SMPDb: a Database of Collective Sense-Making Protocols

Team member names

  • Artem Zhiganov (research lead)
  • Konstantin Kornev (full stack developer)

Short summary of your improvement idea

As the complexity of our world continues to increase, there is a growing need for more efficient, accountable, and impactful governance that can keep pace with these changes. We are witnessing a number of experiments to address this need by enabling more participatory and distributed decision-making: DAOs, network states, citizen assemblies, etc. However, the protocols of collective sense-making are often poorly understood and implemented, limiting the efficiency of governance in general.

We propose to establish a standard approach for documenting and evaluating such protocols, based on the artifacts like Process Cards and Run Reports. By enabling researchers and practitioners to share the best sense-making practices in a formalized way (using a pattern language), this system has the potential to unlock the full potential of DAOs as a means of tackling complex global challenges and shaping a more participatory and equitable future.

Answers to the following questions

  1. What is the existing target protocol you are hoping to improve or enhance?

The target protocol is the collective sense-making processes within DAOs, focusing on the documentation and evaluation of these processes, using the artifacts and taxonomy proposed by Aviv Ovadya.

  1. What is the core idea or insight about potential improvement you want to pursue?

The core idea is to develop a flexible system that enables prototyping, documentation and evaluation of collective sense-making protocols. By providing a standardized approach to these processes, we aim to accelerate innovation, foster accountability, and support the adoption of best sense-making practices in the ecosystem.

  1. What is your discovery methodology for investigating the current state of the target protocol?

The current state of collective sense-making in DAOs will be investigated through a combination of expert interviews, Wardley Mapping of sense-making frameworks and tools, as well as a comprehensive literature review of existing research. We will also conduct interviews and surveys with governance practitioners and facilitators to understand their current challenges and needs in terms of process documentation and evaluation. As a result, we will identify best practices and key elements to include in the documentation and the design of our database.

  1. In what form will you prototype your improvement idea?

The improvement idea will be prototyped through the development of a web-based platform that enables the creation, sharing, and analysis of Process Cards and Run Reports for collective sense-making processes.

  • Run Report: A roughly standardized document providing details about a particular Process Run, including the results of any evaluations of that process.
  • Process Card: A roughly standardized document providing details about a process and its appropriateness for different goals, including summary results of evaluations (roughly analogous to a model card in machine learning).

The platform will include editor (with templates), as well as tools for visualizing and comparing process properties across different organisations and use cases. Some process properties are designed (e.g. # of participants invited) and others must be measured (e.g., speed, resources consumed), or approximated via measurement (e.g. trust in a democratic process, quality of outputs). The platform can incorporate LLM to extract insights and identify patterns across multiple artifacts. The prototype will be developed using open-source technologies and will be shared with experts and potential users for feedback and iterative refinement.

  1. How will you field-test your improvement idea?

The prototype will be field-tested through a pilot program with a diverse set of partner DAOs. Participants will be trained on how to create Process Cards and Run Reports using the platform, and will be encouraged to document their collective sense-making processes over a period of 3-6 months. We will collect feedback from participants through surveys, interviews, and usage data, and will use this information to refine the platform and identify areas for further improvement.

  1. Who will be able to judge the quality of your output? Ideally name a few suitable judges.
  • Aviv Ovadya, the originator of the Process Cards and Run Reports concept

  • Experts in collective intelligence and democratic innovation, such as Antoine Vergne, Beth Simone Noveck, Audrey Tang and Geoff Mulgan

  • Experienced facilitators who have implemented collective sense-making in online as well as offline contexts (e.g. citizen assemblies), such as Andy Paice

  • Dave Snowden, perhaps the leading expert in sense-making

  • Daniel Schmachtenberger

  • Rafa

  1. How will you publish and evangelize your improvement idea?

The system will be published as an open-source project on GitHub, along with detailed documentation and case studies from the pilot program. We will also publish a whitepaper detailing the system’s design, implementation, and evaluation results, and present the system at conferences and workshops focused on collective intelligence, democratic innovation, and decentralized governance. We will actively engage with relevant communities and think tanks, such as Metagov, DAO Research Collective and the Center for Humane Technology, to promote adoption and gather further insights.

  1. What is the success vision for your idea?

The success vision for this project is to establish a standardized approach to capturing and analyzing the key properties and outcomes of sense-making protocols, and to create a library of best practices with good navigation. In the long term, we envision this system becoming a critical infrastructure for promoting more efficient protocols, as well as enabling a permissionless audit / certification system. By providing a shared language and documentation templates, it will enable researchers, funders, and governance practitioners to learn from each other, avoid duplication of efforts, and identify the most effective approaches for their specific goals. Ultimately, it will accelerate democratic innovation, ensuring that our sense-making capabilities keep pace with the complexity of our environment. The system’s success will be measured by the number of organizations adopting it, the quality and diversity of the documented processes, and the demonstrable improvements in the competence, alignment, and robustness of decision systems over time.

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