Original author: 1 a 35 e 1
Original translation: TechFlow
“When the world we seek to explain and improve cannot be neatly described by a simple model, we need to continually refine our theories and methods to better understand complexity, rather than simply denying it.” — Elinor Ostrom
In the coming years, the blockchain-based network economy will develop a complex and diverse operating model that will be very different from the traditional business models we are familiar with today.
When I study networks, systems, or protocols, I am often reminded of the Kardashev Scale, which is a measure of a civilization’s ability to harness and harness energy. Similarly, we can assess the efficiency of a network by its ability to capture and distribute economic value.
Value Capture refers to the networks ability to generate revenue through operational activities and convert user participation into economic benefits.
Value Distribution describes how the network effectively distributes these benefits to stakeholders, including investors, developers, labor contributors, end users, and even the protocol itself.
When evaluating different blockchain networks, we focus on the following key attributes:
Adaptability: Can the network flexibly adjust to changing project needs and market conditions?
Transparency: Are changes in benefits and distribution mechanisms clear and predictable?
Value-alignment: Does the distribution of benefits match the actual value created?
Inclusivity: Does the distribution of benefits cover all stakeholders fairly?
Based on the idea of the Kardashev scale, I tried to use the above criteria to categorize the three types of network economies that emerged during the evolution of blockchain technology.
Type I: Fixed Mechanic Networks
First-generation blockchain networks and tokens are often based on the “principle of skeuomorphism,” a design philosophy that mimics traditional economic models. For example, the pre-set token issuance schedule simulates the mining process of rare minerals or the economics of scarce commodities, while the staking and voting mechanisms draw on traditional public voting systems or corporate governance models.
Bitcoin is a typical example of this type, and its operating rules are highly deterministic: a supply cap of 21 million, fixed mining rewards and halving cycles, and a Nakamoto consensus based on Proof of Work. This system works well as a value storage tool.
Nevertheless, such systems also face significant limitations - they lack the ability to adapt to market changes and are prone to the economic capture problem, where the value of the network is excessively captured by specific stakeholders.
This problem is particularly evident in Curve Finances veLocking mechanism and other early ERC-20 tokens based on the value storage narrative. Curves fixed issuance schedule actually limits the markets judgment of the true value of the token and creates opportunities for external participants such as Convex to exploit the protocol rules, highlighting how the system mechanism can be affected by external optimizers.
Type II: Governable Parameter Networks
The notable feature of the second type of network is that its parameter values can be flexibly adjusted. These on-chain systems are able to respond dynamically through oracles (such as Chainlink, UMAs Optimistic Oracle) or algorithmic information (such as automated market makers AMM), thus forming an adaptive system that responds to changing market conditions through governance protocols.
The economic design of these networks often introduces multi-layer game theory mechanisms aimed at aligning the incentives of stakeholders. Competition in stablecoins and lending protocols provides us with important examples of products that hedge risks and ensure the stable operation of the protocol by dynamically adjusting parameters.
Take Aave as an example, one of the earliest on-chain lending protocols in the Ethereum ecosystem, which successfully protected $21 billion of user funds during extreme market volatility. To achieve this goal, the underlying mechanism of the protocol needs to be continuously monitored and optimized.
In contrast, systems that rely on off-chain components but claim to be “protocols” are often susceptible to the principal-agent problem, which refers to the tendency for agents to prioritize their own interests over the interests of the group as a whole. For example, Celsius was promoted as a decentralized protocol, but when it filed for bankruptcy, its users were owed $4.7 billion as unsecured creditors.
This shows that a true on-chain system provides stronger protection capabilities through algorithmic control and distributed governance, and is less susceptible to concentration of power or human decision-making errors.
Type III: Autonomous Networks
The third type of network represents the theoretical direction of the evolution of blockchain technology towards fully autonomous systems. These systems will operate with minimal human intervention, be highly adaptive to changes in the environment, and demonstrate extremely high efficiency in information transfer across systems.
Although there are no real examples yet, it is foreseeable that such systems may have the following characteristics:
Autonomous Parameter Optimization: Multiple AI agents will continuously optimize the protocol, learn from the market and dynamically adjust system parameters through real-time data aggregation and evolutionary algorithms.
Algorithmic Value Orchestration: Based on predictive models and reward optimization, a dynamic fee structure automatically adjusts based on network usage, thereby achieving long-term sustainability of the protocol.
