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Data from: Modelling the spread of innovation in wild birds

Citation

Shultz, Thomas R.; Montrey, Marcel; Aplin, Lucy M. (2018), Data from: Modelling the spread of innovation in wild birds, Dryad, Dataset, https://doi.org/10.5061/dryad.2p5rb

Abstract

We apply three plausible algorithms in agent-based computer simulations to recent experiments on social learning in wild birds. Although some of the phenomena are simulated by all three learning algorithms, several manifestations of social conformity bias are simulated by only the approximate majority (AM) algorithm, which has roots in chemistry, molecular biology and theoretical computer science. The simulations generate testable predictions and provide several explanatory insights into the diffusion of innovation through a population. The AM algorithm's success raises the possibility of its usefulness in studying group dynamics more generally, in several different scientific domains. Our differential-equation model matches simulation results and provides mathematical insights into the dynamics of these algorithms.

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