Executive Summary

Onchain gaming offers new possibilities, but disincentivized tokenomics puts long-term viability at risk.

Speculation breeds volatility. Inflation spirals. Community gives way to profit-seeking.

P2E tokens fail their users time and again. There’s no utility attached to holding P2E in-game tokens for a long time.

• What if we could evolve ecosystems to instead prioritize fairness, stability, and engagement? Where incentives focus more on collaboration?

• What if we go tokenless and build on top of the native chain token (ETH, SOL)?

That is the vision behind DITE - the proposed Dynamic Interactive Tokenless Economy model.

DITE provides data-driven tools to incrementally transition token-dependent games to be community-focused, not currency-focused.

The core thesis is: “As onchain games mature, their in-game tokens lose value. Many in-game P2E tokens are useless & worthless. Why not transition away from these P2E tokens and use the native chain token (ETH, SOL) for in-game currency? More liquidity, more stability, and more incentives for increased engagement.”

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Table of Contents:

1. Introduction

   1. Why player-centric design is crucial for sustainability

   2. How DITE offers solutions through economic modeling

2. The DITE Model

   1. Key principles and philosophy

   2. Primary variables and dynamics

   3. Constraints for fairness and stability

   4. Adaptability to evolving behaviors

   5. Focus on maximizing player retention

3. Simulating Economies with DITE

   1. Initializing based on current tokenomics

   2. Running scenarios and projections

   3. Quantifying impacts on revenues, speculation, etc.

4. Transitioning to Sustainable Models

   1. Gradual token decoupling strategies

   2. Shifting to predictable off-chain rewards

5. Implementing DITE in New Games

   1. Integrating early in development process

   2. Co-designing tokenomics and gameplay mechanics

   3. Setting up analysis and instrumentation

6. The Future of Player-Centric Economies

   1. Evolving sustainability in blockchain gaming

   2. Community-driven development principles

   3. The next generation of participatory game economies

7. Appendix

   1. Assuming Parameters

   2. Applying DITE model to refine Axie Infinity’s Tokenomics

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Earlier in 2022, I did a deep dive on P2E: Shift in Gaming Business Models, where I believed that a benchmark P2E model comprising of the following four traits:

• Deep liquidity pool and strong network effect,

• Trust from customers with a specific focus on regulatory compliance,

• Strong brand as a secure platform, and

• Platform for innovation.

Over the past few months, I've been an active advocate of P2E platforms being more focused & built on top of the native blockchain token and slowly getting rid of their in-game currency.

When I say Tokenless, I don't mean tokenless forever but eventually over time, after you create an environment of loyalty and craze around your game.

The reason why I tread towards advocating for a tokenless model is that most P2E games fade away, not because the game gets boring, but because the mania behind farming and earning tokens lives a short life.

Transitioning to reduce dependence on the volatile game token could indeed save many P2E projects from going out of business and would also benefit from that greater stability in demand.

Now, designing a great game is tough.

But designing a great in-game economy? Even tougher.

You want your players to be engaged, rewarded, and incentivized just right. But it's tricky to anticipate

• how they'll behave,

• what strategies they'll adopt, and

• how your systems will truly play out long-term.

What if you could model and simulate your game's economy to optimize it even before launch?

• Tweak incentive structures,

• run scenarios, and

• gain insights you'd normally only get years after release.

That's the aim behind this experimental Dynamic Interactive Tokenless Economy model, or DITE for short.

The hypothesis behind DITE is simple:

“A P2E game that transitions from a token-dependent economy to a tokenless economy by dynamically modeling and optimizing incentives around in-game resources will exhibit greater sustainability, fairness, and retention compared to a diminishing token-based economy.”

DITE aims to let you mathematically represent your game's economic DNA—things like supply, demand, pricing, and, crucially, player retention. But the key is it's dynamic.

In theory, you could see how changes impact key factors before going live. Test different reward schedules, gameplay tweaks, and anything through hands-on simulations. It's like a digital petri dish for incubating the perfect in-game economy.

I’ve designed DITE to be accessible too. The math is all under the hood, with intuitive controls and visualizations. Tweak levers and observe outcomes.

And DITE is intended to keep learning as your game operates. Real usage data continuously refining the model, making simulations more accurate over time. It's the best of data-driven design and human craftsmanship.

But it's important to note that DITE is still in the experimental stage.

While it shows promise for proactively engineering sustainable, optimized game economies, more research and testing is needed to validate its capabilities across different gaming environments.

