标题： 金融代理模型的光明未来 显示英文标题

标题： A bright future for financial agent-based models

摘要： 金融和经济学研究的历史受到了基于代理的计算经济学（ACE）领域的广泛影响。 虽然同时因其接近物理学和化学的成功方法而受到自然科学研究人员的欢迎，但ACE领域也因缺乏实证性而受到部分社会科学界的批评。 然而，最近的趋势已经改变了这些普遍论点的权重，并可能为ACE带来了全新的现实性范围。 这些趋势的基础是当今两大科学突破：由于神经心理学的进步，心理学正逐步转向硬科学，以及由于计算能力的提高和大数据的发展，人工智能特别是机器学习取得了进展。 这两个领域也在计算神经科学、人机交互等领域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）进行集体经济行为计算研究的主要方向，其中每个代理将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金融策略。 我们假设这样的ABM将提供全新的现实性范围。 显示英文摘要

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.