摘要翻译：
Agent计算经济学研究的最新趋势是将政府Agent包含在经济模型中，其决策基于学习算法。在本文中，我们试图评估模拟退火算法在此背景下的性能，考虑了文献中先前提出的一个模型，该模型模拟了一个由地理上分散的公司组成的人工经济，这些公司以代理为模型，试图通过在不同的城市以不同的旅行成本销售相同的产品来获得最大的利润。作者在那里使用了一种进化算法，来建模智能体的决策过程。我们的扩展引入了一个政府代理人，它试图通过不同的税收系数来影响不同市场的供给和需求，以使每个城市的销售量相等。研究了模拟退火算法和简单搜索算法作为政府学习算法时出现的情况，并对两者的性能进行了比较。
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英文标题：
《Simulation and Use of Heuristics for Peripheral Economic Policy》
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作者：
Mattheos K. Protopapas, Elias B. Kosmatopoulos
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最新提交年份：
2009
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分类信息：

一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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英文摘要：
  Recent trends in Agent Computational Economics research, envelop a government agent in the model of the economy, whose decisions are based on learning algorithms. In this paper we try to evaluate the performance of simulated annealing in this context, by considering a model proposed earlier in the literature, which has modeled an artificial economy consisting of geographically dispersed companies modeled as agents, that try to maximize their profit, which is yielded by selling an homogeneous product in different cities, with different travel costs. The authors have used an evolutionary algorithm there, for modeling the agents' decision process. Our extension introduces a government agent that tries to affect supply and demand by different taxation coefficients in the different markets, in order to equate the quantities sold in each city. We have studied the situation that occurs when a simulated annealing algorithm and a simple search algorithm is used as the government's learning algorithm, and we have evaluated the comparative performance of the two.
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PDF链接：
https://arxiv.org/pdf/0905.3808