Governance in a Dynamical System
The complexity of the blockchain network economy requires the system to be flexible enough to deal with potential existential threats while maintaining a balanced state of operation. In this process, the governance mechanism plays a vital role at every stage of the networks development.
The systems inherent governance capabilities provide it with a survival advantage in the dark forest environment. The dark forest usually refers to the highly competitive and threatening environment in the blockchain field. The tension between governance flexibility and security is most intuitively reflected in how the network responds to changes in the external environment.
The first type of network (such as Bitcoin) prioritizes security through strict immutability, while the second type of network (such as Aave) demonstrates stronger adaptability through parameter adjustment. However, neither of them has fully resolved the contradiction between flexibility and stability: excessive pursuit of flexibility may weaken security, while over-emphasizing stability may limit the systems adaptability.
Polycentric Systems and the Commons
While exploring best practices for blockchain governance, I discovered Nobel Prize winner Elinor Ostrom’s groundbreaking research on commons management. While her research is not exactly the same as token economics, her empirical work provides a clear roadmap for achieving Type III systems.
The so-called multi-center system is a governance model in which multiple independent decision-making centers have a certain degree of autonomy, but at the same time operate collaboratively as part of an overall system.
The main features of the multi-center system include:
There are multiple centers of authority and decision-making, and these centers are formally independent of each other;
There may be overlap and interaction between the centres in terms of jurisdiction and responsibilities;
Within a unified framework, each centre has significant autonomy;
Coordination is achieved through formal or informal mechanisms.
Ostroms Eight Principles
Ostrom summarized eight principles for commons management based on the study of more than 800 cases around the world. These principles are also important in the governance of blockchain and cryptocurrency:
Clear boundaries: clearly define the scope of resource use and users;
Rules adapted to local circumstances: Rules need to be adapted to local conditions;
Participatory decision-making: stakeholders jointly formulate rules;
Effective monitoring: ensuring that rules are followed;
Progressive sanctions: progressively escalating penalties for violations;
Accessible conflict resolution mechanisms: providing fair and efficient avenues for resolving disputes;
The right to organize: allowing community members to self-organize;
Nested enterprise: An organizational structure that contains multiple levels within a larger governance framework.
If we believe that the tokenized economy is the future, we must recognize that governance technology is key to the success of these emerging systems.
Conclusion
Despite the current massive investments in token economics and cryptocurrency infrastructure, we are underinvesting in the core area of governance systems. The real challenge is not in creating new tokens, but in building robust collective decision-making and oversight frameworks. Venture capital’s excessive focus on tokens reflects a misalignment between short-term profit incentives and the long-term sustainability of decentralized systems. Without complex and robust governance mechanisms, even the most sophisticated token designs will struggle to achieve lasting value.
The evolution of the network economy from the first to the third type of system is not only a technological advancement, but also our continuous exploration of how to build a more resilient, adaptable and fair digital ecosystem. Bitcoins fixed mechanism, Aaves parametric governance, and the theoretical potential of autonomous networks all provide valuable experience for this evolutionary process.
Ostrom’s research on polycentric systems and commons management builds an important bridge between traditional governance wisdom and the future of digital networks. Her principles, validated by hundreds of real-world cases, provide valuable guidance for addressing the core challenges of network governance: how to strike a balance between security and flexibility, ensure fair distribution of value, and promote evolution while maintaining system integrity.
As the network economy grows more complex, the key to success may lie in integrating the following different approaches:
The first type of network has a security first mentality: ensuring system security through fixed rules;
The second type of system has the ability to adapt: respond to changes by dynamically adjusting parameters;
The third type of network has the potential for autonomy: minimal human intervention through AI and algorithms;
The empirical wisdom of polycentric governance: achieving coordination and development through multi-level, polycentric governance structures.
The future of the network economy will not be determined by technological capabilities or popular culture, but by our ability to implement these systems in a way that serves all stakeholders while maintaining operational resilience. As networks continue to evolve, the convergence of artificial intelligence, dynamic parameter optimization, and new governance structures may create forms of economic organization that we do not yet fully understand.
To be sure, the path forward requires us to embrace complexity, rather than try to avoid it. As Ostrom suggests, our task is not to simplify these systems, but to develop better frameworks for understanding and managing them. The next generation of the network economy needs to be as complex as the problems it seeks to solve, while also being friendly and fair to all participants.
