NOTE:

The goal of this research is not to build a long-lasting framework but to understand and categorize decentralized infrastructure as a system that lets its users contribute and build on top of it.

I have tried to explain the parts of the 3-wave model with various examples, including MakerDAO, Uniswap, BitTorrent, AAVE, Polygon & Solana.

But it is all early stages, and I will iterate on this framework throughout the year upon having detailed conversations with builders, investors, and contributors.

If you want to stay updated with this framework and my future research posts, do consider subscribing below

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The 3-wave model of evolution of complex systems is so interesting to me, and I've been looking for parallels between this model and Web3.

Especially if you are a founder, this should be interesting.

For context, the 3-wave model is based on the idea that complex systems undergo three phases of evolution:

• Self-organization: process of the spontaneous formation of patterns and structures from interacting agents.

• Entropy: measure of disorder and randomness in a system.

• Development: process of increasing the complexity and functionality of a system.

In Web3, I'd like to add another trait everyone should resonate with:

• Chaos: the state of unpredictability and sensitivity to initial conditions in a complex system that may lead to new forms of self-organization or the destruction of existing patterns.

Some questions I’ve asked myself to include Chaos into the 3-wave model, especially if I want to build this framework for evaluating decentralized infrastructure:

How to define the criteria for chaos in a complex system? What are some indicators or measures of chaos?

I would define chaos as the state where the system’s behavior is highly sensitive to small changes in initial conditions or parameters.

For example, a dApp that relies on a consensus mechanism or a voting system could exhibit chaotic behavior if a small fraction of nodes or voters change their preferences or strategies.

How to design and deploy smart contracts that account for chaos in a decentralized application? What are some benefits and risks of introducing chaos in a network?

I would design and deploy smart contracts that use random or probabilistic functions to introduce chaos in a decentralized application.

For example, a dApp that implements a lottery or a gambling game could use smart contracts that generate random numbers or outcomes to create chaos and uncertainty in the network.

How to monitor and analyze the effects of chaos on the system and its elements over time? What are some tools or methods for studying chaotic systems?

I would monitor and analyze the effects of chaos using different ways of looking at the system and its behavior. These ways can help me see when and how the system becomes chaotic and what patterns or shapes it makes from chaos.

For example,

• one way of looking at the system is to see how it changes when I change something, like the number of nodes or the speed of transactions.

• Another way of looking at the system is to see how fast or slow it reacts to small changes or differences, like a node going offline or a delayed transaction.

• A third way of looking at the system is to see how complicated or straightforward it seems when I zoom in or out, like a picture with many details or a shape that repeats itself.

If you were to build a decentralized app or an infrastructure, how would you go about it?

One possible way would be to use a smart contract to create dApps with programmable logic and rules. You then define the initial conditions and parameters of your dApp, such as the number of tokens, the distribution mechanism, the governance model, the functionality, etc. you also define some criteria or triggers for transitioning between the phases of evolution.

For example,

-- you can start your dApp in the self-organization phase, where users can join the network, interact with each other, and form clusters or communities based on their preferences or interests.

-- you then monitor the network activity and entropy level to determine when to move to the entropy phase. in this phase,

-- you introduce some randomness or uncertainty into the system, such as changing the token rewards, altering the network topology, or adding new features or challenges. This creates some disruption and diversity in the system, leading to new patterns and behaviors.

next,

-- you move to the development phase, where you increase the complexity and functionality of your dApp, such as adding more layers, modules, or services. You can also allow users to propose and vote on changes or improvements to the dApp, using a democratic or consensus-based mechanism.

Finally,

-- you move to the chaos phase, where you introduce some extreme randomness or uncertainty into the system, such as changing the network rules, altering the token supply, or adding new challenges or threats. This creates some disruption and diversity in the system, leading to new patterns and behaviors or the collapse of existing or unrewarding ones.

Then,

-- you return to the development phase, where you improve the functionality and complexity of the app.

The cycle repeats itself, growing more affluent and complex until you decide to stop. You give users the option to continue migrating the system or release the code or tokens to the mainnet.

In the above scenario, you are clearly following a deterministic approach. You can control every aspect of the system and integrate everything in line with predefined requirements.

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To evaluate decentralized startups using the 3-wave model of evolution of complex systems, you would need to assess how well they perform in each phase of evolution and how they transition between them.

You should compare them with other startups in the same domain or market and the existing centralized solutions (which I have later in this research).

A hypothetical framework that will help with using the 3-wave model for evaluation of Web3 startups could be as follows:

Let’s try to implement the 3-wave model of complex systems, including chaos with decentralized applications. I have brainstormed two different outlines and I’ll be trying out both.

