Since the popularity of ERC-20 assets in the blockchain space in 2017, Web3 has entered an era of low-barrier asset issuance. Various projects have been issuing custom tokens or NFTs through methods like IDO and ICO, often with issues of strong price manipulation or lack of transparency. Rug pulls have become frequent, with many viewing ICOs and IDOs as prime opportunities for scamming investors.

Today, conventional IDOs and ICOs have fully exposed their flaws in terms of fairness. People have long hoped for a more equitable and reliable asset issuance protocol to address the many problems that arise during new project token generation events (TGEs). While some innovative projects have unilaterally proposed their own “fair economic models,” these often lack widespread adoption and end up being seen as “specific cases” rather than “a set of abstracted protocols.”

So, what kind of model would be a fairer and more reliable way to distribute assets? What kind of solution could serve as a universal protocol? This article will introduce Cellula, which offers a fresh perspective on solving the aforementioned problems. They have implemented an asset distribution layer that simulates Proof of Work (PoW), using virtual Proof of Work (vPOW) to “mineify” the asset distribution process, mimicking Bitcoin to achieve a fairer asset allocation paradigm.

Although many view this project as GameFi, since the in-game rewards distributed can be set to any type of token, Cellula could theoretically serve as an asset distribution platform with PoW effects. This brings broader prospects and imaginative space to Web3 asset issuance, and it could even be called “a social experiment paying homage to Bitcoin mining.”

POW and vPOW: Lottery Draws With Unpredictable Outcomes

In fact, whether it is authentic POW or POS, or the vPOW we are going to talk about today, the essence is to set up a set of algorithms with unpredictable/difficult output results, and conduct “lottery draws” through the output results.BTCMiners must construct a block that meets the restrictive conditions locally and submit it to all nodes in the network to pass consensus before they can get the block reward. As for the restriction, the Hash of the constructed block must meet special requirements, such as the prefix being 6 zeros.

Since the block Hash generation result is unpredictable/difficult to predict, in order to construct a block that meets the conditions, the input parameters of a given algorithm can only be constantly changed. This process requires brute force and has a high impact on the miner’s hardware equipment. Require.

In short, BTC mining uses the unpredictability/unpredictability of the SHA-256 hash algorithm to implement a “lottery draw” system in which miners across the entire network participate online. This design ensures that at the expense of electricity, Participation is permissionless in form.

In addition, POW is a fairer method of asset distribution. It is much more difficult for project parties to control the assets in the mainstream POW public chain than in the POS public chain.In many POS public chains or IC0 and ID0 solutions, there are many cases where project parties forcefully control the market.

(Solana, under the manipulation of FTX, skyrocketed nearly 500 times in 2020~2021 alone, which is extremely unfriendly to Validator operators who entered the market later)

For example, under the manipulation of FTX and SBF, Solana’s price skyrocketed nearly 1000 times from 2019 to 2021. Many Solana validator node operators were early investors, acquiring their tokens at nearly zero cost. This severely undermined the fairness of asset distribution. Although there is room for project teams to manipulate the market in POW, the extent is often much less than in POS.

The problem is, since POW is often applied to underlying public blockchains rather than the asset issuance layer of DApps, can we simulate the effect of POW with an on-chain solution? If so, we can implement a fairer and more reliable asset distribution protocol than highly controlled schemes like ICO and IDO. Combined with some game scenarios, we can create interesting GameFi projects (although the actual use cases are not limited to games, it can also provide a fair asset distribution scheme for other projects).

So the key is, how can we simulate the effect of POW on the on-chain asset issuance layer? In the GameFi project Cellula introduced in this article, the famous “Conway’s Game of Life” algorithm is introduced to allocate computing power to virtual digital entities on the chain (called “BitLife”). Simply put, it’s like having a group of people cultivating cell clusters in their own petri dishes. As time goes on, whoever has more surviving cells in their petri dish will get more mining power after conversion and be more likely to get mining rewards.