If collaborating on advancing economic modeling for games intrigues you, I’m eager to explore partnerships.

I first started building this model by defining a few variables:

• Represents the total amount of in-game resources or items or tokens.

• Changes as players earn, spend, or trade resources within the game environment.

• Represents the current in-game value or "price" of resources.

• Affected by player behaviors, in-game supply and demand, and external game events.

• Represents the collective player desire or demand for in-game resources.

• Influenced by in-game events, quests, and overall game popularity and mechanics.

• Indicates the percentage of players who remain engaged with the game over time.

• A reflection of game satisfaction, influenced by game mechanics, rewards, and player interactions.

• Quantifies how easily players can trade, buy, or sell in-game resources.

• Influences and is influenced by the game's economy balance, player behaviors, and external factors.

• The rate at which new players join the game.

• Affected by game marketing, popularity, and external events.

• The rate at which players leave or discontinue playing the game.

• Influenced by game satisfaction, external factors, and changing player preferences.

Some Key Points:

• form the foundational supply-demand dynamics, representing the in-game resources, their value, and the collective player demand, respectively.

• are crucial for understanding and predicting player engagement, detailing player loyalty (retention rate), new player acquisition, and the rate of departing players.

• is a pivotal metric, encapsulating the ease of trading and liquidity considerations. It's essential for understanding how smoothly resources can be traded and exchanged within the game's economy.

After defining the variables, it is important to represent the interactions and feedback loops between various game components.

For the DITE model, let’s call them game dynamics.

These dynamics are crucial because they encapsulate the essence of how different elements of the game economy interact over time. Understanding these interactions allows us to predict potential outcomes and make informed decisions to ensure the health and sustainability of the in-game economy.

For this model, I will define and structure five in-game dynamics.

Before that, let’s identify some parameters for calculations

1. Economic Sensitivity: how responsive the game's economy is to changes in player behavior and other dynamics. Higher values mean the economy reacts more quickly to changes.

2. Player Behavior Sensitivity: indicates how much player behaviors, like trading, playing time, and interactions, influence the game's economic and loyalty dynamics.

Check the Appendix on how to assume values for both the parameters.

Now, let's dive deeper into the primary dynamics of the DITE model:

Game Resources Dynamics:

As the resource value increases, the overall amount of resources in the game tends to decrease, simulating consumption, use, or trading by players.

Rationale:

• This primarily states that the rate of change of in-game resources is inversely proportional to the current value (or price) of the resources (tokens).

• As the value of the in-game resources (tokens) increases, consumption and use of these resources by players are expected to rise, leading to a decrease in the available resources.

• Essentially, the more valuable something becomes in the game, the faster it will be consumed or utilized by the players.

Resource Value Dynamics:

The value of in-game resources changes based on the difference between player needs and available resources, modulated by the economic sensitivity of the game.

So, it's important that we calculate the rate at which the resource's value changes.

Rationale:

• This equation connects the rate of change of the resource's value to the difference between the demand and supply of the resource, scaled by the Economic Sensitivity parameter.

• If demand exceeds supply, the resource's value will increase, and vice versa.

• The Economic Sensitivity parameter helps calibrate how responsive the price is to imbalances between demand and supply.

Player Need Dynamics:

Players' demand for resources is influenced by the current value of those resources and their engagement level (loyalty).

Rationale: Player need (or demand) is influenced by several factors.

• First, it's inversely proportional to the current value of the resources. As prices rise, players demand fewer resources, and

• It's directly proportional to player loyalty. High loyalty indicates greater engagement and, therefore, greater demand.

Trading Ease Dynamics:

The ease of trading in the game is influenced by how much players need resources and how many resources are in the game.

Rationale:

• It reflects how easily resources can be traded within the game environment.

• The ease of trading is influenced by both demand and supply but in opposite directions.

• High demand can make trading more accessible as players actively seek resources. Conversely, an oversupply can hinder trading since everyone has what they need.

Player Loyalty Dynamics:

Player loyalty is influenced by the ease of engagement and the overall game dynamics.

Rationale:

• Player loyalty evolves based on trading ease and player strategies.

• If trading is easy and players can get what they need, loyalty is likely to increase. However, if trading becomes too difficult, loyalty can decrease.

• Player strategies also play a part. As players adapt to the game and form strategies, their loyalty can be influenced.