Here are some possible steps:

1. Choose a domain or a problem that can be modeled as a complex system with decentralized applications. For example, a social network, a marketplace, or a governance system.

2. Identify the elements, interactions, and environment of the system. For example, the elements could be users, products, or votes; the interactions could be messages, transactions, or proposals; and the environment could be the network, the market, or the community.

3. Define the criteria for self-organization, entropy, development, and chaos for the system and its elements. For example,

   • Self-organization could be measured by the degree of clustering or modularity in the network;

   • Entropy could be measured by the level of diversity or uncertainty in the system;

   • Development could be measured by the rate of growth or innovation in the system; and

   • Chaos could be measured by the sensitivity or unpredictability of the system’s behavior.

4. Design and deploy smart contracts that encode the rules and incentives for the system and its elements to achieve self-organization, entropy, development, and chaos. For example, smart contracts enable users to join and leave the network freely; create and exchange products or services; propose and vote on policies or changes; and generate or experience random events or outcomes.

5. Monitor and analyze the behavior and evolution of the system and its elements over time. For example,

   • use data visualization tools or techniques to observe the patterns and structures that emerge from self-organization, entropy, development, and chaos;

   • use statistical tools or techniques to test hypotheses or measure correlations between variables;

   • use computational tools or techniques to simulate scenarios or optimize parameters.

OR

1. Identify the domain or market of the Web3 startup and its centralized and decentralized competitors.

2. Define the criteria or metrics for evaluating the performance and potential of the Web3 startup in each phase of evolution. For example, you could use network size, activity, diversity, resilience, innovation, efficiency, scalability, security, usability, profitability, etc.

3. Collect data on the Web3 startup and its competitors using various sources, such as web analytics, blockchain explorers, user feedback, market reports, etc.

4. Analyze the data using appropriate methods, such as descriptive statistics, network analysis, trend analysis, benchmarking, etc.

5. Evaluate the web3 startup in each phase of evolution using the criteria or metrics defined in step 2.

   1. For example, you could assess how well the Web3 startup self-organizes its network of users and nodes, how it manages entropy and randomness in its system, and how it develops its complexity and functionality over time.

6. Evaluate the web3 startup's transitions between the phases of evolution using the criteria or metrics defined in step 2.

   1. For example, you could assess how smoothly and effectively the web3 startup moves from self-organization to entropy to development and back again.

7. Compare the Web3 startup with its competitors in each phase of evolution and their transitions using the criteria or metrics defined in step 2.

   1. For example, you could compare how well the Web3 startup performs relative to other Web3 startups or centralized solutions regarding network size, activity, diversity, resilience, innovation, efficiency, scalability, security, usability, profitability, etc.

8. Synthesize the results and draw conclusions about the strengths and weaknesses of the Web3 startup and its competitors in each phase of evolution and their transitions.

   1. For example, you could identify what makes the web3 startup stand out or lag behind in terms of self-organization, entropy, or development; what are the opportunities or challenges for improving its performance or potential; what are the best practices or lessons learned from other web3 startups or centralized solutions; etc.

9. Communicate the results and conclusions using appropriate formats and channels, such as reports, presentations, dashboards, blogs, podcasts, etc.

Let’s Evaluate a few DAOs, DeFi protocols, and a few Networks

BitTorrent

BitTorrent is a peer-to-peer file-sharing network that allows users to download and upload files without relying on a central server.

Here is how BitTorrent could be modeled using the 3-wave model of complex systems, including chaos:

The system of interest is the BitTorrent network, and its elements are the users (peers) and the files (torrents). The interactions are the requests and responses for file pieces between peers. The environment is the internet and the bandwidth availability.

The criteria for

• self-organization is the degree of connectivity and cooperation among peers.

• entropy is the level of diversity and availability of torrents.

• development is the rate of growth and innovation of the network.

• chaos is the sensitivity and unpredictability of the network’s performance.

The smart contracts that encode the rules and incentives for the system and its elements are based on the BitTorrent protocol, which defines how peers discover, request, and exchange file pieces.

• The protocol uses a distributed hash table (DHT) to enable peers to discover each other and locate file pieces. The protocol also uses a tracker to coordinate the communication between peers and manage the swarm of peers sharing a file.

The protocol also implements a tit-for-tat strategy that rewards peers who upload more than they download and punishes peers who leech or cheat.

• The protocol uses a choking algorithm to determine which peers to upload file pieces to. The algorithm prioritizes peers who have uploaded more file pieces to the user in the past, and chokes or blocks peers who have uploaded less or nothing. This creates an incentive for peers to upload as much as they download, and discourages peers who leech or cheat by downloading without uploading.

