"When the world we are trying to explain and improve…is not well described by a simple model, we must continue to improve our frameworks and theories so as to be able to understand complexity and not simply reject it." — Elinor Ostrom

Some years from now, blockchain-based network economies will develop a rich operating tapestry radically different from the business models we know today. 

When thinking about networks, systems, or protocols, I often think about the Kardashev Scale — which is used to measure a civilization's ability to harness and utilise energy. In a similar vein, we can assess a network by its ability to capture and distribute economic value effectively.

Value Capture is a network's ability to generate revenue from its operations and convert user interactions into economic benefits.

Value Distribution describes how effectively a network can allocate captured value among its stakeholders — which typically includes investors, labour contributions, end users, and perhaps the protocol itself.

When assessing various networks, we look at the following attributes:

• Adaptability: How does it evolve with project needs and market conditions?

• Transparency: Do changes to emissions and distributions follow clear and predictable mechanisms?

• Value-alignment: Do emissions correspond to demonstrable value creation?

• Inclusivity: Do distributions serve all stakeholder groups equitably?

Keeping in line with the Kardashev Scale, I have used the above criteria to loosely define three types of network economies based on what we have seen so far in the evolution of blockchain technology.

Type I: Fixed Mechanic Networks

First-generation blockchain networks and tokens operate on skeuomorphic principles: predetermined emission schedules mimic precious ore mining or the economics of scarce goods, while staking and voting mechanisms mirror traditional public voting systems or corporate governance.

Bitcoin exemplifies this with its absolute rules: a 21 million supply cap, known mining rewards, fixed halving schedules, and Nakamoto consensus — a system that works as intended as a store of value. 

Though groundbreaking, such systems face significant constraints – they are limited in their ability to adapt to changing market conditions and face issues such as economic capture.

This is most clearly illustrated in Curve Finance's veLocking and other early ERC-20 tokens built on the store-of-value narrative. Curve's emission schedule effectively hindered price discovery and paved the way for Convex to "exploit" the protocol, demonstrating how a system's behaviour can be exposed to external actors optimising the rules. [1]

Type II: Governable Parameter Networks

Type II networks are distinguished by adjustable parameter values. These on-chain systems can respond to oracles (Chainlink, UMA's Optimistic Oracle) or algorithmic information (AMM/s). These properties create reflexive systems that can adapt to changing market conditions through governance protocols.

The economic design of these networks often relies on layering game theory to align stakeholder incentives. The battleground of stablecoins and lending protocols provides great insight into how these products use updatable parameters to hedge risk and ensure protocol operation.

Aave, one of Ethereum's earliest on-chain lending protocols, demonstrated this effectiveness by securing $21B of customer funds through periods of extreme volatility. In order to do so, the underlying protocol had to be constantly monitored and refined. [2]

In contrast, systems that rely on off-chain components while claiming to be protocols have often fallen prey to the Principal Agent problem, in which there is conflict in priorities between a group and the representative authorised to act on their behalf. One example is Celsius, which was presented as a protocol and yet owed $4.7 billion to users listed as unsecured creditors when filing their Chapter 11 bankruptcy. [3]

The key takeaway is that genuine on-chain systems provided actual protection through algorithmic controls and distributed governance and were less susceptible to social dynamics and failures caused by a concentration of power.

Type III: Autonomous networks

Type III networks represent the theoretical evolution towards fully autonomous systems that operate with minimal human intervention, are highly contextual, and have a large baud rate in terms of symbols transmitted across systems.

While real-world examples have not yet been realised, these systems would likely be characterised by:

Autonomous Parameter Optimization: Multiple AI agents would continuously optimise protocols, and with access to near-instant data aggregation, evolutionary algorithms would learn from the market and adapt accordingly.

Algorithmic Value Orchestration: Informed by predictive modelling and reward optimization, dynamic fee structures would self-adjust based on network utilisation, maximising long-term protocol sustainability.

Governance in a Dynamical System

Network economies are deeply complex and require flexibility to respond to existential threats while maintaining operational equilibrium. Governance plays a crucial role at each stage of a network's ability to operate.

