Design Principles for Contribution Systems

The theory of contribution systems has recently been proposed by Rennie and Potts [1, 2] as a general analytic framework to explain detailed ethnographic observations on a range of permissionless organizations (see [3, 4]) through a combination of New Institutional Economics and the evolutionary game theory lens of contribution goods [5]. Rennie and Potts theorize that a contribution system is a new type of economic institution, made possible by new technologies (including blockchains and AI), that represents an evolution of the commons to enable computation over a new institutionally legible object—namely a contribution. Individuals make contributions (of resources, time and effort) toward a common joint project, and software turns those actions into computable objects that form the basis for subsequent claims of value as the project develops. A contribution system is the institutional mechanism by which distributed and permissionless contributions are processed into interobjective value.Please check and confirm if the authors and their respective affiliations have been correctly identified. Amend if necessary."?>

Contribution systems are a new class of economic institution that has evolved from the commons, but is also composed of properties of other economic institutions such as markets, networks, and hierarchies. It is a novel and still experimental institutional innovation. The significance of contribution systems is that they seem well adapted to digital production at internet scale, and especially where a particular problem to be solved involves joint production.

A theory of contribution systems has an additional purpose beyond explaining empirical observations (i.e. its scientific contribution over a range of early prototypes); it also provides a foundation for innovation and design improvements on next generation contribution systems. The challenge is how to design better contribution systems, which we see as a challenge of institutional design. This paper is a first attempt to develop a framework for design principles for contribution systems. Our goal is to articulate principles that address the unique challenges of contribution systems—a category of decentralized institutions that structure participation, recognition, and value creation in digital economies.

We adopt an abductive approach grounded in ethnographic and case study data, generating provisional principles from observed institutional regularities, following Ostrom’s logic of theorizing from empirical design patterns. The core theoretical insight guiding this work is that contribution goods serve a similar function to common-pool resources (CPRs) in Ostrom’s [6] Institutional Analysis and Design (IAD) framework: they define the governance challenge. Just as CPRs are difficult to exclude people from but require governance to prevent depletion [7], contribution goods require governance to sustain participation and prevent under-contribution. The governance challenges they pose are distinct: whereas commons governance focuses on preventing overuse (with positive-sum participation a necessary element of this), contribution systems must ensure ongoing contributions, participation-based exclusion, and dynamic value recognition over time. In this sense, contribution systems are not simply digital commons, they are machine-readable institutions designed to anchor contributions in ways that persist across time and scale. Inspired by the Ostrom framework of design principles for governing the commons (see [7, p. 90]), this paper seeks to develop design principles for contribution systems.

Unlike traditional institutions—such as firms, states, or markets—that rely on contracts, hierarchical enforcement, or pricing mechanisms to structure participation, contribution systems create continuous, decentralized records of contributions that endure beyond any single moment of exchange. The substantive novelty here is the creation of a new institutional object—a contribution, and a mechanism to which that object is legible—a contribution system. This means that rather than being time-bound to a contract, employment period, or immediate transaction, a contribution in these systems can accumulate value, recognition, or influence over extended timeframes. Contributions do not merely exist in the present; they persist and recompute, resurfacing as dependencies are activated in future contexts. This futural dimension positions contribution systems as a new class of institution, capable of structuring long-term cooperation in decentralized settings.

Additionally, machine-readability transforms governance itself. Because contributions can be tracked, computed, and re-weighted over time, they enable automated decision-making, dynamic incentives, and AI-assisted recognition of value in ways that traditional institutions cannot. This programmability allows for new forms of automated governance, real-time recalibration of incentives, and scalable contribution tracking, expanding the ways in which decentralized cooperation can be structured. Our intention here is to explore how contribution systems function as future institutions, structured around a set of core governance principles that emerge from both empirical cases and theoretical insights into contribution goods.

