Science Advances

Supplementary Materials

This PDF file includes:

  • Section S1. Social learning success probability
  • Section S2. An alternative social learning model
  • Section S3. Network metrics for fixed values of pn and pr
  • Section S4. Coupling pr to pn to limit degree centrality
  • Section S5. Time series for simulations with evolving pn and pr
  • Section S6. Low mutation rate
  • Section S7. Connection costs
  • Section S8. Varying population size and trait number
  • Section S9. Varying innovation and social learning success rate
  • Fig. S1. The effect of increasing memory on trait repertoire and highest skill level.
  • Fig. S2. If memory size is limited, then the two different social learning algorithms are qualitatively the same.
  • Fig. S3. The effect of complex contagion on social learning dynamics, and of linking parameters on network characteristics.
  • Fig. S4. Distribution of common traits depends on average connectivity.
  • Fig. S5. Trait proficiency depends on the level of trait convergence and connectivity.
  • Fig. S6. Trajectories for linking probabilities pn and pr averaged over all simulation runs for all three selection regimes (neutral, generalist, and specialist).
  • Fig. S7. Results displayed as in <Fig. 2 of the main text but with mutation rate μ = 0.01.
  • Fig. S8. Adding a cost per connection reduces average degree in specialists, whereas generalists are less affected.
  • Fig. S9. Added connection costs.
  • Fig. S10. Varying the number of traits and individuals in a population.
  • Fig. S11. Increasing population size also increases trait diversity in the population.
  • Fig. S12. Varying innovation and social learning success rate.
  • Reference (64)

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