In short, Cellula replaces the traditional hash calculation of POW with another unpredictable/hard-to-predict calculation method, replacing the “Work” form in “Proof of Work”. In Cellula’s thinking, the key is how to obtain a petri dish (BitLife) with more surviving cells, and the evolution of the BitLife state requires the consumption of computing resources. Essentially, it turns the hash algorithm executed in BTC mining into a specific algorithm for simulating Conway’s Game of Life, which is called vPOW (Virtual POW).

Let’s delve deeper into the mechanism design of vPOW. I have to say, many of the details here are very interesting. We can say that one of the things Cellula is doing is simulating the BTC mining industry chain model through on-chain NFT trading chains.

The Core of vPOW: Conway Game of Life and BitLife

Before delving into Cellula’s mechanism design, let’s first look at the most important core of vPOW: Conway’s Game of Life. It can be traced back to John von Neumann’s concept of “cellular automata” proposed in 1950, and then mathematician John Conway formally proposed “Conway’s Game of Life” in 1970, using algorithms to simulate the evolution of life in nature.

Imagine a petri dish divided into a grid of small squares. We then “initialize” the petri dish by placing living cells in some squares. After that, the life and death states of these cells will evolve over time, gradually forming complex cell clusters (you can imagine how mold reproduces). This is essentially a two-dimensional grid game with very simple rules:

• Each cell has two states: alive or dead, just like in Minesweeper, and each cell interacts with the cells in the eight squares around it.
• If a cell is alive but there are fewer than 2 live cells in the 8 surrounding grids (0 or 1), the cell enters a dead state.
• If a cell is alive and there are 2 or 3 live cells around it, the cell remains alive.
• If a cell is alive and there are more than 3 live cells around it, the cell enters a dead state (simulating a scenario where there are too many life forms and they compete for resources).
• If the current cell is dead but there are 3 live cells around it, the cell becomes alive (simulating cell proliferation).

So, it’s very simple. Given an initial pattern of cell states in a two-dimensional petri dish, and following the above rules, the cell states will continuously evolve and iterate over time, producing a myriad of results. You can even use Conway’s Game of Life to simulate the effects of a computer.

For example, the life/death of each cell in the petri dish corresponds to the binary 0/1. You can treat the initial state of the cells as “input parameters”, and the life or death of each cell (0 or 1) represents the input data. Then, the cell state will begin to evolve according to the initial pattern, and each round of state change is equivalent to a step in the calculation process. The state obtained after a period of time can be regarded as the “output”.

As long as the appropriate initial pattern is arranged, Conway’s Game of Life can output specific results after several generations of evolution. Due to the myriad of initial patterns, its characteristics can be used to simulate lottery draws. We can set restrictions, and each player randomly selects a batch of initial patterns. After 100 generations of evolution, the petri dish owner whose output results meet certain xx characteristics is eligible for the reward. This is quite close to the idea of BTC mining:

“The system first defines which type of output results meet the requirements, and participants input random initial values into the given algorithm, trying to obtain output results that meet the requirements.” Since there are a huge number of initial input parameters to try (almost astronomical), you have to put in a lot of effort to get lucky and win the prize. This is the logic of proof of work: miners must put in a certain amount of work to get a reward.

After understanding the basic ideas of Cellula and Conway’s Game of Life, let’s look at its specific design details. Cellula divides the aforementioned “petri dish” into 9*9=81 squares, and each square has two states of life/death (corresponding to binary 0 and 1). In this way, according to permutations and combinations, there are 2^81 initial states of cells in the petri dish, which is equal to the square of 1 trillion (basically an astronomical number).

Then, what players need to do is to choose the initial pattern (input parameters) of the petri dish. BitLife acts as the entity of the petri dish (actually an NFT) and contains 81 squares, with one cell placed on each square (which may have two states of life or death, and an empty square is equivalent to a dead cell). Then, every 3*3=9 adjacent squares in BitLife constitute a BitCell, and each BitLife is composed of 2-9 BitCells (if the BitLife you construct does not have enough 9 BitCells, some places will be vacant, and the default is all dead cells).