Contribution systems are emerging as a significant mechanism for coordinating value creation in distributed communities. A contribution system is generally defined as a system that recognizes, measures, and may reward contributions made by individuals or machines to a collective endeavour, even when participants are largely strangers. Unlike firms (which rely on hierarchical control) or markets (which rely on price signals), contribution systems use computational metrics and social feedback to attribute value to objects representing contributions. This allows them to incentivize activities that would otherwise remain untraced and unrewarded in a pure market setting.

The significance of contribution systems lies in their potential to coordinate resources and knowledge that are distributed across society, without requiring a centralized company or clear property rights. Early examples have existed in limited forms (for instance, social and technical protocols for academic citations), but they have become fundamental within blockchain protocols and decentralized autonomous organizations (DAOs). As we have argued elsewhere, Ethereum itself is a contribution system, where validators confirm or reject contributions (blocks) and are rewarded or penalized accordingly (Rennie [8, 9] (under review)). Contribution systems create value by computing relationships between contributions and upstream outcomes over a complex forward graph of dependencies, rather than by assigning prices to outputs from an accounting of exchange relations. In doing so, they can bring to light productive activities (such as open-source code maintenance or environmental stewardship) that markets often undervalue. A contribution system therefore builds from the activity of the commons, generating new forms of valued output (reputation, tokens, shared knowledge) that may help sustain communities and protocols.

Institutions are defined in New Institutional Economics as the “rules of the game in a society, or more formally, are the humanly designed constraints that shape human interaction” (North [10], p. 3). These refer to rules such as contracts, hierarchical enforcement, or pricing mechanisms that structure interaction and participation, and it is usually not even mentioned that this happens in the present. Institutions are persistent and legitimate rules from the past. However, we suggest that contribution systems are a “future institution”. The way they coordinate and constrain human interaction is that they move actions in the present (contributions) into the future and compute them back, as value. Institutions persist and are selected for under economic evolution because they provide long-term structures, in the form of social technologies, for coordination, value recognition, and decision-making. Contribution systems are still institutions in the “rules of the game” sense, but they are fundamentally “futural” in their aspect. Contributions are valued by what they add to the future, rather than market and hierarchy mechanisms that value in the present (e.g. exchange value, or cost value). Contribution systems, though emerging from digital and decentralized contexts, are not just tools for managing interactions but foundational mechanisms for anchoring contributions in ways that persist across time and scale.

Contribution systems work by creating continuous, decentralized records of contributions that endure beyond any single moment of exchange. This means that rather than being time-bound to a contract, employment period, or immediate transaction, a contribution in these systems can accumulate value, recognition, or influence over extended timeframes. The ability to track, recompute, and resurface contributions long after they are made introduces a new way of organizing human cooperation, one where individual and collective efforts become legible over long time scales rather than being ephemeral or forgotten when the immediate context shifts.

This temporal (futural) dimension—the ability to make contributions visible and relevant across different moments in time through the creation of a new institutional object, a contribution record—is what positions contribution systems as a future institution. They do not merely record present activity; they create infrastructures that bind past, present, and future contributions into an evolving system of value recognition. They are what we elsewhere call “hyperinstitutions” (extending on Timothy Morton’s “hyperobjects” [11]).

This is a significant departure from conventional institutional frameworks, where contributions are often constrained to finite windows of recognition (such as a wage). Instead, a contribution made today may gain recognition later when its dependencies are activated in another context, much as Deep Funding’s dependency graph surfaces foundational Ethereum code contributions. Moreover, because contributions become machine-readable, they can be scaled, programmed, and recombined in ways that produce different outcomes than the rules and monitoring systems of traditional commons. This programmability allows for automated governance, dynamic incentive structures, and AI-assisted recognition of contributions, expanding the ways in which decentralized cooperation can be structured. This ability to compute and contextualize contributions dynamically over time suggests they could become powerful governance mechanisms for long-term collective endeavours.