The BitTorrent protocol introduce some randomness in choosing peers to connect with, but not for the sake of creating chaos or diversity. Rather, the randomness is intended to prevent starvation or unfairness in the network.

• The protocol uses a rarest-first algorithm to select which file pieces to request from peers, which ensures that all file pieces are equally distributed among the swarm. The protocol also uses an optimistic unchoking algorithm to occasionally unchoke a random peer, which allows new or slow peers to join the swarm and receive file pieces.

The behavior and evolution of the system and its elements can be monitored and analyzed using various tools and techniques, such as data visualization, network analysis, game theory, or agent-based modeling. These tools and techniques can help observe the patterns and structures that emerge from self-organization, entropy, development, and chaos; test hypotheses or measure correlations between variables; simulate scenarios or optimize parameters.

MakerDAO

MakerDAO is a decentralized autonomous organization that allows users to create and manage a stablecoin called DAI, which is pegged to the US dollar.

Here is how MakerDAO could be modeled using the 3-wave model of complex system including chaos:

The system of interest is the MakerDAO network, and its elements are the users (holders, borrowers, and governors) and the tokens (DAI and MKR). The interactions are the creation and redemption of DAI, the borrowing and repayment of DAI, and the voting and governance of MakerDAO. The environment is the cryptocurrency market and the global economy.

The criteria for

• self-organization is the degree of alignment and participation among users.

• entropy is the level of volatility and risk in the system.

• development is the rate of growth and innovation of the network. The criteria for

• chaos is the sensitivity and unpredictability of the system’s stability.

The smart contracts that encode the rules and incentives for the system and its elements are based on the Maker protocol, which defines how DAI is created, backed, and managed by collateral assets.

• The protocol uses smart contracts to lock up collateral assets, such as ETH, WBTC, USDC, etc., in exchange for DAI. The protocol also uses smart contracts to manage the solvency and stability of the system, such as liquidating undercollateralized positions, adjusting risk parameters, distributing fees, etc.

The protocol also implements a governance mechanism that allows MKR holders to vote on various parameters and policies that affect the system, such as interest rates, collateral types, oracles, etc.

• Maker protocol uses a governance mechanism to enable MKR holders to propose and vote on changes to the protocol or its parameters. These changes can include adding or removing collateral types, setting interest rates or stability fees, choosing oracle providers or price feeds, approving core units or budget distributions, etc. The governance mechanism uses a quadratic voting system that weights votes based on the square root of MKR staked.

The protocol also introduces some randomness in choosing oracles to provide price feeds to create some chaos and diversity in the network.

• The protocol does introduce some randomness in choosing oracles to provide price feeds, but not for the sake of creating chaos or diversity. Rather, the randomness is intended to prevent manipulation or collusion among Oracle providers.

• The protocol uses a decentralized oracle network called Maker Oracles, which consists of a set of nodes that report market prices of various assets to the protocol.

• The protocol randomly selects a subset of these nodes to form a quorum for each asset and calculates the median price from their reports. This ensures that the price feeds are accurate and reliable.

Let’s implement the 3-wave model.

Self-organization: We can evaluate the degree of connectivity and cooperation among the MakerDAO network participants by looking at the

• number and diversity of users, borrowers, and governors;

• frequency and quality of interactions and transactions among them; and

• level of consensus and satisfaction among them.

We can also evaluate the emergence of patterns and structures that enhance the functionality and value of the network by looking at the stability and scalability of the DAI stablecoin, the variety and quality of the collateral assets, and the alignment of incentives and goals among the network participants.

Entropy: We can evaluate the level of diversity and uncertainty in the MakerDAO network by looking at the

• volatility and risk of the cryptocurrency market;

• exposure to external shocks and internal fluctuations such as hacks, attacks, or black swan events; and

• balance between exploration and exploitation of opportunities such as new collateral types, interest rates, oracles, etc.

We can also evaluate the trade-off between efficiency and robustness by looking at the cost and speed of transactions, the security and reliability of the smart contracts, and the resilience and adaptability of the network to disruptions or changes.

Development: We evaluate the rate of growth and innovation in the MakerDAO network by looking at the

• adoption and usage of DAI stablecoin;

• creation and improvement of new products or services such as lending platforms, decentralized exchanges, or DeFi protocols; and

• competitiveness and utility of the network in the cryptocurrency ecosystem.

We can also evaluate the adaptation to changing environments and user needs by looking at the feedback and learning mechanisms that improve the network’s performance, such as governance votes, risk assessments, audits, etc.