Based on combinations, a BitCell (a 3x3 grid) has 2^9 initial patterns. Players randomly select and combine different patterns to form a BitLife. Simply put, it’s like randomly choosing an initial pattern for your petri dish. As mentioned before, there are a total of 2^81 initial patterns, an astronomical number. This vast choice resembles the BTC mining scenario using SHA-256.

The cell state of BitLife changes with the increase in block height. Cellula allocates computing power based on the state of BitLife at different block heights. At a given block height, a BitLife with more living cells has more computing power, effectively creating a virtual mining machine.

To give a concrete example, Cellula participants aim to exhaustively enumerate the 2^81 initial patterns of BitLife off-chain to predict the evolved state of each pattern and check if they meet the reward system’s requirements. Suppose the current block height is 800, and the system requires that by block height 1000, the BitLife with the most living cells receives the highest reward. Participants then have a clear objective:

At block height 800, I need to acquire a BitLife pattern that at block height 1000 will have more living cells than other BitLifes.

This is essentially the core gameplay of Cellula. Your goal is to construct or buy from others the BitLife most likely to win mining rewards. This model allows ordinary and advanced users to develop their own mining machines, sell them to others, or buy others’ machines for mining. If you want to create your own mining machine, you must simulate the evolution of different BitLife patterns’ states off-chain, which consumes computational resources. If you choose to buy others’ machines, you are essentially buying BitLifes of different initial patterns, requiring you to independently assess future state changes of these BitLifes, thus necessitating off-chain calculations. This is a particularly intriguing aspect of Cellula’s game design.

After understanding the game’s core mechanism, let’s explore further details: Actually, the living cells in BitLife can overflow beyond the initial 9x9 grid, with the number of living cells potentially far exceeding 9x9, without boundary limitations. As shown, if a BitLife contains an increasing number of active cells, its allocated mining power will also increase. Conversely, if the initial pattern choice for BitLife is poor, leading to fewer living cells, its computing power will decrease.

Then, the system distributes mining rewards (called energy points in the game) every 5 minutes based on each BitLife’s computing power share in the network.

In Cellula, the process of synthesizing BitLife is akin to “manufacturing” a new mining machine. We’ve previously mentioned that the BitLife entity is an NFT. Once minted on-chain, BitLife requires a “charging” operation to activate mining. Each charge is valid for 1, 3, or 7 days, requiring a small fee, and needs to be renewed upon expiration.

It’s worth noting that to encourage users to charge their BitLifes frequently, Cellula has implemented a “charging lottery” feature. Each time you initiate a charging operation, you may be randomly selected to receive additional rewards (separate from mining rewards). This design will be briefly introduced later in the Analysoor algorithm section.

According to Cellula’s official rules, the minting of BitLifes with 3x3 BitCells (81 small squares) has been discontinued, with over 1.5 million such BitLifes minted. New users can purchase BitLifes on the secondary market and engage in charging mining. The official explanation for limited minting is to maintain the stability of the game ecosystem, preventing scientists from infinitely minting BitLife NFTs, which would devalue the mining machines.

Moreover, in the future, Cellula will introduce roles similar to mining machine manufacturers. These roles will be permission-based, requiring token staking, public sales channels, and a certain community size and influence. These manufacturers will be responsible for minting and selling BitLifes containing 4x4 BitCells, which include 16x9=144 small squares. The amount of BitLifes a manufacturer can mint will be limited by their staked token amount.

We’ve broadly explained the core concepts involved in vPOW. The essence of vPOW lies in a computational model based on predefined rules, where participants can engage in competition through optimized strategies, gamifying asset issuance and distribution. Cellula simulates the operational form of the BTC mining machine market, replacing the computational task form in proof of work. As the distribution method of mining power can be dynamically adjusted, no BitLife pattern is globally optimal. The BitLife with the most living cells today might be surpassed by others tomorrow, leading to complex emergent phenomena and dynamic strategies.

