Entropy in Science of Science

Abstract

This study investigates entropy's potential for analyzing scientific research patterns across disciplines. Originating from thermodynamics, entropy now measures uncertainty and diversity in information systems. We examine Shannon Entropy, Entropy Weight Method, Maximum Entropy Principle and structural entropy applications in scientific collaboration, knowledge networks, and research evaluation. Through publication analysis and collaboration network studies, entropy-based approaches demonstrate effectiveness in mapping interdisciplinary knowledge integration, with higher entropy values correlating to increased knowledge diversity in citation networks. Structural entropy analysis reveals dynamic collaboration patterns affecting research productivity. Results indicate entropy metrics offer objective tools for assessing research quality, optimizing team structures, and informing science policy decisions. These quantitative methods enable systematic tracking of knowledge evolution and resource allocation efficiency, providing actionable insights for researchers and policymakers managing complex scientific ecosystems