Chaos: We can evaluate the sensitivity and unpredictability of the MakerDAO network’s behavior by looking at

• how small changes or differences in initial conditions or parameters affect the system’s outcomes or behaviors, such as DAI price, collateral ratio, liquidation events, etc.

• the generation of novel and unexpected outcomes or behaviors by examining how randomness or probabilistic functions introduce chaos and uncertainty in the network, such as oracle price feeds, governance votes, etc.

• the instability or collapse of existing patterns or structures by looking at how crises or challenges threaten or disrupt the network’s stability, such as collateral shortfall events, governance attacks, protocol upgrades, etc.

We can also evaluate the emergence of new forms of self-organization or entropy by looking at how chaos creates novel and unexpected patterns or structures that enhance or reduce the functionality and value of the network, such as flash loans, arbitrage opportunities, forks, etc.

Now let’s try to evaluate the transition between phases as well.

Self-organization to entropy: We can evaluate how MakerDAO responds to triggers or signals that indicate a change from self-organization to entropy by looking at

• how the network reacts to increasing diversity and uncertainty in the cryptocurrency market,

• how the network balances between efficiency and robustness; and

• how the network prepares for or anticipates potential shocks or fluctuations.

• how the network leverages its existing patterns and structures to cope with entropy, explores new opportunities or challenges, and maintains its functionality and value.

Entropy to development: We can evaluate how MakerDAO responds to triggers or signals that indicate a change from entropy to development by looking at

• how the network reacts to increasing growth and innovation in the cryptocurrency ecosystem, how the network adapts to changing environments and user needs;

• how the network learns from feedback and improves its performance.

• how the network leverages its existing diversity and uncertainty to foster development, exploits new opportunities or challenges, and increases its functionality and value.

Development to self-organization: We can evaluate how MakerDAO responds to triggers or signals that indicate a change from development to self-organization by looking at

• how the network reacts to increasing connectivity and cooperation among the network participants,

• how the network aligns incentives and goals among them, and how the network achieves consensus and satisfaction among them.

• how the network leverages its existing growth and innovation to enhance self-organization, creates new patterns and structures, and maintains its functionality and value.

Evaluating the transition phases between waves will be much clearer if we compare MakerDAO with another DAO.

One possible example of another DAO that we can compare with MakerDAO using this model is Uniswap. Uniswap is a decentralized autonomous organization that allows users to exchange tokens without intermediaries or fees.

Here is how we can compare Uniswap with MakerDAO using this model:

Self-organization:

MakerDAO and Uniswap have a high degree of connectivity and cooperation among the network participants, allowing anyone to join and interact with the network freely and transparently. This is true, as both projects are open and permissionless protocols that enable anyone to access their services or contribute to their development.

• For example, anyone can generate or borrow DAI on MakerDAO, or provide or swap tokens on Uniswap, without any intermediaries or gatekeepers. Anyone can also participate in their governance or community activities, such as proposing or voting on changes, providing feedback or suggestions, joining core units or working groups, etc.

Both networks also have patterns and structures that enhance their functionality and value, such as the DAI stablecoin, the collateral assets for MakerDAO and the liquidity pools, and the automated market makers for Uniswap. This is also true, as both projects have designed and implemented various mechanisms or components that improve their performance or utility. For example,

• MakerDAO has created the DAI stablecoin, a decentralized and censorship-resistant currency that maintains a soft peg to the US dollar. MakerDAO also supports various collateral assets that can be used to generate DAI, such as ETH, WBTC, USDC, etc., as well as real-world assets such as solar bonds or music royalties.

• Similarly, Uniswap has created liquidity pools, which are pools of tokens that facilitate trading on the protocol. Uniswap also uses automated market makers (AMMs), which are algorithms that determine the price of each token in a pool based on the ratio of their reserves.

However, MakerDAO does have a more formal governance structure than Uniswap, as it has a well-defined process for creating and advancing governance proposals through various stages.

• MKR token holders can vote on key decisions that affect the network, such as changes to risk parameters, collateral types, core units, budget distributions, etc., using a quadratic voting system that weights votes based on the square root of MKR staked.

• However, this voting process is not carried out through the MakerDAO Governance Portal anymore, as it has been replaced by a new interface called GovAlpha Portal in September 2022. The GovAlpha Portal is a more user-friendly and accessible platform that allows MKR holders to view and vote on proposals using different wallets or devices.

In contrast, Uniswap does have a more informal governance structure than MakerDAO, as it does not have a clear or consistent process for developing or advancing governance proposals. However, there is a formal voting process for some proposals that involve changes to the protocol or its parameters.

