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This repository contains the simulation code for the paper

Paul Gölz and Ariel D. Procaccia: Migration as Submodular Optimization. 2018.

The paper is freely available at


  • Python 3.6 (higher versions might work, but so far are not supported by Gurobi)
  • Gurobi with gurobipy python bindings (we used version 8.0.1)
  • Igraph (0.7.1)
  • Seaborn (0.9.0)
  • Pandas (0.23.4)
  • Jupyter (4.4.0)

For academic use, Gurobi provides free licenses at

At the time of this writing, the installation manual for igraph on OSX is out of date. We had to directly install brew install igraph, then pip install python-igraph. Should you see the warning

DeprecationWarning: To avoid name collision with the igraph project, this visualization library has been renamed to 'jgraph'. Please upgrade when convenient.

when importing igraph, then the following StackOverflow answer might help:

Replication of experiments in the paper

The individual experiments are provided as Jupyter/IPython notebooks. If you just want to read them, the easiest way is to go to the corresponding page on Github, e.g.,, where they can be viewed in a browser. If you want to replicate our results or experiment with different parameter settings, you need to install the dependencies mentioned above. Then, running, say, jupyter notebook num_localities.ipynb opens a browser window, in which you can see our simulation results and easily rerun them.

While we fix the random seed to 0 in all our experiments, your simulations might produce slightly different results due to different library versions being used, non-determinism in these libraries or Python implementation details such as the iteration order over dictionaries. For what it is worth, we ran our simulations on a MacBook Pro (2017) with a 3.1 GHz Intel Core Duo i5 processor, with 16 GB of RAM, and running MacOS 10.12.6.


For questions on the simulations, please contact Paul Gölz.


Simulation code for the paper “Migration as Submodular Optimization” by Paul Gölz and Ariel D. Procaccia.







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