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This Python implementation tries to model school choice and resulting school segregation based on the work of Schelling (1971) and Stoica & Flache (2014).
Empirically calibrated agent-based modelling offers a powerful tool for future research, but it does require more computational power than traditional methodologies used in school choice/segregation research. Fortunately, the efforts Jisk Attema and Ji Qi from the eScience centre effectively helped us improving code efficiency.
Empirical calibration of full scale agent-based models of school choice
Modelling consequences for school segregation with an agent-based model