• These proposals are submitted and voted on using the UNI token through the Uniswap Governance Portal. The Uniswap community can also signal support or opposition to proposals through other platforms such as Snapshot, which is an off-chain voting interface that does not require gas fees.

• In December 2022, the Uniswap Foundation proposed some changes to the governance and voting processes of Uniswap, such as reducing the quorum requirement, simplifying the proposal stages, and introducing temperature checks and consensus checks. These changes are intended to reduce friction in governance and increase participation and engagement from the community.

Entropy:

Both MakerDAO and Uniswap are known to have a high level of diversity and uncertainty in the network, as they are exposed to the volatility and risk of the market, as well as hacks, attacks, or black swan events as both projects rely on smart contracts that can be vulnerable to bugs, exploits, or malicious actors. They also depend on the price movements of their underlying assets, which can be affected by market sentiment, whale movements, or other factors. For example,

• In September 2022, MakerDAO suffered a flash loan attack that drained $12 million worth of DAI from its protocol.

• Similarly, in October 2022, Uniswap faced a front-running attack that manipulated its oracle price feeds and caused $5 million worth of losses for its users**.**

Both networks also balance between exploration and exploitation of opportunities, such as new collateral types, interest rates, oracles, etc., for MakerDAO, and new tokens, liquidity pools, fees, etc., for Uniswap. This is also true, as both projects constantly seek to improve their services or features by experimenting with new options or optimizing existing ones. For example,

• MakerDAO regularly adds new collateral types to its protocol, such as real-world assets (RWA), synthetic tokens (sTokens), or wrapped tokens (wTokens), to diversify its risk profile and increase its DAI supply. MakerDAO also adjusts its interest rates or stability fees to maintain the DAI peg and incentivize borrowing or saving.

• Similarly, Uniswap supports new tokens or liquidity pools on its protocol, such as stablecoins (USDC/DAI), governance tokens (UNI/MKR), or L2 tokens (OPT/ARB), to increase its trading volume and liquidity. Uniswap also changes its fees or fee tiers to balance between profitability and competitiveness.

Development:

MakerDAO and Uniswap have a high rate of growth and innovation in the network, as they have high adoption and usage of their products or services, such as DAI stablecoin and lending platforms for MakerDAO, and token exchange and decentralized exchanges for Uniswap.

• This is true, as both projects have seen significant growth in terms of total value locked (TVL), trading volume, users, and governance participation in 2021. For example, MakerDAO’s TVL reached over $10 billion in 2021, while Uniswap’s trading volume surpassed $100 billion in August 2021, Both projects have also launched new products or services this year, such as MakerDAO’s Real-World Assets (RWA) program that allows non-crypto assets to be used as collateral, and Uniswap’s version 3 protocol that introduces concentrated liquidity and multiple fee tiers.

Both networks also adapt to changing environments and user needs, as they learn from feedback and improve their performance, such as governance votes, risk assessments, audits, etc. for MakerDAO, and community proposals, discussions, upgrades, etc. for Uniswap. This is also true, as both projects have active and engaged communities that participate in shaping the future direction and development of the protocols. For example,

• In 2022, MakerDAO conducted about 150 governance polls and 50 executive votes. Some of the topics covered by these polls and votes include onboarding new collateral types, adjusting risk parameters, approving core units, distributing budgets, updating oracle fees, deploying new modules, etc.

• In 2022, Uniswap implemented about 10 governance proposals. Some of the topics covered by these proposals include creating a DeFi Education Fund, deploying Uniswap v3 on Optimism, forming a Uniswap Grants Program, simplifying the community governance process, implementing a fee switch pilot program, etc.

However, MakerDAO has a more competitive and utility advantage than Uniswap, as it offers a unique product that is not easily replicated by other networks. At the same time, Uniswap faces more competition from other similar networks. This statement might have split support, including mine. This is partly true, but it depends on how you define competitive and utility advantage.

• MakerDAO does offer a unique product that is not easily replicated by other networks, as it is the first and largest decentralized stablecoin issuer on Ethereum. However, MakerDAO also faces competition from other stablecoin issuers or lenders that offer lower fees or higher yields than DAI or Maker Vaults.

• Uniswap faces more competition from similar networks, such as SushiSwap, Curve, Balancer, etc., that offer different features or incentives for liquidity providers or traders. However, Uniswap also has a utility advantage over other networks, as it is the most liquid and widely integrated decentralized exchange on Ethereum.

