CAREER: Automated scientific discovery and the philosophical problem of natural kinds

这是一个教师早期职业发展（Career）奖，是美国国家科学基金会（NSF）最负盛名的奖项，旨在支持通过杰出的研究、优秀的教育以及在其组织使命的背景下将教育和研究结合起来，体现教师学者角色的初级教师。该奖项支持一个综合研究和教育项目，该项目解决了一个基本的科学问题：在与某些感兴趣的现象相关的无数经验量中，为了成功地发现现象背后的规律或规律，应该将注意力集中在哪些量上？只有极少数人能够从少数特定的事实中准确地归纳出许多没有证据的事实，然而在他们的工作过程中，科学家们有效地选择了支持归纳的变量。科学家们能够做到这一点既令人着迷又令人困惑。该项目将通过构建一系列计算机算法来阐明和测试解决这一难题的新方法，这些算法将自动执行为自主科学发现服务的变量选择过程。当这些算法应用于当前科学环境中的实际问题时，归纳的成功将作为这些算法背后的理论的切实验证。所产生的自动发现算法将被用来向一代哲学和科学研究生介绍物理计算和形式认识论之间的深刻联系。一个经常性的暑期学校将训练研究生基本的编程和形式化方法，并参与自动化发现系统的开发。本项目将自然类型的哲学问题与人工智能中自动发现的计算问题联系起来。它测试了一种新的方法，即动态自然种类理论，即动态种类理论，通过从该理论的规范内容中推导发现算法，然后将这些算法应用于现实世界的现象。当这些算法应用于当前科学环境中的真实问题时，归纳的成功将作为哲学理论的切实验证。更戏剧性的是，这些发现算法有可能产生不止一种同样有效但不一致的现象分类。这些替代方案的存在在关于科学实在论的争论中起着核心作用。在哲学之外，将发现算法应用于生态学、进化、宏基因组学、代谢组学和系统生物学等领域的开放性问题，有可能提出这些领域中以前未设想的基本本体理论。特别是，这些算法将应用于基于主体的进化动力学模型，以搜索种群水平的规律，并应用于公开可用的长期生态数据，以搜索标准生态类别之外的稳定动态类型。

General Audience Summary This is a Faculty Early Career Development (CAREER) award, the NSF's most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations. This award supports an integrated research and education project that addresses a fundamental scientific question: Out of countless number of empirical quantities related to some phenomenon of interest, to which quantities should attention be directed in order to successfully discover the regularities or laws behind the phenomenon? Only a special few facilitate accurate generalization from a few particular facts to a great many that are not in evidence, and yet in the course of their work scientists efficiently choose variables that support generalization. That scientists are able to do this is both fascinating and perplexing. This project will clarify and test a new approach to solving this puzzle by constructing a series of computer algorithms that automatically carry out a process of variable choice in the service of autonomous scientific discovery. The inductive success of these algorithms when applied to genuine problems in current scientific settings will serve as tangible validation of the theory underlying these algorithms. The automated discovery algorithms produced will be leveraged to introduce a generation of graduate students in philosophy and science to the deep connections between physical computing and formal epistemology. A recurring summer school will train graduate students in basic programming and formal methods, with hands on development of automated discovery systems.Technical Summary This project connects the philosophical problem of natural kinds with computational problems of automated discovery in artificial intelligence. It tests a new approach, a dynamical natural kinds theory, denoted the Dynamical Kinds Theory, by deriving discovery algorithms from that theory's normative content and then applying these algorithms to real-world phenomena. The inductive success of these algorithms when applied to genuine problems in current scientific settings will serve as tangible validation of the philosophical theory. More dramatically, these discovery algorithms have the potential to produce more than one equally effective but inconsistent classification of phenomena into kinds. The existence of such alternatives plays a central role in debates over scientific realism. Outside of philosophy, the application of the discovery algorithms to open problems in areas of ecology, evolution, metagenomics, metabolomics, and systems biology has the potential to suggest previously unconceived theories of the fundamental ontology in these fields. In particular, the algorithms will be applied to agent-based models of evolutionary dynamics to search for population-level laws, and to publicly available long-term ecological data to search for stable dynamical kinds outside the standard set of ecological categories.