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

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

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.