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Original translation: Deep Tide TechFlow
"When the world we are trying to explain and improve cannot be clearly described with a simple model, we need to continuously improve our theories and methods to better understand complexity rather than simply deny it." — Elinor Ostrom
In the coming years, blockchain-based network economics will develop a complex and diverse operating model that will be radically different from the traditional business models we are familiar with today.
When studying networks, systems, or protocols, I often think of the Kardashev Scale, which is a measure of a civilization's capability to utilize and control energy. Similarly, we can assess the operational efficiency of networks based on their ability to capture and distribute economic value.
Value Capture refers to the network's ability to generate income through operational activities and transform user participation into economic benefits.
Value Distribution describes how networks effectively allocate these gains to stakeholders, including investors, developers, labor contributors, end users, and even the protocol itself.
When evaluating different blockchain networks, we primarily focus on the following key attributes:
Adaptability: Can the network flexibly adjust according to project needs and market conditions?
Transparency: Are the changes in revenue and distribution mechanisms clear and predictable?
Value-alignment: Does revenue distribution match actual value creation?
Inclusivity: Is revenue distribution fairly covering all stakeholders?
Based on the Kardashev Scale concept, I attempt to classify the three types of network economics that have emerged during the evolution of blockchain technology using the above criteria.
Type I: Fixed Mechanic Networks
First-generation blockchain networks and tokens are often based on the 'physicalization principle', which mimics the design concepts of traditional economic models. For example, preset token issuance plans simulate the mining process of rare ores or the economics of scarce goods, while staking and voting mechanisms draw on traditional public voting systems or corporate governance models.
Bitcoin is a typical representative of this type, with operational rules characterized by high determinism: a supply cap of 21 million, fixed mining rewards and halving periods, and a Nakamoto consensus based on proof of work. This system functions well as a store of value.
Nevertheless, these systems also face significant limitations—they lack adaptability to market changes and are prone to 'economic capture', where network value is excessively concentrated among specific stakeholders.
This issue is particularly evident in Curve Finance's veLocking mechanism and other early ERC-20 tokens based on value storage narratives. Curve's fixed issuance plan in fact restricts the market's judgment of the token's true value and creates opportunities for external participants like Convex to 'exploit' protocol rules, highlighting how system mechanisms are influenced by external optimizers.
Type II: Governable Parameter Networks
A significant characteristic of Type II networks is that their parameter values can be flexibly adjusted. These on-chain systems can dynamically respond through oracles (e.g., Chainlink, UMA's Optimistic Oracle) or algorithmic information (e.g., automated market makers AMM), forming adaptive systems that respond to changing market conditions through governance protocols.
The economic design of these networks typically introduces multi-layer game theory mechanisms aimed at aligning the incentives of stakeholders. The competition between stablecoins and lending protocols provides us with important case studies, as these products hedge risks and ensure stable operation of the protocol through dynamic parameter adjustments.
For example, Aave, one of the earliest on-chain lending protocols in the Ethereum ecosystem, successfully protected $21 billion of user funds during extreme market volatility. To achieve this, the underlying mechanisms of the protocol require continuous monitoring and optimization.
In contrast, systems that rely on off-chain components but claim to be 'protocols' are often vulnerable to principal-agent issues. This issue refers to agents potentially prioritizing their own interests over the collective interest of the group. For example, Celsius was marketed as a decentralized protocol, but at the time of filing for bankruptcy, its users faced unsecured debts totaling $4.7 billion.
Therefore, it is evident that true on-chain systems provide stronger protective capabilities through algorithmic control and distributed governance, and are less susceptible to power concentration or human decision-making errors.
Type III: Autonomous Networks
Type III networks represent the theoretical direction of blockchain technology evolving into fully autonomous systems. These systems will operate with minimal human intervention, capable of highly adaptive adjustments according to environmental changes, and demonstrate high efficiency in cross-system information transmission.
Although there are currently no real-world examples, it is foreseeable that these systems may possess the following characteristics:
Autonomous Parameter Optimization: Multiple AI agents continuously optimize the protocol by learning from the market and dynamically adjusting system parameters through instant data aggregation and evolutionary algorithms.
Algorithmic Value Orchestration: Based on predictive models and reward optimization, dynamic fee structures can automatically adjust according to network usage, thus achieving the long-term sustainability of the protocol.