Chaos

Both MakerDAO and Uniswap have a high sensitivity and unpredictability of the network’s behavior, as they are affected by small changes or differences in initial conditions or parameters such as DAI price, collateral ratio, liquidation events, etc. for MakerDAO, and token price, liquidity ratio, slippage events, etc. for Uniswap. This is true, as both projects operate on complex and dynamic systems that can exhibit nonlinear and emergent behaviors that are hard to predict or control. For example,

• MakerDAO experienced a collateral shortfall event in March 2020, when a sudden drop in ETH price triggered a massive liquidation of undercollateralized vaults.

• Similarly, Uniswap experienced a slippage event in November 2020, when a large trade of DAI/USDC caused a temporary price deviation of DAI from its peg.

Both networks generate novel and unexpected outcomes or behaviors by introducing randomness or probabilistic functions such as oracle price feeds, governance votes, etc., for MakerDAO, and liquidity pool selection, fee distribution, etc., for Uniswap. This is also true, as both projects rely on stochastic processes that can introduce uncertainty or variability into their outcomes or behaviors. For example,

• MakerDAO uses oracle price feeds to determine the market prices of its collateral assets and DAI. However, these price feeds can be subject to delays, errors, or manipulation that can affect the accuracy or timeliness of the data.

• Similarly, Uniswap uses liquidity pool selection to determine the best route for a trade across multiple pools. However, this selection can be influenced by factors such as pool size, fee tier, gas cost, etc., that can change over time or across transactions.

However, MakerDAO has a higher risk of instability or collapse of existing patterns or structures than Uniswap, as it faces more severe crises or challenges that threaten or disrupt its stability, such as collateral shortfall events, governance attacks, protocol upgrades, etc. In contrast, Uniswap has more resilience and adaptability to such disruptions or changes. This statement might have split support, including mine. This is partly true, but it depends on how you measure instability or resilience.

• MakerDAO does face more severe crises or challenges that threaten or disrupt its stability, as it has to maintain the solvency and stability of its system under various market conditions and external shocks. For example, MakerDAO had to deal with collateral shortfall events in March 2020 and May 2021, governance attacks in October 2020 and January 2021, protocol upgrades in April 2020 and November 2021, etc.

  • However, MakerDAO also has some resilience and adaptability to such disruptions or changes, as it has implemented various mechanisms or solutions to mitigate or prevent them. For example, MakerDAO has introduced the Peg Stability Module (PSM) to improve DAI’s peg stability, the Liquidations 2.0 system to improve the efficiency and fairness of liquidations, the Real-World Assets (RWA) program to diversify its collateral portfolio, etc.

• Similarly, Uniswap has more resilience and adaptability to such disruptions or changes, as it has a simpler and more flexible design that allows it to operate with minimal intervention or maintenance. For example, Uniswap has launched its version 3 protocol that allows users to customize their liquidity provision with concentrated liquidity and multiple fee tiers, deployed its protocol on multiple L2 solutions such as Optimism and Arbitrum , integrated its protocol with various cross-chain bridges such as Hop Protocol and Gelato Network , etc.

  • However, Uniswap also has some risk of instability or collapse of existing patterns or structures, as it faces competition from other similar projects that offer different features or incentives for liquidity providers or traders. For example, Uniswap has lost some market share to other decentralized exchanges such as SushiSwap, Curve, Balancer, etc., that offer different AMM models or reward schemes.

AAVE

One possible example of a DeFi protocol to which we can apply this model is AAVE. AAVE is a lending protocol allowing users to borrow various tokens without intermediaries or fees.

Here is how we can apply this model to AAVE:

Self-organization: We can evaluate the degree of connectivity and cooperation among the AAVE network participants by looking at the

• number and diversity of users, borrowers, and lenders;

• the frequency and quality of interactions and transactions among them;

• the level of consensus and satisfaction among them.

• the emergence of patterns and structures that enhance the functionality and value of the network by looking at the stability and scalability of the lending platform,

• the variety and quality of the tokens available for borrowing and lending, and the alignment of incentives and goals among the network participants.

Entropy: We can evaluate the level of diversity and uncertainty in the AAVE network by looking at the

• volatility and risk of the cryptocurrency market;

• exposure to external shocks and internal fluctuations such as hacks, attacks, or liquidation events; and

• balance between exploration and exploitation of opportunities such as new tokens, interest rates, flash loans, etc.

We can also evaluate the trade-off between efficiency and robustness by looking at the cost and speed of transactions, the security and reliability of the smart contracts, and the resilience and adaptability of the network to disruptions or changes.

Development: We can evaluate the rate of growth and innovation in the AAVE network by looking at the

• adoption and usage of the lending platform; the creation and improvement of new products or services such as staking, governance, or insurance;

• competitiveness and utility of the network in the cryptocurrency ecosystem.

