英文标题：
《Enabling Scientific Crowds: The Theory of Enablers for Crowd-Based
  Scientific Investigation》
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作者：
Jorge Faleiro
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最新提交年份：
2018
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英文摘要：
  Evidence shows that in a significant number of cases the current methods of research do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of critical decisions at all levels, from personal investment choices to overreaching global policies, rely on some variation of try-and-error and are mostly non-scientific by definition. We lack transparency for procedures and evidence, proper explanation of market events, predictability on effects, or identification of causes. There is no clear demarcation of what is inherently scientific, and as a consequence, the line between fake and genuine is blurred. This paper presents highlights of the Theory of Enablers for Crowd-Based Scientific Investigation, or Theory of Enablers for short. The Theory of Enablers assumes the use of a next-generation investigative approach leveraging forces of human diversity, micro-specialized crowds, and proper computer-assisted control methods associated with accessibility, reproducibility, communication, and collaboration. This paper defines the set of very specific cognitive and non-cognitive enablers for crowd-based scientific investigation: methods of proof, large-scale collaboration, and a domain-specific computational representation. These enablers allow the application of procedures of structured scientific investigation powered by crowds, a collective brain in which neurons are human collaborators
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中文摘要：
证据表明，在许多情况下，当前的研究方法不允许重复和伪造科学调查程序。因此，从个人投资选择到过度的全球政策，所有层面上的大多数关键决策都依赖于尝试和错误的变化，从定义上讲，大多数决策都是不科学的。我们缺乏程序和证据的透明度、市场事件的正确解释、影响的可预测性或原因的识别。对于什么是天生的科学，没有明确的界限，因此，真假之间的界限变得模糊。本文介绍了基于人群的科学调查的促成因素理论，简称促成因素理论。使能因素理论假设使用下一代调查方法，利用人类多样性、微型专业人群的力量，以及与可访问性、再现性、沟通和协作相关的适当计算机辅助控制方法。本文定义了基于人群的科学调查的一组非常特定的认知和非认知促成因素：证明方法、大规模协作和特定领域的计算表示。这些使能技术允许应用由群体驱动的结构化科学调查程序，群体是一个集体大脑，其中神经元是人类的合作者
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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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