2025-06-12 –, 7 - Side Stage
Open knowledge has diverse forms — including open-source software, scientific research, and collaborative data commons — that are all built upon intricate webs of dependencies. As vividly demonstrated in cascading OSS failures caused by overlooked maintenance of dependencies (e.g., the npm left-pad incident), sustainable open knowledge production requires ongoing care and investment in its foundational contributions. This talk explores "knowledge dependency graphs" as a conceptual tool, generalizing from software dependency graphs, to better understand public goods coordination problems across diverse open knowledge domains. By examining how decentralized dependency funding protocols have met these challenges within software ecosystems, we explore their potential, and limitations, as part of an iterative approach toward sustainably supporting critical dependencies throughout the broader landscape of open knowledge.
While information wants to be free, knowledge may have other agendas: high-quality open knowledge production requires foundational cultivation, validation, and maintenance work that faces complex coordination problems analogous to those in OSS.
This talk explores how dependency graphs have been used in decentralized funding solutions in OSS ecosystems, and how that might fruitfully extend to broader domains of cultural production such as open science, data commons, and open-source AI development.
We propose that "knowledge dependency graphs" can render legible the complex webs of contributions underlying various forms of open knowledge, as well as enable scalable mitigation of their systemic risks.
We'll discuss:
- How dependency networks can identify transitive vulnerabilities across different knowledge domains
- The potential for decentralized protocols plus off-chain algorithms to enable more granular, equitable, and efficient allocation of funding to knowledge dependencies
- The unique attribution challenges facing each domain in that effort
We aim through this talk to foster dialogue about incentivizing knowledge validation work (e.g. replication research), attributing impact in open knowledge, and fortifying infrastructure for intellectual and creative commons.
Currently head of data at Drips Network, Warren previously led data science and analytics engineering teams at SoundCloud and worked as a research partner at Seed Club Ventures, focusing on decentralized science and AI. Warren's other work has included developmental cognitive neuroscience research at Boston Children's Hospital, on-chain analysis of DAO governance dynamics, and contributions to scientific research evaluation and infrastructure development at PsyDAO.