• Player Behavior Sensitivity indicates how much player behaviors, like trading, playing time, and interactions, influence the game's economic and loyalty dynamics.

With these dynamics, the model provides a more intuitive and user-friendly representation of the game's economy while still capturing the essential dynamics.

It's designed to be accessible to a broader audience without sacrificing the depth needed to understand and influence the game's economic behavior.

A few weeks ago, I saw this tweet that asked was it possible to build a sustainable P2E model; these were my initial thoughts:

If tokenomics were to be sustainable, the game theoretic economics would have to be structured around three variables:

1. Strategies each player uses and how one player’s strategy affects others

2. Zero-sum game, if you want the consideration that money can’t be created

3. Principle distribution economics to maintain demand/supply directly

   • influenced by deep liquidity pools and

   • strengthened by strong network effects.

The center would, however, revolve around high retention rates, and without a native game token, it would have a higher chance of success.

However, I have already worked around the 4 variables that I discussed above. It's time I tweak the model a little based on the other three variables.

My concerns primarily touch upon the heart of game theory and decentralized systems.

These are my rationale for the new variables and how I intend to integrate them into the model where needed:

Player Strategies: The model's existing dynamics inherently account for player strategies in a broad sense, especially through parameters like "Economic Sensitivity" and "Player Behavior Sensitivity." However, to explicitly model different strategies players might adopt and their effects on others.

• Modification: Introduce a variable Σ(t) (sigma) that represents the prevailing player strategy at time ( t ), which can vary based on player decisions, game updates, or external factors. We can then further adjust the demand and loyalty dynamics to be influenced by Σ(t), reflecting how player strategies affect the game's economy and other players.

• (Sigma) brings in the strategic depth, modeling the various strategies that players might adopt based on game mechanics, other players' actions, and the overall in-game economic environment.

Zero-Sum Game: The idea behind a zero-sum game is that the gain (or loss) of one player is exactly balanced by the losses (or gains) of other players.

• Modification: Ensure that any increase in resources or value for one player corresponds to a decrease for another.

• This can be captured in the "Game Resources Dynamics" and "Resource Value Dynamics."

• This ensures that money or value isn't created out of thin air but is transferred between players.

Principle Distribution Economics:

• Deep Liquidity Pools:The current model considers "Trading Ease" as a representation of liquidity. A deep liquidity pool ensures stability in the game's economy.

  • Modification: We can introduce a constraint on the "Trading Ease Dynamics" to ensure that liquidity remains within certain bounds, reflecting the presence of a deep liquidity pool.

• Strong Network Effects:Network effects mean that the game becomes more valuable as more players join and participate.

  • Modification: We can adjust the "New Players" and "Player Loyalty" dynamics to be influenced by the total number of players or overall game activity. As more players join or stay engaged, the game's attractiveness (and thus acquisition and retention rates) increases.

Now, with the updates, the following dynamics change a bit

Player Loyalty Dynamics:

Rationale:

• This equation captures how player loyalty or retention changes over time.

• The idea is that if trading is easy and seamless (high (L(t)), players are likely more satisfied and engaged, leading to increased loyalty.

• Additionally, the prevailing player strategies ( Σ(t)) also influence retention, suggesting that different strategies can lead to different levels of engagement and loyalty.

• The "Player Behavior Sensitivity" parameter determines how strongly these factors impact loyalty.

Zero-Sum Constraint:

Rationale:

• This equation ensures fairness in the game economy by imposing a constraint that the total value of resources remains constant.

• It captures the principle that while individual resource quantities and values can fluctuate, the overall "wealth" or value in the game remains unchanged.

This final DITE model integrates the key elements discussed, ensuring it captures

• the strategic interactions of players,

• maintains a zero-sum fairness,

• focuses on deep liquidity and network effects, and

• emphasizes high player retention.

Also, remember that this model works for the main assumption that games are better without an in-game token.

Further Thoughts & Analysis

On further analyzing the model, I realized that the zero-sum constraint implies that any decrease in the game's resource should result in a corresponding increase in its value (and vice versa). Therefore, the equations should reflect this relationship.

Given the zero-sum constraint:

If we've defined Game Resource dynamics as:

Then to maintain the zero-sum constraint, Resource Value dynamics should be:

This relationship would mean that as the game resources decrease (due to consumption or other factors), the value of those resources would correspondingly increase. Conversely, as the game resources increase, their value decreases.

However, this modification would simplify the dynamics and would not take into account the influence of demand or the economic sensitivity of the game's economy on the resource's value.

