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Agent-Based Simulation of Residential Promoting Policy Effects on Downtown Revitalization

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Abstract

In recent decades, compact cities have become a new concern in urban planning in most Japanese cities. The main reason for this trend among Japanese cities is the phenomenon of de-urbanization and downtown decline that gradually occurred after the 1990s. As such, at present, there are dispersed, small, built-up portions of suburban areas that have resulted in household mobility outside the downtown. Therefore, some local governments in Japan are attempting to realize compact cities through policy intervention, such as encouraging households to relocate from suburban to downtown areas in order to address the population decline in urban areas. Recently, one such residential policy have been promoted by Japanese local city governments. By offering a local housing allowance, this policy encourages households to relocate to downtown areas. We developed an agent-based household residential relocation model (HRRM) to visualize the effect of this residential policy, that is, the local housing allowance. The HRRM is built on households’ adaptive behaviours and interactions through housing relocation choices and policy attitudes, and so it can simulate the diversified residential relocations of households in various lifecycle stages. Through simulation using the HRRM, the effectiveness of this residential policy can be visualized, and the HRRM will help local governments to understand the effects of residential policies.

Suggested Citation

  • Yan Ma & Zhenjiang Shen & Mitsuhiko Kawakami, 2013. "Agent-Based Simulation of Residential Promoting Policy Effects on Downtown Revitalization," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(2), pages 1-2.
  • Handle: RePEc:jas:jasssj:2011-116-3
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    Cited by:

    1. Nicola Lettieri, 2016. "Computational Social Science, the Evolution of Policy Design and Rule Making in Smart Societies," Future Internet, MDPI, vol. 8(2), pages 1-17, May.
    2. Chen Gao & Xiaochong Lan & Nian Li & Yuan Yuan & Jingtao Ding & Zhilun Zhou & Fengli Xu & Yong Li, 2024. "Large language models empowered agent-based modeling and simulation: a survey and perspectives," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-24, December.

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