Recent gaming history has been littered with examples of triple-A games being released well before they are ready.

Huge releases from major studios and developers such as Anthem, Cyberpunk 2077, and Fallout 76 have all been released to the public as buggy or highly unbalanced messes.

These releases were followed by the predictable fallout as the gaming community expressed their disappointment and anger.

While CD Projekt Red or Bethesda might be able to ride out releasing a disappointing game and rely on huge marketing efforts to recoup their profits through pre-orders and day one purchases. The same cannot be said for freemium mobile game designers.

If you’re relying on your game’s internal economy to create revenue, then it has to work smoothly and effectively on day one.

With so many other freemium mobile games on the market, releasing a poorly balanced game is an excellent way to lose your player base in quick order.

If you’re a new game developer, you might also tarnish your reputation to the point where the marketing efforts for future games are going to be much harder.

There is, however, a way to ensure that the economy for your base game and any ongoing content works first time, every time.

You make simulations a core part of your game design process.

The 4 Primary Problems of Designing for the Freemium Model

Before we get into how using game simulations can solve some of the issues of designing an in-game economy, we first need to understand what the primary problems are when designing for the freemium model.

1. Designing for long-tail earnings

The primary design ethos when it comes to freemium game design is that players need to stay engaged with the game over the long term.

This long-term engagement, coupled with the actions of the in-game economy, is what creates the constant low-level stream of in-game purchases that most freemium games rely on for revenue.

In order to keep players engaged over the long term, the game needs to create a sense of progression. The players always need to have something they are working towards to keep them coming back to the game.

This sense of progression is what drives players to invest in in-game purchases in order to accelerate their movement through the game.

At the same time, the ability to shortcut the game’s mechanics through in-app purchases adds another layer of complexity to the overall design, as it has to encompass both players who embrace the grind and those who are happy to pay for fast advancement.

2. Content Pipeline

Unlike traditional video games that offer, replayability aside, a set experience in return for an upfront cost. Freemium games constantly need to be introducing new content in order to keep players engaged.

This comes with its own challenges.

Not only is producing additional content expensive and time-consuming, having to be weighed against the online cost of maintaining a live game, but it also has to mesh seamlessly with existing content.

New content constantly needs to be produced that adds to the game’s existing economy without breaking it for either players who have been investing in the game for the long term, or those who are just starting out.

3. The Complexity of Game Economies

Game balance is far more important in games created for the freemium model than it is in other games, as it directly translates into studio earnings.

The economy is generally the backbone and the driving force of most freemium games, generating revenue and keeping players engaged by creating a feeling of constant forward motion.

Almost every in-game economy is balanced around the parts of the game that provide resources and opportunities to spend those resources.

The sweet spot most game developers aim for is a balance between resource scarcity, which makes in-game purchases to speed advance more attractive, and the player’s desire for whatever those resources can be spent on.

If the game provides too many resources, then the player will get bored because the lack of scarcity removes challenge and reward from the game.

If there are too few resources, the player becomes frustrated and unengaged because of a lack of perceived forward progression in the game.

Because of the complexity of juggling multiple currencies, multiple different types of resources, game events, player choices, and the impact of in-game purchases, freemium economies are hugely complicated and connected to every aspect of the game.

This gives developers far more opportunities to make mistakes and, if those mistakes unbalance the game’s economy, it can negatively impact every aspect of the game.

As players lose interest in the game, revenue starts to dwindle and increasing negative reception cuts down on the number of new players, giving the game’s design team fewer resources to work with, creating a dangerous downward spiral.

Taken together, the need to constantly add new and carefully balanced content to keep players engaged, and therefore make more revenue, coupled with the budgetary and time constraints of making that content, make it very hard to conduct proper testing using traditional methods.

4. The Issue With Traditional Testing Methods

When it comes to testing games built on the freemium model, there are three primary drawbacks to using traditional game testing methods.

● Testing the game by playing through game mechanics is hugely time-consuming and we have already mentioned the issues associated with budgetary and time constraints common to mobile game development.

● Freemium game economy models are often hugely complex and abstract with a large number of interlinked operations. This makes them far more difficult to test than say, a menu function or a particular weapon.

