Research Proposal: Matter Patterns - Fund What Matters, Autonomously

Research & Strategic Intelligence
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Research Proposal: Matter Patterns

Problem Statement

Grants are spread across many different ecosystems, making it difficult for funders to identify which grantees align most with their values and desired impact. Grantees, in turn, lack a unified mechanism to attest to their theory of change and effectively map their work against measurable outcomes. Existing grant allocation mechanisms require extensive time and effort to:

  • Track current grant rounds and programs
  • Research grantees and assess alignment
  • Make funding decisions with confidence

A more efficient, interoperable, and intelligent system is needed to streamline this process for both funders and grantees.

Research Objectives

This research seeks to assess the feasibility of a tool called “Matter Patterns,” which will leverage AI-assisted preference mapping, grant interoperability, and autonomous grant allocation. Specifically, the research will explore:

  • AI-assisted preference mapping to help funders articulate their values and funding priorities
  • Interoperability between existing grant mechanisms such as Grants Stack, RetroPGF, Giveth, and other funding infrastructures
  • The potential for autonomous grant making and allocation based on funder-defined parameters
  • The ability for funders to set more granular criteria for autonomous grant allocations, leveraging onchain project track records to make data-driven decisions
  • A universal tool for grantees to describe their theory of change, values, and outcomes, ensuring a standardized and transparent framework for grant applications

The goal is to develop an interoperable tool that enables funders to seamlessly discover and support aligned projects while reducing administrative overhead.

Mechanism Overview

Matter Patterns functions as a multi-layered system that integrates AI-driven preference mapping, structured data interoperability, and automated allocation mechanisms. The key components of the mechanism include:

  1. User Preference Mapping:
    • A chatbot-driven interface guides funders through a structured questionnaire to determine their funding priorities.
    • Preferences are captured in a high-dimensional vector store database, allowing for precise alignment with potential grantees.
  2. Grantee Theory of Change Mapping:
    • Grantees use a standardized framework to document their mission, values, and desired outcomes.
    • This structured data is stored in an interoperable format, making it easier for funders to discover and assess projects.
  3. Grant Interoperability Engine:
    • A universal grant data schema ensures that funding opportunities from various ecosystems (e.g., Grants Stack, Giveth) are indexed and searchable.
    • Metadata from onchain grant programs is structured to allow seamless comparison across funding mechanisms.
  4. Onchain Track Record Assessment:
    • Grantees’ past funding history, project impact metrics, and performance data are aggregated from blockchain records.
    • Funders can set weightings for different track record factors, such as milestone completion rates or governance participation.
  5. Automated and Assisted Allocation:
    • Funders define allocation parameters, including budget per epoch, oversight levels, and preferred grant pools.
    • The system offers two modes:
      • Autonomous Allocation: Automatically distributes funds based on funder preferences and real-time project performance.
      • Decision-Support Mode: Provides ranked funding recommendations for funders to manually review and approve.
  6. Budget and Oversight Management:
    • Funders set constraints on how much they wish to allocate per funding cycle.
    • Optional oversight mechanisms allow for manual intervention in case of unexpected developments.

Technical Considerations

To ensure feasibility and scalability, the research will evaluate:

  • Variable and composable automation: Allowing funders to choose between autonomous allocation or decision support.
  • High granularity of preference mapping: Structuring funder preferences in a vector store database to enable nuanced decision-making.
  • Interoperable grant standards: Developing a universal grant data schema to ensure cross-ecosystem compatibility.
  • Structured data architecture: Defining a JSON-based structure to represent funder preferences and grant program data for seamless interoperability.
  • Onchain project track record integration: Enabling funders to assess project performance using historical blockchain data to refine funding decisions.
  • Grantee theory of change framework: Establishing a universal format for grantees to clearly articulate their mission, values, and expected outcomes, enhancing discoverability and alignment.

Expected Deliverables

The research will result in the following key outputs:

  1. Feasibility Study: Assessing the technical, economic, and social viability of Matter Patterns.
  2. Universal Grant Data Schema: A proposed standard for structuring grant-related data to enable interoperability across ecosystems.
  3. Request for Proposal (RFP): A document outlining the technical requirements for implementing Matter Patterns, serving as a guide for potential development teams.