If we want to keep the impact of demand on the resource's value, we'd need to further adjust our model to accommodate both influences (i.e., the natural increase in value due to decreasing resources and the influence of demand).

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While the zero-sum constraint provides a guiding principle to ensure balance in the game's economy, achieving it while considering multiple influences on a resource's value introduces complexity to the modeling process. It's essential to keep iterating on the model and refining it to capture the nuances of the game's economy while adhering to foundational principles like the zero-sum constraint.

The primary reason for the change was to resolve the contradiction between the dynamics of the model and the zero-sum constraint. It was observed that the original dynamics did not satisfy the zero-sum condition, meaning that the total value of resources wouldn't remain constant, violating the foundational premise of the model.

By revising the model, we ensure that it abides by its core principles and it becomes a more accurate representation of the in-game economy. This corrected model will provide more realistic and actionable insights when applied to real-world scenarios.

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With the variable S(t), we are only considering one in-game item, the in-game token.

What if they have NFTs too? Does the DITE model take that into consideration?

Since other in-game assets like NFTs are likely to be traded on decentralized exchanges using native tokens such as ETH, SOL, etc., it may add unnecessary complexity for the DITE model to explicitly represent their dynamics.

These are some reasons why I prefer keeping the model focused on the aggregated core in-game currency/resources:

• Prices of NFTs and other assets on DEXes will be driven by external market forces beyond the game's control. So modeling them may have limited utility.

• The complexities of modeling so many assets can reduce the model's straightforward interpretability and focus.

• Keeping the model simple allows it to be tailored to games with very different asset configurations and valuations.

The aggregated resource flow is likely sufficient to inform strategic tokenomics decisions for sustainability.

Hence, this is the finalized Dynamic Interactive Tokenless Economy (DITE) model.

1. Variables

2. Constraints

This constraint ensures the total value of resources remains constant, reflecting the principle that money can't be created.

3. Dynamics:

                     *Modified after taking the “Zero Sum” constraint into consideration*

Parameters:

1. Economic Sensitivity: How responsive the game's economy is to changes.

2. Player Behavior Sensitivity: How much player behaviors influence game dynamics.

Now, how we determine the variable Σ(t) is a complex task…

…because it involves capturing behavioral patterns in a dynamic gaming environment.

The interconnection of components is a key feature, ensuring that the model doesn't view any variable in isolation but rather appreciates the ripple effects a change in one can have across the system.

There are several approaches to quantify and represent player strategies, but due to limited resources available at hand, I’ll not dive deep into it.

1. Game Analytics:

• In-game analytics tools can track player behaviors. For example:

• Frequency of trades or resource acquisitions.

• Patterns of collaboration or competition with other players.

• Clustering or segmentation algorithms can then categorize players based on their behaviors, leading to defined strategy archetypes.

2. Behavioral Econometrics:

• By analyzing in-game transaction data, econometric models can be built to understand how players respond to changes in resource prices, game updates, or external events.

• These models can provide insights into prevalent strategies and how they evolve over time.

3. Game Theory Analysis: By modeling the game's economy as a game (in the mathematical sense), one can predict equilibrium strategies that players might adopt.

4. Machine Learning:

• Advanced machine learning algorithms can analyze vast amounts of player data to detect patterns and classify strategies.

• Techniques like reinforcement learning can also predict how strategies might evolve in response to changes in the game environment.

It's also important to note that Σ(t) is always meant to be dynamic and will change over time, so continuous monitoring and adaptation are crucial.

If it doesn't change over time, we can infer that the engagement has reached the highest saturation levels.

After we define the variables, assign a number, and then calculate the dynamics with the formulas mentioned in the DITE model, then what?

• How does calculating those dynamics help with transitioning to a tokenless model?

• What practical implications and utilities does the DITE model provide, especially in the context of transitioning to a tokenless model?

Let's break this down step by step.

1. You First Understand the Current State:

Before making any changes to an existing system, you need to understand its current state. The DITE model, through its variables and dynamics, provides a snapshot of the game's economy at any given point in time.

• How much in-game resource is currently in circulation S(t)?

• What's the perceived value of this resource P(t)?

• How is player demand fluctuating D(t)?

• What fraction of your player base remains loyal and engaged R(t)?

Getting quantifiable answers to these questions sets the stage.

