Investigates how DAOs (Decentralized Autonomous Organizations) sustain cooperation using reputation systems and smart consensus to avoid governance traps in decentralized, algorithm-driven communities.

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PhD

Ilya Lavrov

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Rijksuniversiteit Groningen

Supervisor

Prof. dr. Rafael Wittek

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Rijksuniversiteit Groningen

Supervisor

dr. Francesca Giardini

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Rijksuniversiteit Groningen

Supervisor

Prof. dr. Lisa Herzog

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Rijksuniversiteit Groningen

• Cooperation
• Public good game

• Organisation science
• Philosophy
• Platforms
• Sociology

• Work

• External Shocks

• Reshaping organizational forms

The Theory of Governance Traps (Wittek, 2022) is used as a point of departure and extended to the context of Blockchain Governance in DAOs. Several theoretical analyses have pointed to the endogenous downward spirals challenging the viability of DAOs and related organizational forms. For example, some scholars argue that DAOs face the same “paradox of flexibility and structure” that threatens the viability of what has been labeled Fluid Organizations (Schirmacher et al., 2021; Schreyögg & Sydow, 2010). Similarly, analyses of algorithmic decision making and control point to the contested nature of the related practices (Kellogg et al., 2022) and highlight the inherent problems of ambiguity intolerance and pressures on social decision making practices (Herzog, 2021). A governance trap reflects a self-reinforcing process in which an institutional arrangement that is intended to elicit cooperation, also triggers behaviors that indirectly undermine it. An example are performance contingent incentives in organizations, like bonuses. Whereas such incentives are powerful in eliciting the type of behavior that yields the reward, they may also lead to the neglect of other behaviors that are not rewarded, but nevertheless important for overall performance, like not taking excessive risks (Becker & Huselid, 1992). Building on insights from research on goal framing and joint production motivation (Lindenberg & Foss, 2011), this theory argues that independently of its success in getting cooperation going in the short run, any governance structure also bears the seeds for its own decay in the middle and long run. This tendency towards endogenous decay has its roots in the brittle nature of human motivation when it comes to sustaining contributions to collective goods (Lindenberg, 2014). As recent experimental research has shown, maintaining a collective good is more difficult than creating a new one (Gächter et al., 2017). One implication is that governance structures geared towards keeping joint production motivation salient will be more successful in preventing the emergence of governance traps. DAO platforms – the digital infrastructures that potential DAO founders can use to configure their own DAOs, like Colony or Aragon – are well aware of the many potential threats that may lead to the (early) dissolution of a DAO. This is why they equipped their platforms with a series of tools that allow founders to implement and calibrate a variety of institutional safeguards to prevent and mitigate governance failures (Baninemeh et al., 2021). Reputation and consensus systems are two particularly important elements of the broader set of governance instruments used by DAOs (e.g. Rea et al., 2020). First, most DAOs provide the opportunity to track and reward member contributions to the collective good, like a specific project. Often, such contributions can be made visible through an individual reputation score, and thereby contribute to the reputation of the DAO member. This reputation can be expressed in the DAO’s own token, and may therefore also have monetary value for the member, or it may translate into voting or control power within the DAO. The opportunity to build up reputations therefore can be a powerful incentive for individuals to invest intelligent effort into joint endeavors. But reputation systems come with their own challenges. For example, how to avoid that members who have accumulated high reputation scores in the past also keep contributing in the present? DAOs therefore differ with regard to their approach to reputation based governance. Particularly noteworthy is the solution that the Colony platform has developed. Here, the reputation algorithm is programmed such that a member’s reputation decays through time (e.g. at an hourly rate), in order to incentivize members to keep contributing (Rea et al., 2020). Second, most DAOs have some form of collective decision making process in place. Such processes are used to vote, for example, on budget allocations for specific projects, or on strategic issues. Also here DAOs differ in the way they design the related consensus and voting procedures. Again, the Colony platform’s approach is pioneering in its reliance on what it calls lazy consensus, i.e. “decentralized decisions without voting”. This principle is based on the idea that voting is only necessary if there is disagreement, thereby avoiding one of the potential shortcomings of participatory decision making. A DAO is sustainable if it succeeds in eliciting and maintaining joint production efforts that create internal and social value - also if circumstances for this joint production deteriorate. Pre-programmed reputation decay and lazy consensus are just two of a vast array of blockchain based governance practices designed to boost the sustainability of DAOs through a radical implementation of organizational practices geared to increase accountability, objectivity and participation. But like any form of algorithmic control (Herzog, 2021; Kellogg et al., 2020), also blockchain governance creates a whole array of new challenges, some of which may actually undermine these very objectives. This project investigates under which conditions DAOs succeed to prevent and mitigate such governance traps.

This project employs a multi-phase, mixed-method research design to investigate how different governance mechanisms—particularly reputation-based systems and smart consensus—affect the sustainability and cooperative capacity of Decentralized Autonomous Organizations (DAOs). The first phase involves a comprehensive inventory of existing DAOs to map and categorize their governance features. Building on the typology developed in Phase 1, a controlled behavioral lab experiment will be designed to simulate DAO-like environments and test how specific governance features influence individual behavior and group cooperation. The third phase adopts a longitudinal, participatory digital ethnography approach to study the inner workings of selected DAOs. Grounded in the principles of Participatory Digital Ethnography (Rennie et al., 2022), this phase involves deep engagement with DAO communities through digital observation, interviews, focus groups, and content analysis of internal communications.