Network connectivity dynamics model

Agent-based model to calculate convergence and adaptiveness by manipulating connecitvity dynamic and cognitive biases. As described in Segovia Martín, J., Walker, B., Fay, N. & Tamariz, M. (2019). "Network connectivity dynamics affect the evolution of culturally transmitted variants". Preprint https://arxiv.org/abs/1902.06598

The purpose of the model is to understand how the interaction between cognitive biases, memory and the order in which agents pair with each other over time affect convergence. It also aims to evaluate the relative importance of each parameter combination and make predictions on the evolution of cultural diversity.

To successfully run these scripts, you need a number of Python packages.They are all listed in the scripts.

You can manipulate a number of parameters: Content bias, Coordination bias, Pair composition, Memory size, Number of agents, Number of rounds, Innovation rate.

In a second version of the model (2.0) you will also be able to manipulate value systems and institutional reinforcement.

The file named Experiment_Data_and_code contains experimental data and simulation code to perform the analyses described in "Testing early and late connectivity dynamics in the lab: an experiment using 4-agent micro-societies" (preprint forthcoming).

In the following link you can visualise how the simulations look like: https://jsegoviamartin.github.io/simulations/

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Bib TeX of the preprint

@ARTICLE{2019arXiv190206598S,
       author = {{Segovia Mart{\'\i}n}, Jos{\'e} and {Walker}, Bradley and
         {Fay}, Nicolas and {Tamariz}, Monica},
        title = "{Network connectivity dynamics affect the evolution of culturally transmitted variants}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Social and Information Networks, Computer Science - Computation and Language},
         year = "2019",
        month = "Feb",
          eid = {arXiv:1902.06598},
        pages = {arXiv:1902.06598},
archivePrefix = {arXiv},
       eprint = {1902.06598},
 primaryClass = {cs.SI},
       adsurl = {https://ui.adsabs.harvard.edu/\#abs/2019arXiv190206598S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}