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PolicySpace2: Modeling Markets and Endogenous Public Policies

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Abstract

Policymakers' role in decision making on alternative policies is facing restricted budgets and an uncertain future. The need to decide on priorities and handle effects across policies has made their task even more difficult. For instance, housing policies involve heterogeneous characteristics of the properties themselves and the intricacy of housing markets within the spatial context of cities. Here, we have proposed PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit, and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks by integrating economic, spatial and transport research. PS2 is applied here as a comparison of three competing public policies aimed at reducing inequality and alleviating poverty: (a) house acquisition by the government and distribution to lower income households, (b) rental vouchers and (c) monetary aid. Within the model context, monetary aid, that is smaller amounts of help for a larger number of households, improves the economy in terms of production, consumption, reduction of inequality and maintenance of financial duties. PS2 is also a framework that can be further adapted to a number of related research questions.

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  • Bernardo Alves Furtado, 2022. "PolicySpace2: Modeling Markets and Endogenous Public Policies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 25(1), pages 1-8.
  • Handle: RePEc:jas:jasssj:2021-112-2
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    1. Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.

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