2. Simulating Scenarios:

Once you've established your baseline, the DITE model allows you to simulate different scenarios. By adjusting the parameters or initial conditions, you can see how the in-game economy might evolve in response to various interventions or changes.

For instance, if you were to reduce the supply of the in-game resource (mimicking the effect of burning or reducing token issuance),

• how would that impact its value?

• How would it affect player demand or loyalty?

3. Guiding the Transition:

You can realize the true value of the DITE model when considering the transition to a tokenless model. Here are some ways:

• Gradual Transitioning: By observing how drastic changes can destabilize the in-game economy, you'll be guided to make gradual changes.

  • For instance, you might opt for a phased reduction in token dependency rather than an abrupt shift.

• Feedback Loops: The interconnected nature of the DITE model shows how changes in one variable can impact others. This understanding can help in making informed decisions.

  • For example, if reducing token dependency lowers resource value initially, it might be offset by increased player demand or loyalty as the economy stabilizes.

• Player Engagement: If the model indicates declining player loyalty as token dependency reduces, it signals the need for other engagement strategies, perhaps new in-game content, rewards, or mechanics.

4. DITE lets you focus on Iterative Refinement:

Transitioning to a tokenless model isn't a one-time activity. It's an ongoing process of observing, adjusting, and refining.

The DITE model, with its dynamic equations, is designed for this iterative approach:

• Continuous Monitoring: As the game evolves, continuously feed new data into the model to keep it relevant.

• Adaptive Strategies: If an intervention doesn't yield the expected results, adjust your strategy. The DITE model provides the feedback mechanism to understand what's working and what's not.

The DITE model doesn't just provide numbers; it offers insights, guidance, and a structured approach to navigate the complexities of transitioning to a tokenless economy.

By understanding the current state, simulating future scenarios, guiding the transition process, allowing iterative refinements, and facilitating clear communication, the DITE model becomes an invaluable tool for game developers aiming to reduce token dependency in their P2E games.

These types of insights allow game designers to anticipate second-order effects and paint a picture of the transition pathways.

The key is leveraging the model's predictive capabilities by:

1. Establishing baseline behavior under current tokenomics

2. Introducing changes to incentivization, constraints, gameplay, etc.

3. Re-simulating dynamics under these alternate scenarios, and

4. Quantifying and evaluating the projected outcomes

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Now, How would you, as a user, approach DITE?

Here's a step-by-step guide:

First & foremost, Understand the variables. Start by getting acquainted with the primary variables in the model:

• Game Resources: The virtual currency or items in the game.

• Resource Value: How much these resources are worth, either in real-world currency or in terms of in-game utility.

• Player Demand: The total demand for these resources by players.

• Player Loyalty: A measure of player retention and engagement.

• Trading Ease: Indicates how easy it is to trade or convert resources.

• Player Strategies: Represents the prevailing strategies players adopt.

2. Observe Player Demand Trends: If player demand consistently exceeds supply, it might indicate scarcity, which can lead to increased resource value. Conversely, a drop in demand might hint at diminished interest or an overabundance of resources.

3. Engage with Trading Platforms: If the game has a marketplace or trading platform, players can gauge trading ease. A bustling marketplace with quick trades indicates good liquidity, while prolonged trade times or stagnant listings might hint at liquidity issues.

4. Evaluate Strategy Diversity: Note the dominant strategies in the game. If everyone is following the same strategy, it might point to imbalances in-game mechanics. A diverse range of strategies indicates a more balanced and dynamic game environment.

5. Utilize In-Game Analytics Tools (if available): Some games might offer analytics tools or dashboards for players to track economic metrics directly. These tools can provide insights into supply, demand, trading volumes, and other key indicators.

6. Engage with the Community: Players often discuss strategies, share experiences, and voice concerns, which can offer valuable context to the model's quantitative metrics.

7. Run Simulations (for advanced users): If the game provides a sandbox mode or if there are third-party tools available, players can run simulations based on different scenarios. By tweaking parameters, they can predict potential outcomes and adjust their strategies accordingly.

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What is the DITE model centered on?

Based on its design and mechanisms, we can make several inferences about the nature of P2E economies and the potential impact of transitioning away from a token-dependent framework:

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What’s the thesis behind DITE?

The main thesis behind the finalized DITE model is centered on creating a sustainable, balanced, and engaging in-game economy by understanding and managing the intricate interplay of various game dynamics.

• Imposing zero-sum constraints to prevent inflationary imbalances.

• Responding to evolving player strategies through dynamic modeling.