The term contribution goods, introduced by Terence Kealey and Martin Ricketts [5], describes goods—often knowledge-based—that are non-rivalrous (one person’s use doesn’t reduce availability to others) yet partly excludable in an unusual way. Unlike private goods (excludable via prices) or club goods (excludable via membership fees), contribution goods are effectively excludable through participation: one must contribute or have certain capabilities to fully benefit. The example they use is scientific knowledge: once discoveries are published, they are in principle open to all (no one’s use diminishes another’s), but only those who become scientists and contribute to the field can truly understand and utilize those discoveries. Outsiders may enjoy some spillover benefits, but the most valuable insights remain “clubbed” among contributors with the requisite expertise. This soft form of exclusion is not enforced by a paywall or patent (leaving aside the problems of scholarly publishing), but by the inherent cognitive/skill barrier—effectively an earned membership instead of a bought one.

By requiring contribution as the “ticket” to full benefits, contribution systems invert the usual incentive problems of public goods. In a pure public good, anyone can benefit regardless of whether they helped produce it, leading to classic free-rider incentives (why pay if you can enjoy it for free?). Contribution goods change this calculus. Because key benefits (like deep knowledge, skill, reputation) accrue mainly to those who participate in creating or understanding the good, non-contributors are naturally left behind. In essence, would-be free-riders exclude themselves by not contributing. The outcome is similar to a club good—a subset (the contributors) enjoys most of the benefits—but without formal exclusion mechanisms. This concept was developed to better explain knowledge-driven collective projects (science, open-source, etc.) that did not fit neatly into standard categories. For Kealey and Ricketts [5], it provides a new lens for examining how innovation communities organize and why they succeed or fail.

Because of these properties—and despite being used for so-called retroactive public goods funding [12]—contribution goods challenge the standard public/private/club good classification. Traditional economic models often treat collective innovation or knowledge production as a public goods problem modelled by a Prisoner’s Dilemma: everyone would be better off if all contribute, but each individual is tempted to shirk and free-ride on others’ efforts. Science and open knowledge projects, however, appear to defy this trap. Kealey and Ricketts [5] argue that science is not a Prisoner’s Dilemma at all, but a coordination game. The reason is that spillover benefits in science are asymmetrically greater for contributors than for outsiders. A scientist who contributes to the knowledge pool gains early access to new findings, know-how, and reputation that a non-contributing observer does not. This tilts the payoff structure so that contributing becomes the best response when others are contributing. In game-theoretic terms, the “game of science” (p. 1014) shifts from a prisoner’s dilemma to one of pure coordination. If enough peers are working at the research frontier, an individual’s optimal choice is to join and contribute, so as not to be left behind. Conversely, if nobody is contributing yet, there is little to gain by being the lone contributor—hence the need for a critical mass to kick start the endeavour.

This leads to a crucial insight: the key collective action problem in contribution systems is not ongoing free-riding, but achieving critical mass. Early on, a contribution-based project faces a coordination challenge—how to attract a minimum viable group of contributors so that contributions become worthwhile for each other. Kealey and Ricketts call this initial core the “visible college”, distinguishing it from the broader “invisible college” (p. 1015) of peripheral participants. Once a sufficient base of active contributors is established, each member has strong incentives to continue contributing, since they directly share in the fruits of the joint effort. In effect, contribution systems offer a built-in selective incentive for participation: contributors simply get more utility or value from the good than do non-contributors. Once the project is self-sustaining, members have much less incentive to free-ride in the usual sense, because opting out means losing access to significant benefits. The result is a more optimistic outlook for collective innovation: under the right conditions, individuals can coordinate to produce knowledge, software, or other shared goods without the situation devolving into a defection-heavy dilemma. The incentives are structured such that cooperation (contribution) begets more cooperation, as success breeds success.

The contribution good concept also refines our understanding of knowledge commons—resources like scientific knowledge, open data, or shared digital content that are managed by communities [13]. Ostrom’s work on commons emphasized how communities can self-govern shared resources with the right norms and institutions. Knowledge commons scholars have noted that scientific communities function as self-governing commons, often relying on informal norms of sharing and credit rather than market or state control [14]. The contribution lens adds that these commons often have an intrinsic gating mechanism: one must enter the community to reap full benefits. This naturally encourages continued contribution, as seen in open collaboration projects.

