Stress Testing Tokenomics for a Successful Launch

Leverage simulations for a robust economy

Introduction

The first months following a token listing can be some of the most stressful times in the life of a token economy. Doubts about whether the token will hold its value are inevitable, as speculation, conflicts of interest, and vesting calendars come into play. While hype and speculation are significant factors, it's essential to have some extra guarantees beyond the hype that you will have a successful launch.

At Cenit, we have built a tokenomics modeling tool that lets you anticipate and solve these scenarios before they become a problem. By creating a digital replica of your tokenomics, we can stress test it and find the best tokenomics design parameters for a robust and sustainable protocol.

Predicting speculation is impossible, but our simulations offer a comprehensive view of what to expect in a token economy under all potential scenarios you may face.

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YogiLand Tokenomics: A Case Study

For demonstration purposes, we will examine the hypothetical token economy of YogiLand, a web3 town builder game where players compete for resources to create and improve their camp. 

The collected resources can be used to purchase in-game boosts in the form of NFTs (non-fungible tokens). The primary resource, YOGI, is a tradable currency on the blockchain. Users can also obtain resources from surrounding areas, which are simultaneously NFTs that generate yields for their landlords.

Modeling the Economy

When modeling the tokenomics, we consider four different types of agents:

1. Players/funseekers: They play for fun and spend some money on the game to improve their gameplay experience. They are the primary revenue drivers in the economy.‍
2. Workers: They are players as well. However, their objective is to extract as much value from the economy as possible, and are attracted by its play-2-earn aspects.‍
3. Landlords: These passive players purchase NFTs for yield farming.‍
4. Token Holders: Originating from the vesting schedule, each Token Holder may have different selling strategies.

For our modeling purposes, we incorporate a range of player growth scenarios, resulting in an increased number of funseekers entering the economy and consistently generating revenue for the project, regardless of the actions of the rest of the ecosystem. Based on these initial assumptions, the other agents will respond accordingly.

Our methodology is entirely quantitative, requiring the addition of parameters to establish behavioral boundaries for each agent. We consider two types of parameters:

• Behavior hypotheses: variables that the modeled protocol has limited to no control over
• Design parameters: variables that they can control, such as fees, burning ratios, vesting calendars, and so on.

Baseline Scenario

At our gamefi demo, we have a baseline scenario with the following player growth:

Which represents a typical curve of player count evolution for many games of medium success. Players will spend on average $0.02 per day.

The design parameters/user behavior parameters chosen can be seen in detail in the dashboard sidebar:

For example, the setup sets an initial token price at $0.6 USD, with a maximum token supply of 1 billion of tokens.

Baseline economy

With this parameter configuration, the token economy experiences a major setback just after launching, with a decrease in token price of approximately 80% from its initial value.

Upon analyzing the selling and buying pressure generated by the various simulated agents, it becomes clear that the selling pressure from vested allocations is overwhelming the game economy. A break-even point is only reached after the 15th month.

Prior to that point, it is evident that the volume of tokens bought by funseekers is not enough to counterbalance the selling pressure, even in this configuration, where only a small percentage of the vested amount (20%) is being sold by the different agents.

Actions to Address the Issue

Several strategies can be implemented to address the difficulties encountered in the tokenomics early stages:

1. Marketing Efforts to Increase Users: A strong marketing campaign can draw more users to the platform, boosting token demand and potentially counteracting selling pressure. ‍
2. Prolonged  Vesting Schedule: The main issue with this economy appears to be the selling pressure resulting from the vesting schedule. A potential solution under the control of the developer team could be to prolong the vesting schedule, ensuring that the community and project owners are more aligned while giving the project extra time to grow.‍
3. Adjusting Public Sale Price: Modifying the initial token offering price to align more closely with market expectations and demand can aid in stabilizing the token's value during the initial stages. Of course, it is not always possible to change this price freely since many projects have to maintain the promises previously made with their investors, but it is worth exploring the impact of the variable.‍
4. Staking Strategies: Introducing staking strategies can incentivize users to hold their tokens for longer periods, decreasing selling pressure until the break-even point is reached.

Let's now examine our demo dashboard, where we can quantify the effect of multiplying marketing efforts to increase the initial number of users:

With the initial set of parameters, we had the selling pressure break-even point happening approximately in the 15th month, and a minimum fully diluted market cap of 14 M$. This was reached under the following expected player count growth conditions:

For instance, if we were able to bring three times as many users to the platform during the token launch (which can be simulated in our dashboard by changing the "initial player count" to be 60K and the players at saturation to be 3M) the break-even occurs at month 10th. 

There is a reduction of 30% in the time until the project becomes self sustainable. This reduction could make the difference between a community that holds the token for longer or jumps ship. 

Additionally, now we are able to set a reference point for the marketing expense of the project, since now we are able to measure the impact on the Market Cap. With the new scenario, the project's lowest fully diluted market cap is $24M instead of the original $14M. Financing this marketing campaign would be considerably cheaper.

Conclusion 

While it is true that mosts of the projects at the very beginning it is too weak to stand the selling pressure of the vesting calendars by itself without a group of believers that purchase and hold a stake in the project, thanks to our simulations we have analyzed the tokenomics of the project and

• Understood a potentially critical problem with our baseline tokenomics
• Quantified when the break-even in the token economy will happen, and therefore we have a temporal horizon in which to plan and budget our marketing strategies
• Identified and quantified multiple tweaks that we could do to minimize the impact in the economy for these early months, and modeled the effect of these tweaks.

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If you are interested in knowing more about how to model your economy, stay tuned for the upcoming posts and contact us here. 

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