The innate ability to govern a system provides evolutionary advantages needed to survive in the Dark Forest. The tension between governance flexibility and security manifests most clearly in how networks respond to their environment.

While Type I networks like Bitcoin prioritise security through rigid immutability, and Type II protocols like Aave demonstrate adaptability through parameter adjustments, neither fully resolve the flexibility-stability paradox.

Polycentric systems and the commons

While attempting to distil best practices, I discovered Nobel laureate Elinor Ostrom's incredible work on the commons. Though distinct from token economics, her empirical research effectively provides a roadmap to realising a Type III system.

A polycentric system is a form of governance where multiple independent decision-making centres operate with some degree of autonomy while still functioning as part of a coherent system.

Polycentric systems feature:

• Multiple centres of authority and decision-making that are formally independent

• Centres that interact and overlap in jurisdictions and responsibilities

• Significant autonomy within an overarching framework

• Coordination through various formal and informal mechanisms

Ostrom's Eight Principles

Based on research of over 800 cases worldwide, Ostrom's principles for managing commons are highly relevant to blockchain and cryptocurrency governance:

1. Clearly Defined Boundaries

2. Rules Adapted to Local Context

3. Participatory Decision-Making

4. Effective Monitoring

5. Graduated Sanctions

6. Accessible Conflict Resolution

7. Right to Organise

8. Nested Enterprises

If we are to believe that tokenized economies are the future, we must also recognise that governance technology is a critical component in these emerging systems.

Conclusion

The evolution of network economies from Type I to Type III systems represent more than just technological advancement — it reflects our growing understanding of how to create more resilient, adaptive, and equitable digital ecosystems. Bitcoin's fixed mechanics, Aave's parametric governance, and the theoretical potential of autonomous networks each contribute valuable lessons to this evolutionary story.

While there's significant investment in tokenomics and cryptocurrency infrastructure, we're underinvesting in what truly matters: governance systems. The fundamental challenge isn't creating new tokens – it's developing robust frameworks for collective decision-making and oversight. Venture capital's disproportionate focus on tokens over governance technology reflects a misalignment between short-term profit incentives and the long-term sustainability of decentralised systems. Without sophisticated governance mechanisms, even the most elegant token designs may ultimately fail to create lasting value.

Ostrom's work on polycentric systems and commons management provides a crucial bridge between traditional governance wisdom and the future of digital networks. Her principles, validated across hundreds of real-world cases, offer practical guidelines for addressing the core challenges in network governance: balancing security with flexibility, ensuring equitable value distribution, and maintaining system integrity while enabling evolution.

As we move toward more sophisticated network economies, success will likely come from synthesising these different approaches:

• The security-first mindset of Type I networks

• The adaptive capabilities of Type II systems

• The autonomous potential of Type III networks

• The empirical wisdom of polycentric governance

The future of network economies won't be determined by technological capabilities or memes, but by our ability to implement these systems in ways that serve all stakeholders while maintaining operational resilience. As networks continue to evolve, the integration of artificial intelligence, dynamic parameter optimization, and new governance structures will likely create forms of economic organisation that we're only beginning to imagine.

What's clear is that the path forward requires us to embrace complexity rather than shy away from it. Just as Ostrom suggested, our task is not to simplify these systems, but to develop better frameworks for understanding and managing them. The next generation of network economies will need to be as sophisticated as the challenges they aim to solve, while remaining accessible and beneficial to all participants.

References

• [1] Harrigan et al (2023) Emergent Outcomes of the veToken Model

• [2] https://www.tally.xyz/gov/aave/proposal/315 

• [3] Biswas, Soma; Huang, Vicky Ge (July 15, 2022). "Celsius Owes Users More Than $4.7 Billion". Wall Street Journal. 

• Cover image: Spiral Galaxy M33 (1980). https://area-books.com/en/heat

• https://www.paradigm.xyz/2020/08/ethereum-is-a-dark-forest