Over almost thirty years Elinor Ostrom gathered thousands of empirical studies of successful and unsuccessful governance of common-pool resources and realized that despite the immense diversity of commons governance systems, it is both possible and necessary to identify generalizable patterns that underlie successful self-governance. She called these high-level theoretical summaries “design principles” (see [15]).

The diversity of regularized social behaviour that we observe at multiple scales is constructed […] from universal components organized in many layers. In other words, whenever interdependent individuals are thought to be acting in an organized fashion, several layers of universal components create the structure that affects their behaviour and the outcomes they achieve. [6, p. 6]

She observed that scholars and practitioners were often lost in case-specific details, making it hard to learn broader lessons. To overcome this, Ostrom asked whether we can dig below the immense diversity of social settings to find common building blocks of human cooperation. Her answer was yes—she believed there are underlying principles of organization shared by effective commons institutions, even if the surface rules differ from one community to another. Ostrom justified the search for general principles as a way to develop better theory and policy—by identifying common patterns, we can create a shared framework to understand how fallible humans nonetheless achieve and sustain self-governance in complex, varied environments. In her view, communities managing resources from forests to fisheries are not utterly unique; they often rely on analogous solutions (e.g. defining user boundaries or monitoring resource use) that can be distilled into core principles. Ostrom explicitly posited that a set of universal components or “grammar” of institutions exists beneath the particularities of real-world cases. This conviction laid the groundwork for her formulation of general design principles applicable to commons governance everywhere. At the same time, she acknowledged this was a conjecture open to challenge—her approach was to propose common principles and then test them against diverse evidence.

In Governing the Commons [7], Ostrom distilled insights from a “meta-analysis of a large number of existing case studies” [16, p. 641] to propose several core principles that successful commons institutions tend to exhibit. These came to be summarized as eight design principles (e.g. clearly defined user and resource boundaries, congruence between local conditions and rules, participatory collective-choice arrangements; effective monitoring of use; graduated sanctions for rule-breakers; cheap conflict-resolution forums; minimal recognition of the community’s right to self-organize; and for larger systems, nested enterprises). Ostrom emphasized that these principles were empirical patterns, not mere theories—each was derived from multiple real-world cases of commons that had persisted or thrived over decades or centuries. For instance, in virtually all the enduring commons she studied, the user community had a clear sense of who had rights to the resource and what the boundaries were (Principle 1), in contrast to open-access situations that led to overuse. The repetition of such features across cultures—from Japanese farmer cooperatives to Swiss villagers—gave Ostrom confidence that these were generalizable “best practices” in commons governance. She defined a “design principle” as an “element or condition that helps to account for the success” [7, p. 90] of institutions in sustaining a resource over the long term. Notably, her empirical research was not limited to observational field studies. She also drew on history (e.g. colonial era records of communal resource management) and conducted laboratory experiments on collective action. The convergence of evidence from these varied methods reinforced the design principles.

The role of the design principles, therefore, is not to dictate rigid templates for institutions, but to provide adaptable guidelines that communities can use in crafting their own rules (one of Ostrom’s design principles itself is that rules should be congruent with local needs and conditions, underscoring that there is no one-size-fits-all approach to governing a commons). The design principles we outline below likewise function as a diagnostic checklist or heuristic—a set of critical factors to consider—rather than a statutory code. They are intended to guide users, developers, and researchers in asking the right questions.

Our approach to understanding contribution systems began with empirical research, specifically an ethnographic study of SourceCred [3], supplemented by case studies of other decentralized contribution networks. Through this work, we identified recurring governance challenges and incentive structures that did not fully align with existing economic categories, such as public goods or club goods. During the SourceCred ethnography we realized that Kealey and Ricketts’ [5] theory of contribution goods offered a useful framing for what we had already observed: that these systems function not by enforcing strict excludability, but by structuring participation in ways that make contributions the mechanism for accessing benefits. Rennie incorporated Kealey and Ricketts’ contribution goods framework as a way to refine and interpret her findings.

