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Briefing Document - Contribution Systems - A New Framework for Open and User Innovation

Briefing Document: Contribution Systems - A New Framework for Open and User Innovation​

Source: Rennie, E., & Potts, J. (2024). "Contribution systems: A new framework for open and user innovation." SSRN.

Date: Aug 2025

Summary: This document provides a detailed briefing on "Contribution Systems," a novel institutional framework for open and user innovation, as conceptualised by Rennie and Potts. The core idea is to address the perennial challenges of persistence and scale in decentralised, user-driven innovation by leveraging advanced digital technologies. Contribution systems transform voluntary individual actions into "durable, computable objects" that can be recorded, evaluated, and rewarded over time, thereby creating a "hard" institutional memory for the "commons." This shift enables new mechanisms for value creation, coordination, and governance, offering a powerful alternative to traditional firm-based or market-based innovation models.

Main Themes and Key Ideas:​

1. The "Epistemic Shift" from Schumpeterian to von Hippel Innovation:

  • Traditional View: Innovation primarily originated from "R&D labs of large corporations, elite universities, or the strategic budgets of national governments" (Schumpeterian model).
  • New View (von Hippel Innovation): Innovation is increasingly recognised as a "decentralised and distributed activity, rooted in the knowledge and initiative of users themselves." Users, particularly "lead users," possess unique, context-specific knowledge and often develop solutions to their own problems, sharing them freely and collaboratively.
  • Economic Logic: This paradigm centres on the "efficient use of local knowledge," making innovation "faster, cheaper (often free!), and highly functional."
  • The Dilemma: While users are originators, "the institutional systems that surround them are not always well-suited to support their work," leading to many innovations remaining "local in their use, short-lived or failing to realise potential social value precisely because the institutional infrastructure is not built for scale."

2. Introduction to Contribution Systems:

  • Definition: Contribution systems are "a structured digital environment that records, evaluates, and rewards individual inputs made toward a shared objective." They are a "technology upgrade of an ancient institution" (e.g., guilds, cooperatives) enabled by new digital tools.
  • Core Innovation: The "Contribution" as an Object: The central innovation is transforming a voluntary "action" (e.g., writing code, moderating a discussion) into a "durable digital record of that input that is institutionally legible and can be computed on." This "computable object" enters "institutional memory" and can be "queried, searched, verified, recombined and re-valued over time."
  • Retroactive Value: Unlike industrial innovation where value is assigned early, in contribution systems, "value can emerge retrospectively, as contributions are referenced, reused, or built upon by others, and as dependencies are revealed." This introduces "a form of institutional memory into open and user innovation."
  • Coordination without Central Control: These systems allow individuals to "contribute without formal employment, to be rewarded without prices, and to build shared value without relying on top-down control." They function as an "invisible robotic arm, coordinating distributed actors without central control."

3. Operational Properties and Digital Infrastructure:

  • Digital Technologies: Contribution systems leverage a "stack of technologies" including the internet, social media, blockchains, smart contracts, and platform-based coordination tools.
  • Key Operational Properties:Participation Graphs: Track relationships between contributors, tasks, and outputs to model influence.
  • Blended Evaluation: Combines "peer evaluation with algorithmic logic."
  • Smart Contract Incentives: Programmatic rewards (tokens, access rights, governance privileges) triggered by recognition thresholds.
  • Future-Oriented: Contributors act with "optionality," speculating on future value, with rewards assigned "retroactively based on downstream significance."
  • Contribution History for Governance: Access to governance or resources tied to contribution history.
  • AI Augmentation: Used for clustering work, suggesting attributions, and summarising activity.
  • Examples: Public blockchains (Bitcoin, Ethereum), SourceCred (open-source software), DAOs using Coordinape, and Regenerative Finance (ReFi) for ecological stewardship.