Chaos: We can evaluate the sensitivity and unpredictability of the AAVE network’s behavior by looking at

• how small changes or differences in initial conditions or parameters affect the system’s outcomes or behaviors, such as token price, liquidity ratio, interest rate, etc.

• the generation of novel and unexpected outcomes or behaviors by introducing randomness or probabilistic functions such as flash loans, liquidation bonuses, etc.

• instability or collapse of existing patterns or structures by looking at how crises or challenges threaten or disrupt the network’s stability, such as liquidity shortages, governance attacks, protocol upgrades, etc.

• how chaos creates novel and unexpected patterns or structures that enhance or reduce the functionality and value of the network, such as arbitrage opportunities, yield farming strategies, forks, etc.

Evaluating the transition between phases would include the same criteria as MakerDAO.

Polygon

Polygon is a Web3 startup that provides a platform for scaling and developing Ethereum-compatible blockchain networks. Polygon was founded in 2017 and is based in India.

Let’s see how we can apply the framework to evaluate Polygon using the 3-wave model of the evolution of complex systems.

1. Identify the domain or market of Polygon and its centralized and decentralized competitors.

   1. Polygon operates in ethereum scalability and interoperability, a crucial challenge for Ethereum-based applications that require fast, cheap, and secure transactions.

   2. Polygon’s main competitors include other L2 solutions for Ethereum, such as Optimism, Arbitrum, zkSync, and StarkWare, as well as other blockchain platforms that offer scalability and interoperability, Cosmos, Polkadot, Avalanche, and Solana.

2. Define the criteria or metrics for evaluating the performance and potential of Polygon in each phase of evolution.

   1. For example, we could use network size, activity, diversity, resilience, innovation, efficiency, scalability, security, usability, profitability, etc.

3. Collect data on Polygon and its competitors using various sources, such as web analytics, blockchain explorers, user feedback, market reports, etc.

   1. For example, we could use websites like DappRadar, DeFi Pulse, CoinGecko, Etherscan, etc., to get data on the number of users, transactions, fees, value locked, market capitalization, etc. of Polygon and its competitors.

4. Analyze the data using appropriate methods, such as descriptive statistics, network analysis, trend analysis, benchmarking, etc.

   1. For example, we could use charts, tables, graphs, etc., to visualize and compare the data on Polygon and its competitors regarding network size, activity, diversity, resilience, innovation, efficiency, scalability, security, usability, profitability, etc.

5. Evaluate Polygon in each phase of evolution using the criteria or metrics defined in Step 2. For example:

   1. Self-organization: We could evaluate how well Polygon self-organizes its

      1. network of users and nodes by looking at the number of users (both individual and institutional),

      2. transactions (both simple and complex),

      3. fees (both fixed and variable), value locked (both native and bridged),

      4. market capitalization (both absolute and relative), etc.

      5. distribution of users and nodes across different regions (both geographic and demographic),

      6. applications (both general and specific),

   2. Entropy: We could evaluate how Polygon manages entropy and randomness in its system by looking at the

      1. level of disorder and diversity in its network.

      2. variance of users (both active and inactive),

      3. transactions (both successful and failed),

      4. fees (both high and low),

      5. value locked (both stable and volatile),

      6. market capitalization (both rising and falling), etc.

      7. diversity of users and nodes across different regions (both geographic and demographic),

   3. Development: We could evaluate how Polygon develops its complexity and functionality over time by looking at the

      1. number of features and services it offers its users and nodes.

      2. number of applications (both existing and new),

      3. integrations (both internal and external),

      4. upgrades (both planned and unplanned),

      5. innovations (both incremental and radical), etc.

6. Evaluate Polygon in terms of its transitions between the phases of evolution using the criteria or metrics defined in step 2. For example:

   1. Self-organization to entropy: We could evaluate how smoothly and effectively Polygon moves from self-organization to entropy by looking at

      1. how it balances order and disorder in its network.

      2. how it maintains stability and security while allowing for diversity and innovation; incentivizes participation while preventing exploitation; adapts to changing conditions while preserving its identity; etc.

   2. Entropy to development: We could evaluate how smoothly and effectively Polygon moves from entropy to development by looking at

      1. how it leverages disorder and diversity for complexity and functionality.

      2. how it uses randomness and uncertainty for creativity and discovery, diversity and competition for collaboration and cooperation, disruption and challenge for improvement and growth, etc.

   3. Development to self-organization: We could evaluate how smoothly and effectively Polygon moves from development to self-organization by looking at

      1. how it simplifies complexity and functionality for order and structure.