• Maximizing player retention through incentives aligned with engagement.

• Maintaining stability via deep liquidity pools and network effects.

• Decoupling value from volatile crypto markets through off-chain rewards.

• Allowing ongoing refinement as real-world data emerges post-launch.

• Balancing rigor with accessibility for both developers and players.

This thesis addresses the challenges:

• Preventing inflation or deflation that disrupts gameplay over time.

• Ensuring equitable participation regardless of entry time or strategy.

• Maintaining liquidity for healthy in-game trading and transactions.

• Maximizing player retention and engagement critical for long-term success.

Eventually, DITE should not be seen as an exclusively tokenless model…

…but as a tool to strategically reduce dependency on a game's native token over time, you're essentially broadening its application and appeal.

Let DITE:

• identify which in-game assets or activities should remain tied to the token versus shifting rewards to direct real-world value. This allows selective decoupling.

• forecast the transitions in player behaviors and incentives as token importance declines, allowing developers to prepare responses.

The adaptive nature of DITE would allow iterative fine-tuning of the right token/tokenless balance based on live data and testing.

Token burning/sinks could be modeled to take tokens out of circulation in addition to reducing emissions.

The model is flexible enough to support optimization across any point on the tokenization spectrum. The model does not inherently require a solely tokenless end-state to be useful.

Also, the demand for native P2E game tokens tends to decline over time…

…while demand for underlying platform tokens like ETH remains more stable.

• Speculative mania fades after launch, and early adopters cash out proceeds.

• Continual emissions and rewards dilute scarcity promised at launch.

• Newer players lack the profit motivations of early adopters.

• Declining players mean fewer transactions to drive utility and burn.

• Native onchain tokens (ETH, SOL, BNB) hold more stable value due to broader utility and network effects.

Transitioning to reduce dependence on the volatile game token could indeed save many P2E projects from going out of business and would also benefit from that greater stability in demand.

The DITE model could help by simulating the impacts of incrementally reducing native token emissions and shifting payouts to ETH or USD. This would provide data-driven insights to find the right token/platform token balance for sustainability.

There a diverging demand curves for game vs. platform tokens. Using DITE to strategically transition toward the stability of ETH and other broader utility assets makes tremendous sense for P2E longevity. The model can provide the guidance needed to pull off such a transition successfully.

It's a strategic move that can indeed save many P2E games from potential economic pitfalls, ensuring longevity and continued player engagement.

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Appendix

Ap #1

A value between 0.5 and 1.5 for both parameters might be a starting point, but this should be calibrated based on real game data and simulations.

Parameter #1

Economic Sensitivity: Measures how responsive the game's economy is to changes, particularly imbalances between supply and demand.

1. Historical Analysis:

   • Start by examining past data on supply and demand dynamics.

   • Track how much the resource value P(t) changes in response to imbalances in player need D(t) and game resources S(t).

2. Econometric Modeling:

   • Use regression models to determine the strength of the relationship between supply-demand imbalances and resource value changes.

   • This can give you a coefficient that quantifies the economic sensitivity.

3. Trial & Error:

   • Introduce small, controlled changes in the game's economy and observe the resultant changes in resource values.

   • This allows you to gauge how the economy reacts to perturbations.

Assumptions:

• A higher value of economic sensitivity implies that the in-game economy is very responsive to changes.

• A game that wants to provide a stable economic environment might aim for a lower economic sensitivity, ensuring that large imbalances in supply and demand don't drastically shift resource values.

Parameter #2

Player Behavior Sensitivity: Measures how much player actions, behaviors, and strategies influence game dynamics, particularly player loyalty and demand.

1. Historical Player Data Analysis:

   • Examine past data to see how shifts in player strategies or behaviors influenced player loyalty or demand.

   • This could involve looking at the introduction of new strategies or game mechanics and tracking subsequent changes in loyalty.

2. A/B Testing:

   • Introduce certain changes or features to a subset of the player base and observe any shifts in their behavior.

   • Comparing this subset with a control group can give insights into how much players' behaviors are influenced by specific game mechanics or features.

Assumptions:

• A higher value of player behavior sensitivity indicates that players' strategies and actions have a significant impact on game dynamics.

• Games aiming for a dynamic, player-driven economy might embrace a higher behavioral sensitivity, whereas those seeking a more controlled, developer-driven environment might opt for a lower value.

