Tenured researcher @ government think-tank (IPEA) and CNPq (productivity grant - since 2014), complex modeler interested, data fan, transitional Python user, PhD. Background in urban analysis, economics, geography. From twitter.com/furtadobb
Agent-based modeling, urban policy, urban economics. Metropolis and municipalities analyses.
This is an initial exploratory exercise done for the class @ http://thiagomarzagao.com/teaching/ipea/ Text available here: https://arxiv.org/abs/1712.04429v1
Reads output from an ABM model and its parameters’ configuration
Creates a socioeconomic optimal output based on two ABM results of the modelers choice
Organizes the data as X and Y matrices
Trains some Machine Learning algorithms
PolicySpace models public policies within an empirical, spatial environment using data from 46 metropolitan regions in Brazil. The model contains citizens, markets, residences, municipalities, commuting and a the tax scheme. In the associated publications (book in press and https://arxiv.org/abs/1801.00259) we validate the model and demonstrate an application of the fiscal analysis. Besides providing the basics of the platform, our results indicate the relevance of the rules of taxes transfer for cities’ quality of life.
This is an adaptation and extension of Robert Axtell’s model (2013) of endogenous firms, in Python 3.4
The modeling includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The model is spatial and dynamic.