In the principles presented below, contribution goods serve a similar function to common-pool resources in Ostrom’s work, in that they define the core governance challenge of the system. Just as Ostrom identified design principles to sustain CPRs, we identify governance principles to ensure contribution systems remain viable and continue to generate value.

Drawing on Kealey and Ricketts’ work on contribution goods, alongside empirical research from Ethereum, SourceCred, Regen Network, and Deep Funding, we suggest the following set of principles for governing contribution systems. These principles ensure that contribution systems remain effective, fair, and self-sustaining.

Ostrom’s confidence in general principles was rooted in extensive empirical research. She and colleagues amassed a remarkable body of evidence on how real communities manage common-pool resources (see, e.g. [18, 19]). These studies were drawn from fieldwork, archival records, and collaborations with other researchers, producing a rich comparative dataset. Crucially, the cases included both successes and failures, allowing Ostrom to identify which institutional arrangements tended to sustain the resource and community over time. By comparing these diverse cases, Ostrom looked for shared factors present in the enduring, successful regimes and often absent in the failed ones. As we have only a small body of empirical work to date, the draft principles outlined above will require refinement and testing.

How such empirical research should proceed is a key concern of our current work. Studying contribution systems empirically presents new challenges for researchers, as these systems are often distributed, technical, and rapidly changing. We are exploring Actor-Network Theory (ANT) as it is useful for mapping how systems recognize, structure, and sustain value [20]. This approach helps reveal value as an emergent property of networks that include both human and non-human actors. In the context of contribution systems, the relevant actors include not only the contributors (people) but also algorithms, data structures, sensors, tokens, and even natural entities (in the case of systems like Regen Network). According to ANT, each of these entities can be seen as an actant that influences the state of the network. Value is not a static attribute, but is continuously co-constructed through interactions among the actors. A contribution system essentially formalizes these interactions into a “valuemeter”—a concept borrowed from Latour and Lépinay’s [21] essay on the work of Gabriel Tarde to describe devices that materialize collective value.

The task of empirical research is to reveal how meaning (or value) is inscribed in material forms and how those inscriptions stabilize social order. In contribution systems, the inscriptions are things like commits in a repository, transactions on a blockchain, or entries in a cred ledger—these are the traces or footprints of contributions. The system aggregates and interprets these traces, effectively translating subjective appreciations into objective data. In ANT terms, the value of the contribution is enacted by the network: it comes into being when a chain of actors acknowledges and incorporates that contribution. While traditional ethnographic methods—participant observation, interviews, qualitative coding—remain valuable, they do not always transfer easily to the types of field sites where contribution systems are observable (discord servers, governance forums), or to the technical dimensions (smart contracts, etc.). To address this, we have been developing tools for investigating contribution systems. This approach blends immersive qualitative research with computational tools, including custom software like Telescope, knowledge management systems like Obsidian (see [4, 22]).

Contribution systems represent a fundamental shift in institutional design, offering an alternative to firms, markets, and traditional commons governance. Unlike conventional institutions that rely on excludability through property rights or state enforcement, contribution systems introduce participation-based exclusion, where access to value is determined by contribution rather than ownership. This inversion of traditional governance logics demands new design principles—ones that ensure high-value contributors remain engaged, contributions are dynamically valued, and governance structures adapt over time.

In this paper, we have proposed draft principles for contribution systems that address the unique incentive structures, valuation mechanisms, and scalability challenges these systems present. These have been developed from our deep observations of SourceCred, and initial observations of Protocol Guild, Regen Network, and Deep Funding. The governance principles developed here emphasize the need to attract critical mass, track and reward dependencies, and structure adaptive, modular governance frameworks that can evolve alongside the systems they support.

However, this is only a starting point. As contribution systems continue to develop, there is a need for further empirical research to test these governance models, refine valuation mechanisms, and explore how AI and automation will shape future contributions. By developing a robust institutional theory of contribution systems, we can better understand how decentralized communities coordinate and sustain long-term collaboration. As these systems continue to evolve, they may become a primary governance mechanism for structuring cooperation and managing shared knowledge—in other words, future institutions.