Abstract
Growth of simulation methods in social sciences lead to a turn in the way we think about experimental approach in humanities. Simulation experiments inside artificial urban environments are one of the emerging research fields that may result in particularly interesting conclusions. The reason for this is straightforward link to the city governance and urban planning or, in general, the quality of life in cities. In this paper, we develop the universal conceptual framework for building agent-based models of the real cities: Complex Artificial Urban System (CAUSE). The geographical space in CAUSE is projected through GIS data. Agents’ behaviors in the virtual sandbox replicate the major economic activities of humans. By assumption, we focus on the labor market and real estate market—these two crucial urban markets are modeled through the agent-based matching function approach. Inhabitants of the artificial city try to achieve the highest possible level of satisfaction maximizing individual utility function. This function is based on geographically adjusted five-level Maslow’s pyramid. Analysis of such urban sandbox will help to address the vital questions about the role of physical city environment in both: the cities’ economic performance and the decisions made by their inhabitants. As a result, CAUSE-based models could support the decisions made by policy makers to improve the quality of city life.
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Wozniak, M. (2021). Conceptual Framework for Modeling Complex Urban Systems—From Theoretical Assumptions to Empirical Basis. In: Ahrweiler, P., Neumann, M. (eds) Advances in Social Simulation. ESSA 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-61503-1_48
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