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An agent-based simulation for studying the effect of various parameters on the evolution of teaching.

Easter, Carrie (2018) An agent-based simulation for studying the effect of various parameters on the evolution of teaching. University of Leeds. [Dataset]

Dataset description

An agent-based social learning / teaching simulation, created in NetLogo. Within the simulation, a population of related agents must learn to feed on food patches by learning the correct action associated with the patch in order to gain energy. Learning can occur asocially (independently), socially (learning from others) or via teaching. Teaching and non-teaching alleles present in the population determine the phenotype of an agent and thus whether or not it is able to teach other, naive agents. Agents breed to produce new offspring at regular intervals. The agents which produce the offspring are determined by their energy value, meaning that breeding ultimately depends on an agent's ability to learn correct feeding actions - thus allowing the population to evolve.A number of variables, including the levels of asocial learning, social learning and kin association are easily changable in order to study their effect on the spread of teaching alleles.

Additional information: This material is associated with a paper currently under peer review. It is possible details may change. Any changes will be recorded in the metadata. A link will be added to the published work once it is finalised.
Keywords: social learning, teaching, evolution, simulation, nonhuman animals, kin association, NetLogo, agent-based model, individual-based model
Subjects: C000 - Biological sciences > C300 - Zoology
Divisions: Faculty of Biological Sciences > School of Biology
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited: 26 Apr 2018 12:47
URI: https://archive.researchdata.leeds.ac.uk/id/eprint/344

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