Note: Both these parameters are crucial in tailoring the DITE model to the specific nuances of a game. They should be revisited and recalibrated regularly based on ongoing data collection and game updates to ensure the model remains accurate and relevant.

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Ap #2:

Applying the DITE Model to Axie Infinity: The DITE model can be applied to Axie Infinity as a potential evolution of their economic system. Here’s how it can be approached:

(with the limited resources in hand, I will only outline the steps and will revisit this in detail later on)

1. Calibration of Parameters and Variables:

• Data Gathering: Collect historical and real-time data on game resources, player needs, resource value, player loyalty, trading ease, new player acquisition, and player churn.

• Economic Sensitivity: Observe how Axie Infinity's economy has historically responded to imbalances in player need and game resources. Calculate the relationship strength between these imbalances and changes in resource values (such as the price of Axies or Small Love Potions).

• Player Behavior Sensitivity: Analyze how player strategies, behaviors, and in-game decisions have historically influenced player loyalty and demand. This includes observing how major game updates, introduction of new Axies, or changes in reward structures have influenced player behaviors.

2. Measuring Outcomes and Impacts:

• Simulate Scenarios: Using the calibrated DITE model, run various scenarios simulating potential changes in Axie Infinity. This could include changes in reward structures, introduction of new game mechanics, or shifts in player base.

• Key Metrics to Monitor:

  • Resource Value Stability: Monitor how stable in-game resource values are, especially in response to external market conditions.

  • Player Loyalty: Track retention rates and churn to see if the game maintains its player base.

  • Economic Health: Examine trading volumes, liquidity, and overall economic activity within the game.

3. Communicating Findings and Recommendations:

• To Game Developers:

  • Structured Reports: Provide detailed analyses, including visual charts and graphs, showcasing the model's predictions and outcomes.

  • Regular Meetings: Organize regular touchpoints to discuss the model’s findings, potential game updates, and collaborative adjustments to the model.

  • Feedback Loop: Establish a mechanism for developers to provide feedback on the model's accuracy and relevance, allowing for continuous refinement.

• To Players:

  • Transparent Updates: Use in-game announcements, community forums, and social media to communicate any potential economic changes and the rationale behind them.

  • Interactive Workshops: Host online sessions or webinars where players can learn about the economic model, ask questions, and provide feedback.

  • Feedback Channels: Allow players to provide feedback on the game's economic changes, ensuring they feel involved in the game’s evolution.

Thoughts: Applying the DITE model to Axie Infinity would involve careful calibration based on historical data, continuous measurement of key metrics, and transparent communication to both developers and players.

The primary goal would be to gradually reduce the game's dependency on its native token while ensuring a robust, stable, and player-centric economy. This approach could potentially make Axie Infinity more resilient to external market volatilities and ensure long-term sustainability.

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If you find this deep dive analysis useful, please consider donating to aplusb.eth (Arbitrum, Ethereum, Optimism) and/or by minting an NFT for this & other blog posts by me.

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Previous Research:

1. Decoding & Democratizing Web3

2. P2E: A Shift in Gaming Business Models

3. Stablecoins: Is there hope?

4. If you don't control your data why do you trust it

5. Primer on L2 Scaling Solutions

6. Understanding User Dynamics in DeFi

7. Intro to Lending and Borrowing Mechanics in DeFi

8. Part 2: DeFi Deep Dive on COMP, AAVE, and MKR

9. Best Way to Create Value with Data in Web3

10. Building a Decentralized Climate Finance DAO

11. Org vs. DAOs: Governance & Growth in Modern Society

12. ERC-4337: The Future of Ethereum Token Standards

13. Identity Without Borders: Decoding My Online Identity

14. Web3s 3-Wave Model of Evolution of Complex Systems

15. Understanding Tokenomics: Case Study of dYdX

16. DeFi Hacks Unveiled: What We've Learned from Q2

17. Voting Mechanisms & Incentives for Governance in DAOs

18. Uniswap’s Fee Switch Dilemma

19. MakerDAO's Endgame: 5 Phases and 14 MIPs

20. Liquid Staking Tokens: Can They Bounce Back?

21. Binance Smart Chain: Luban Hard Fork

22. crvUSD: A Stable Alternative?

23. friend.tech: Tokenizing Incentives for "friends"

24. MEV Endgame: Exploring Mempool Privacy Schemes

25. Privacy Pools: Towards Practical Privacy & Compliance with Smart Contracts

26. Rollup Roulette: Deep Dive into Shared Liquidity