4. Computable Commons: From "Soft" to "Hard" Institutions:

  • Soft Institutions: Rely on "informal rules, shared norms, and socially maintained agreements" (e.g., cultural practices, tacit knowledge, reputation). Effective locally but limited in "scale, auditability, and durability."
  • Hard Institutions: Built on "formal, digital systems that enforce rules through computation" (e.g., blockchains, smart contracts, algorithmic governance). They define boundaries, record contributions, and allocate rewards using "persistent, verifiable, and executable" data structures.
  • The Shift: Contribution systems exemplify this transition, transforming contributions from "events embedded in human interaction" (soft) to "discrete, durable records" (hard). This enables "institutional computation" and "formalized memory" independent of social roles.
  • Benefits of Hard Commons: Improved "processing performance and its scale" for the commons, enabling new mechanisms for "value allocation" (e.g., recursive funding models, retroactive public goods financing).

5. Addressing Weaknesses in Open and User Innovation:

  • Legibility of Informal Work: Contributions are recorded as "structured data," creating a "searchable, auditable history of participation," which can then be linked to "formal consequences such as eligibility for rewards, governance rights, or downstream reuse."
  • Automated Incentive Mechanisms: Algorithms and community input allocate rewards (tokens, influence) dynamically, collectively, and often "retroactive (such that they reward past contributions)." This "aligns incentives with long-term impact rather than short-term effort."
  • Coordination at Scale: Contributions as "modular, linkable objects" enable work to be "versioned, branched, reused, or improved without central planning or permissioning." Immutable and persistent records on blockchains allow for "safe downstream investment." This supports "cumulative innovation" and the scaling of decentralised efforts.

6. Dimensions of Improvement for Open and User Innovation:

  • Memory: Creates "structured, machine-readable memory" that enables "retrospective evaluation" and supports "innovation trajectories that may unfold deep into the future."
  • Complexity: Formalises contribution objects and their dependencies within "structured graphs," making "complexity legible and navigable," and supporting "emergent specialization" and "resilience."
  • Value: Introduces an "alternative form of economic value calculation" where value is determined through "interobjective dependency and future utility" rather than market prices. This "post hoc and recursive" model opens doors to new funding mechanisms.
  • Scale: Provides the "institutional substrate" for open systems to grow without losing coherence. Enables "open participation without sacrificing accountability" and facilitates "self-financing models" based on actual contributions and impact.

7. Design Principles for Governance:

  • Evolution of Ostrom's Principles: While building on Elinor Ostrom's (1990) principles for managing common-pool resources, contribution systems incorporate a focus on "computability, scalability, and temporal valuation."
  • Rennie's (2023) Nine Design Principles:
    1. High-value contributors anchor long-term system health.
    2. Active contributors receive disproportionate rewards.
    3. Systems exist to sustain contributor networks, not merely to fund outputs.
    4. Dependencies must be recorded and rewarded.
    5. Value is dynamic; it evolves post-contribution.
    6. Systems must be legible and explainable.
    7. Governance should be adaptive and decentralized.
    8. Critical mass matters more than preventing free-riding.
    9. Machine-readable data enables scalability and automation.
  • Key Differences from Ostrom: Monitoring becomes "algorithmic," sanctions potentially "automated or embedded in smart contracts," and institutional memory is "formalized, machine-readable memory" rather than oral or informal.

8. Conclusion: A New Institutional Infrastructure:

  • Third Mode of Coordination: Contribution systems offer a new way to coordinate distributed work in open, decentralised environments, distinct from hierarchical firms or price-based markets. They operate "without contracts or centralized control."
  • Addressing the "Commons" Gap: They address the struggle of traditional commons to handle "complex coordination," by "embedding memory, valuation and tools for designing incentives into the infrastructure itself."
  • Formalising Informal Assets: Similar to de Soto's (2000) argument about formalising informal assets, contribution systems "make the raw elements of voluntary, distributed labour visible and actionable through computational consensus, forming the infrastructure through which participation can be valued and rewarded at scale."
  • Future Impact: They offer "alternatives to extractive platform capitalism and rigid institutional gatekeeping," enabling "experimental governance and collaborative resource allocation" and generating "new forms of capital from within the commons." The authors suggest that "the more we can move governance to machines, the better we can scale the commons, including and especially for innovation."

Areas for future research​