Governance in a Dynamical System
The complexity of blockchain network economics requires systems to possess enough flexibility to cope with potential survival threats while maintaining operational balance. In this process, governance mechanisms play a crucial role in every stage of the network's development.
The system's inherent governance capabilities provide it with a survival advantage in a 'dark forest' environment. The 'dark forest' typically refers to a competitive and threatening environment in the blockchain space. The tension between the flexibility and security of governance is most intuitively reflected in how the network responds to changes in the external environment.
Type I networks (like Bitcoin) prioritize security through strict immutability, while Type II networks (like Aave) demonstrate greater adaptability through parameter adjustments. However, both fail to fully resolve the contradiction between flexibility and stability: an excessive pursuit of flexibility may undermine security, while an overemphasis on stability may limit the system's adaptability.
Polycentric Systems and the Commons
In exploring best practices for blockchain governance, I discovered Nobel laureate Elinor Ostrom's pioneering research on commons management. Although her research is not entirely the same as token economics, her empirical studies provide a clear roadmap for achieving Type III systems.
A multi-center system is a governance model in which multiple independent decision-making centers have a degree of autonomy while simultaneously collaborating as part of a whole system.
The main characteristics of multi-center systems include:
There are multiple authorities and decision-making centers, and these centers are formally independent of each other;
There may be overlaps and interactions between the jurisdictions and responsibilities of each center;
Within a unified framework, each center has significant autonomy;
Coordination is achieved through formal or informal mechanisms.
Ostrom's Eight Principles
Based on her research on over 800 global cases, Ostrom summarized eight principles regarding commons management. These principles are also significant in the governance of blockchain and cryptocurrencies:
Clear boundaries: Clearly define the scope of resource use and users;
Rules adapted to local environments: Rules must be context-specific;
Participatory decision-making: Stakeholders co-create rules;
Effective monitoring: Ensure compliance with rules;
Gradual sanctions: Gradually escalating penalties for violations;
Accessible conflict resolution mechanisms: Provide fair and efficient dispute resolution pathways;
Organizational rights: Allow community members to self-organize;
Nested enterprises: Incorporate multiple layers of organizational structure within a larger governance framework.
If we believe that tokenized economies are the trend of the future, we must recognize that governance technology is key to whether these emerging systems can succeed.
Conclusion
Despite significant investments in token economics and cryptocurrency infrastructure, we are under-invested in the core area of governance systems. The real challenge is not to create new tokens, but to build strong collective decision-making and oversight frameworks. The venture capital 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 intricate token designs will struggle to achieve lasting value.
The evolution of network economics from Type I to Type III systems is not just a technological advancement, but also a continuous exploration of how to build more resilient, adaptive, and equitable digital ecosystems. The fixed mechanisms of Bitcoin, the parametric governance of Aave, and the theoretical potential of autonomous networks all provide valuable insights for this evolutionary process.
Ostrom's research on multi-center systems and commons management bridges traditional governance wisdom with the future of digital networks. Her principles, validated through hundreds of real cases, provide valuable guidance for addressing the core challenges of network governance: how to balance security and flexibility, ensure fair value distribution, and promote evolution while maintaining system integrity.
As network economics evolves towards greater complexity, the key to success may lie in integrating the following different approaches:
The 'security-first' mindset of Type I networks: Ensuring system security through fixed rules;
The adaptability of Type II systems: Responding to changes through dynamic parameter adjustments;
The Autonomous Potential of Type III Networks: Minimizing Human Intervention through AI and Algorithms;
Empirical Wisdom of Multi-Center Governance: Achieving Coordination and Development through Multi-Level, Multi-Center Governance Structures.
The future of network economics will not be determined by technological capabilities or popular culture, but by whether we can implement these systems in a way that serves all stakeholders while maintaining operational resilience. As networks continue to evolve, the integration of AI, dynamic parameter optimization, and new governance structures may create forms of economic organization that we do not yet fully understand.
It is certain that the path forward requires us to embrace complexity rather than trying to avoid it. As Ostrom suggested, our task is not to simplify these systems but to develop better frameworks to understand and manage them. The next generation of network economics needs to be as complex as the problems it seeks to solve while also remaining friendly and fair to all participants.