Analysoor Lottery Algorithm and VRGDAs Exponential Pricing Curve

In the previous sections, we’ve delved into the core mechanics of Conway’s Game of Life and Cellula. Now, let’s explore some of the other design elements within the game. As mentioned earlier, Cellula features a charging lottery system that utilizes a random number generation algorithm called Analysoor. By using block hashes as input parameters for the random number generator, the algorithm selects winners from among those who have charged their BitLifes within each block, introducing a lottery-like mechanism.

For example, in Analysoor’s design, the current BNB Chain block hash might be a long string like 6mjv…. containing four numbers: 6, 2, 1, 6. Based on their order in the string, the first and last numbers are 6, which are even, so counting proceeds from the beginning. The number 6 corresponds to the 7th transaction (counting starts at 0), thus selecting the 7th charging player in the current block as the winner. The design can be more flexible; this is just an example. This randomness-based lottery algorithm effectively incentivizes players to charge more, boosting in-game ecosystem activity.

Additionally, in Cellula’s entire transaction model, there’s an issue: once a certain BitLife pattern is minted by a prominent player, its BitCell combination scheme becomes public, allowing others to “follow suit” and mint BitLife using the same combination. This can lead to a phenomenon where many people follow the trend, severely affecting the randomness of game outcomes. To address this, Cellula introduces Variable Rate Gradual Dutch Auctions (VRGDAs), a pricing algorithm developed by Paradigm. It dynamically adjusts prices—raising them when minting exceeds expectations and lowering them when it falls short.

Suppose the initial expectation is to mint 10 A-type NFTs daily, starting at 1 CKB. By the 5th day, the expectation is to have minted 50 A-type NFTs, but due to many followers, the minting reaches 70, the equivalent of the target for the 7th day. To regulate this, the exponential pricing curve quickly raises the minting price, increasing the unit price to 4 CKB to curb minting.

If by the 15th day, only 120 have been minted (rather than the 150 expected), prices will be lowered to stimulate minting.

In this scenario, when a certain type of BitLife is minted in large quantities over a short period, its minting price will exponentially increase. This steep price rise can effectively deter excessive mimicking by players.

Summary: Viewing Cellula from a Player’s Perspective

After discussing the core designs of Cellula, let’s consider this imaginative game mechanism from a player’s perspective. In vPOW, there are many participants, each with different strategies. Take the primary issuance market as an example. A “scientist” can write code to combine different BitCells to find a BitLife with greater computational power, thus obtaining higher mining rewards. Meanwhile, some MEV players monitor minting events on the chain, and when they notice a notable scientist minting a certain type of BitLife, they follow suit and mint in large quantities.

However, due to the existence of the VRGDAs exponential pricing algorithm, the minting price of a single type of BitLife can rise exponentially. This effectively deters scientists (acting as a Sybil attack countermeasure) and also prices BitLife/mining machines. If a type of mining machine has high computational power, its minting/production price will also be high, influencing its price on the secondary market and throughout the supply chain.

Comparable to the issuance process of BTC mining machines, when a scientist discovers a BitLife with high computational power, it’s akin to a mining company developing a new chip. When MEV players follow and mint, it’s like a primary distributor setting the mining machine’s price, and subsequent secondary market transactions resemble retail investors buying equipment from distributors.

The difference is that, compared to real-world mining machine development, scientists can discover new BitLife much faster, and anyone can participate in the BitLife state evolution. This significantly lowers the barrier to mining machine development, giving everyone a chance to become a “scientist,” which is friendlier to most and impossible in the real-world mining production chain.

For the project team itself, adopting a POW-style asset distribution scheme weakens its power, so neither scientists, project teams, nor ordinary players can unilaterally control the market. In the minting and issuance stages of mining machines, a three-way game emerges, with no side able to completely monopolize the market, forming a dynamic equilibrium.

Overall, compared to the BTC mining machine industry chain, Cellula’s approach is a more interesting social experiment.

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