To be FAIR: Theory Specification Needs an Update

Innovations in open science and meta-science have focused on rigorous *theory testing*,yet methods for specifying, sharing, and iteratively improving theories remain underdeveloped.To address these limitations, we introduce *FAIR theory*:A standard for specifying theories as Findable, Accessible, Interoperable, and Reusable information artifacts.FAIR theories are Findable in well-established archives,Accessible in practical terms and in terms of their ability to be understood,Interoperable for specific purposes, e.g., to guide control variable selection,andReusable so that they can be iteratively improved through collaborative efforts.This paper adapts the FAIR principles for theory,reflects on the FAIRness of contemporary theoretical practices in psychology,introduces a workflow for FAIRifying theory,and explores FAIR theories’ potential impact in terms of reducing research waste,enabling meta-research on the structure and development of theories,and incorporating theory into reproducible research workflows – from hypothesis generation to simulation studies.We make use of well-established open science infrastructure, includingGit for version control, GitHub for collaboration, and Zenodo for archival and search indexing.By applying the principles and infrastructure that have already revolutionized sharing of data and publications to theory, we establish a sustainable, transparent, and collaborative approach to theory development.FAIR theory equips scholars with a standard for systematically specifying and refining theories, bridging a critical gap in open research practices and supporting the renewed interest in theory development in psychology and beyond.FAIR theory provides a structured, cumulative framework for theory development, increasing efficiency and potentially accelerating the pace of cumulative knowledge acquisition