Urban planning is a complex problem which includes choosing a social objective for a city, finding the associated optimal allocation of agents and identifying instruments like subsidies to decentralize this allocation as a market equilibrium. We split the problem in two independent steps. First, we find the short-term optimal allocation for a social objective and, second, we derive subsidies that reproduce this optimal allocation as a market equilibrium. This splitting is supported by a fundamental result asserting that the optimal allocation of any social objective can be decentralized by applying feasible subsidies, which can be computed even in the case with location externalities and transportation costs. In the first step, we compute the optimal allocation using an algorithm to solve a convex urban planning problem, which is applicable to a wide class of objective functions. In the second step, we compute optimal subsidies in several political situations for the planner, like budget constraints and limited impact on specific agents, zones, rents and/or utilities. As an example, we simulate a prototype city which aims at improving social inclusion.

中文翻译：

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短期土地利用规划和最佳补贴

城市规划是一个复杂的问题，包括选择城市的社会目标，找到相关的代理商最佳分配以及确定诸如补贴之类的工具以将这种分配分散为市场均衡。我们将问题分为两个独立的步骤。首先，我们找到了针对社会目标的短期最优分配，其次，我们获得了将这种最优分配再现为市场均衡的补贴。这种分裂得到一个基本结果的支持，该基本结果断言，可以通过应用可行的补贴来分散任何社会目标的最优分配，即使在位置外部性和运输成本的情况下也可以计算出这种补贴。第一步，我们使用一种算法来计算最优分配，以解决凸的城市规划问题，这适用于多种目标函数。第二步，我们在计划者的几种政治情况下计算最佳补贴，例如预算约束和对特定代理商，区域，租金和/或公用事业的有限影响。例如，我们模拟了一个旨在改善社会包容性的原型城市。