Name: B Furtado
Type: User
Company: Ipea
Bio: Background in urban economics and spatial analysis. PhD.
Interested in agent-based modeling of Public Policies at Institute for Applied Economic Research
Twitter: furtadobb
Location: Brasília, Brazil
Blog: https://sites.google.com/view/bernardo-alves-furtado
B Furtado's Projects
Simple demonstration of von Thünen's rings'
AgentPy is an open-source framework for the development and analysis of agent-based models in Python.
Complexity Weekend May 21 Albatross Project
Tests on alternative of fiscal redistribution among municipalities in Brazilian metropolis
Modelo inicial de análise de posse de armas
The firm is the institutional means that employees use to work together and achieve economies of scale whereas applying the same level of effort. This work investigates the dynamic process of aggregation of individuals in firms, reflecting the dynamics of the firms themselves. A network-based agent-model is adapted from Axtell (2013) and simulated for the case of Belo Horizonte and neighboring municipalities, with two network alternatives. The results are able to replicate empirical aggregate data. They also show a higher intrinsic volatility in the model which the contact network is restricted to by municipality.
One day course on causal inference, MPI-EVA 9 September 2021
data wrangling, census, Brazil
projetofinal
COVID-19 Agent-based Simulator (Covasim): a model for exploring coronavirus dynamics and interventions
Repositório destinado ao registro dos dojos realizados
Policy Analysis for the Auto Industry
Simulation of Income Contingent Loan (ICL) policy scheme for Brazil
This is the repository for IDP's class (November, 2020)
Easy access to official spatial data sets of Brazil in R (and soon in Python too)
Site do Grupo de Usuários Python de Brasília
Generalization of Stable Marriage Problem: group sizes and active members within each group
A tentative website
humans of simulated new york
Agent-based model of the UK housing market.
Studying innovation and labor markets via ABM
Notebooks and code for the book "Introduction to Machine Learning with Python"