PolicySpace: a modeling platform

TL;DR

PolicySpace is an agent-based modeling platform that simulates public policy impacts within spatial environments, using data from Brazilian metropolitan regions to analyze fiscal policies and urban quality of life.

Contribution

This paper introduces PolicySpace, a novel agent-based platform for empirical, spatial modeling of public policies with detailed validation and application examples.

Findings

Tax transfer rules significantly affect cities' quality of life.

Model validation confirms the platform's accuracy in policy simulation.

Application demonstrates PolicySpace's utility in fiscal analysis.

Abstract

Public Policy involves proposing changes to existing practices, alternatives, new habits. Citizens and institutions react accordingly, accepting, refuting or adapting. Agent-based modeling is a tool that can enrich the policy analysis package explicitly considering dynamics, space and individual-level interactions. This paper presents a modeling platform called PolicySpace that models public policies within an empirical, spatial environment using data from 46 metropolitan regions in Brazil. We describe the basics of the model, its agents and markets, the tax scheme, the parametrization, and how to run the model. Finally, 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.