach, definitions and categories help generate new empirical and theoretical research avenues, including charting the expected distribution of collective learning capacities across taxa and its links to social stability and evolution. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SOCIAL learning
*SOCIAL stability
*SOCIAL evolution
*LEARNING
Subjects
Details
- Language :
- English
- ISSN :
- 09628436
- Volume :
- 378
- Issue :
- 1874
- Database :
- Academic Search Index
- Journal :
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 162018922
- Full Text :
- https://doi.org/10.1098/rstb.2022.0060