      2. how it reduces redundancy and inefficiency for optimization and performance, standardizes features and services for compatibility and interoperability, consolidates applications and protocols for scalability and usability, etc.

7. Synthesize the results and draw conclusions about the strengths and weaknesses of Polygon and its competitors in each phase of evolution and in terms of their transitions.

Furthermore, the 3-wave model of complex systems framework can be modeled across multiple scales.

This scale model consists of three waves:

• (start) microscopic level, where individual users interact with each other according to simple rules;

• (progress) mesoscopic level, where emergent patterns and structures arise from the collective behavior of the users; and

• (growth) macroscopic level, where the collective contribution is observed and measured.

Some of the key challenges and considerations while considering implementing the scale model of 3-wave model of complex systems for building dApps are:

1. How to design and implement simple and robust rules for users at the microscopic level that can generate complex and adaptive behavior at higher levels.

2. How to ensure the security, scalability, and performance of the dApps on a decentralized network that may be prone to attacks, congestion, or failures.

3. How to balance the trade-offs between decentralization and efficiency, such as gas fees, consensus mechanisms, and governance models.

4. How to measure and evaluate the macroscopic outcomes and impacts of the dApps on the users, society, and environment.

5. How to communicate and educate the users and stakeholders about the benefits and risks of using dApps and complex systems.

One possible example of how to apply this model to the Solana blockchain is as follows:

Microscopic level:

We can evaluate the individual users and their interactions with each other according to simple rules by looking at the

• characteristics and preferences of the users,

• types and frequencies of the interactions, and

• outcomes and feedback of the interactions.

We can also look at

• how the rules are defined and enforced by the protocol,

• how the users comply or deviate from the rules, and

• how the rules affect the users’ behavior and satisfaction.

For example, we can look at

• how Solana users choose which validators to delegate their tokens to;

• how Solana validators communicate and cooperate to produce blocks; and

• how Solana rewards or penalizes users and validators for their actions.

Mesoscopic level:

We can evaluate the emergent patterns and structures that arise from the

• collective behavior of the users by looking at the degree of connectivity and cooperation among the users;

• emergence of clusters, modules, or communities; and

• functionality and value of the patterns and structures.

We can also look at

• how the network topology, dynamics, and environment influence the patterns and structures;

• how the patterns and structures influence the network performance and potential; and

• how the patterns and structures evolve over time.

For example, we can look at

• how Solana users form groups or alliances based on their interests or goals;

• how Solana validators form clusters or subnets based on their location or capacity; and

• how Solana creates a scalable, fast, and secure network using its innovative features such as proof of history, tower consensus, etc.

Macroscopic level:

We can evaluate the collective contribution that is observed and measured by looking at the

• adoption and usage of the protocol,

• creation and improvement of new products or services, and

• competitiveness and utility of the protocol in the ecosystem.

We can also look at

• how the collective contribution is influenced by external factors such as market conditions, user needs, or regulatory frameworks;

• how the collective contribution influences external factors such as social impact, economic value, or environmental sustainability; and

• how the collective contribution evolves over time.

For example, we can look at

• how Solana attracts and retains users, developers, and investors with its high performance and low cost;

• how Solana supports and enables new products or services such as decentralized applications, smart contracts, oracles, etc.; and

• how Solana competes and cooperates with other blockchain platforms such as Ethereum, Binance Smart Chain, Polkadot, etc.

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After Thoughts

The model can be applied to various domains and contexts, such as DeFi, DAOs, NFTs, etc., to understand their dynamics and potential.

On further implementation of this model, I’d be able to suggest some implications and challenges for the future of whatever domain I explore. But I will do that in the coming weeks, not now.

I truly believe that by using this model, we can better appreciate the complexity and diversity of Web3 and the decentralized infrastructure built on it and understand the overall impact on the collective.

Please share your thoughts here:

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My previous research:

1. Decoding & Democratizing web3

2. P2E: A shift in gaming business models

3. Stablecoins: Is There Hope?

4. Unlocking the Potential of Decentralized Data

5. Primer on L2 Scaling Solutions

6. Understanding User Dynamics in DeFi

7. Centralizing Blockchain Ecosystems

8. DeFi Lending & Borrowing Primer: Part 1 & Part 2

9. What's the Best Way to Create Value with Selective Data?

10. Build in Web3 Series: Climate Finance DAO

11. Governance & Growth in Modern Society

12. Understanding ERC-4337: The Future of Ethereum's Account Abstraction Revolution

13. Identities without Borders: Decoding our online identity