英文标题：
《A bright future for financial agent-based models》
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作者：
J. Lussange, A. Belianin, S. Bourgeois-Gironde, B. Gutkin
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最新提交年份：
2018
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英文摘要：
  The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the field of ACE has also received critics by a part of the social science community for its lack of empiricism. Yet recent trends have shifted the weights of these general arguments and potentially given ACE a whole new range of realism. At the base of these trends are found two present-day major scientific breakthroughs: the steady shift of psychology towards a hard science due to the advances of neuropsychology, and the progress of artificial intelligence and more specifically machine learning due to increasing computational power and big data. These two have also found common fields of study in the form of computational neuroscience, and human-computer interaction, among others. We outline here the main lines of a computational research study of collective economic behavior via Agent-Based Models (ABM) or Multi-Agent System (MAS), where each agent would be endowed with specific cognitive and behavioral biases known to the field of neuroeconomics, and at the same time autonomously implement rational quantitative financial strategies updated by machine learning. We postulate that such ABMs would offer a whole new range of realism.
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中文摘要：
基于Agent的计算经济学（ACE）领域对金融经济学的研究历史产生了广泛的影响。尽管ACE领域因其接近物理学和化学等成功方法而受到自然科学研究人员的欢迎，但它也因缺乏经验主义而受到部分社会科学界的批评。然而，最近的趋势改变了这些一般论点的权重，并可能赋予ACE一个全新的现实主义范围。在这些趋势的基础上，我们发现了当今两个重大的科学突破：神经心理学的进步导致心理学向硬科学的稳步转变，以及计算能力和大数据不断增强导致的人工智能和更具体的机器学习的进展。这两个领域还以计算神经科学和人机交互等形式找到了共同的研究领域。我们在这里概述了通过基于代理的模型（ABM）或多代理系统（MAS）对集体经济行为进行计算研究的主线，其中每个代理都会被赋予神经经济学领域已知的特定认知和行为偏差，同时自主实施由机器学习更新的理性定量财务策略。我们假设，这种反弹道导弹将提供一种全新的现实主义。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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