Many-to-Many - Field Guide with learnings, models, insights and tools

Resource type
Authors/contributors
Title
Many-to-Many - Field Guide with learnings, models, insights and tools
Abstract
A living library of practical tools, frameworks, and case studies designed to support practitioners in complex collaborations. Many-to-Many System -aims to support collaborations tackling complex, entangled challenges where disrupting norms and values, ownership, and power is essential. Solving today’s complex, interconnected problems requires what we term “complex collaborations” - bringing together many diverse groups (public, private, civic) with many new perspectives, including future generations and the natural world. While many collaborations are already doing great work, we believe that finding better ways to support how they are structured and organised them could unlock more effective, system-level change. The Many-to-Many System is focussed on unlocking the governance, organising, legal, and learning structures of complex collaborations to enable many resources, not just money, but also knowledge and relationships to flow more freely, and to foster many ways of working that embrace diverse value exchange. For each and everyone of us, our fundamental understandings of the world are influenced by ‘deep codes,’ often invisibly embedded within our creations, frameworks, and rules. The Many-to-Many System explored how these codes shape collaboration and governance, aiming to understand if they could be reimagined and how those within complex collaborators themselves can embed them into their collaboration’s infrastructures. More intentional and visible shifting of deep codes for governance and organising could help collaborations to better align with their systemic missions and offer approaches for rethinking core concepts like value, power, risk, and ownership. The Many-to-Many System distills two years of learning, prototyping, research, and practice. Here we offer a blend of elements: a core framework, practical insights and learnings, illustrative models for complex topics, and tools developed along the way. Our aspiration is to provide a range of resources that can help others embed the Many-to-Many deep code shifts into their own complex collaboration work.
Institution
Dark Matter Lab
Date
2025
Accessed
23/04/2026, 09:00
Short Title
Many-to-Many
Language
en
Citation
Zucker, M., & Dhami., A. (2025). Many-to-Many - Field Guide with learnings, models, insights and tools. Dark Matter Lab. https://www.manytomany.systems/discover-the-system