● Since the game economy is both the backbone of most freemium games and the primary source of revenue, it benefits the entire game design process for testing to begin early, allowing for continuous small iteration, rather than trying to make big changes later in the development.

How Running Simulations Can Solve These Issues

Using a programmed simulation to test the in-game economic mechanics can help to sidestep some of the issues associated with traditional testing methods.

Since simulation is a tool encoded with the specific mechanic that needs to be tested, rather than the game itself, there’s no need to play through the game’s core mechanics while testing.

This can reduce testing times from a matter of weeks to a matter of hours or days, which is hugely important given the shorter development times of most mobile games.

Because simulations are able to test mechanics outside of the game itself, they don’t require other facets of the game to be working.

This means the testing process for the basic mechanics of an in-game economy can be tested as early as possible, while other parts of the game are still being developed.

Because of the complexity of freemium game mechanics, it can be difficult to do testing through whiteboard meetings, presentations, roundtable discussions or even high-level prototypes.

But one of the most important aspects of freemium games is building an experience that can keep players engaged for years, and that allows for deep monetization. Programmed simulations can help you forecast player experience for the period of time of your choosing – like months or years, whereas playtesting cannot.

Monte Carlo Simulations and the Importance of Randomness

As with any complex system, there is much more to a game than the sum of its parts. There are games that are famous for generating enormous depth of play with relatively simple elements or rules. Think of a game of Tic Tac Toe. Its 4 simple rules generate more than 255 thousand combinations that can be played. The idea here is, that the complexity of a game is not only given by how complex the individual parts/systems of said game are, but it’s ultimately the result of the many interactions between its parts. The complexity of each individual part only adds up (most probably exponentially) to the complexity of the result. This is called emergence, and it’s key to understanding why you should simulate early in your design process and the importance of randomness in the equation.

When it comes to creating a simulation to represent your game, it’s important not to simply rely on mathematically created averages.

The reality is that simple averages often fail to predict the behavior of players because of the lack of randomness.

Players, unfortunately, don’t interact with your game’s mechanics in the mathematically optimal pattern, as personal preferences, role-playing choices, favorite characters, and other biases skew their decision-making.

The good news is that a combination of Monte Carlo simulations and the Law of Large Numbers represent a simple solution to the issue of adding randomness back into your simulations.

What Is a Monte Carlo Simulation

A Monte Carlo simulation, or the Monte Carlo method, describes a computerized mathematical technique that builds models of possible outcomes by substituting a range of values for any factor that has inherent uncertainty.

This is useful for game designers because it allows them to understand the probability of different outcomes in a process that includes a significant amount of random variables.

The name of the ‘Monte Carlo simulation’ is a reference to the famous casino in the Principality of Monaco and reflects the method’s usefulness for predicting the probability of inherently random gambling games, such as roulette.

The most common example of a Monte Carlo Simulation is calculating the probability of rolling a set number on two dice.

The Law of Large Numbers

Monte Carlo methods use the process of repeated random sampling to make numerical estimates of unknown parameters. The basis for Monte Carlo simulations is the Law of Large Numbers, which states that:

“The average of the results obtained from a large number of trials should be close to the expected value and will tend to become closer to the expected value as more trials are performed.”

So the more simulations you run on your parameters, the accurate the result.

What Are the Benefits of Simulating Your Game?

● Rather than traditional testing that requires large numbers of human testers to play the game multiple times, simulation testing can be done without the core game being playable. This means that testing can begin early, while other sections of the game are still being worked on and can be done much faster (days or hours rather than weeks) than traditional testing.

● Monte Carlo simulations and the Law of Large Numbers mean that simulations can account for the variability introduced by a real-life player base and produce more accurate results than simple probability. This allows designers to balance the game for different types of players over a longer period of time. The added accuracy also makes it far easier to balance the in-game economy, allowing designers to test content to make sure that it doesn’t disrupt the balance of taps and sinks.

● The speed of simulation testing both cuts down on production time and allows more intensive testing to be done before the game is brought to market. Additionally, most freemium games require constant injections of new content on a regular basis to keep players engaged. Because of the speed at which simulation testing can be run, it is better placed to keep up with the constant need to produce new, but balanced, content.

● Simulation testing is cost-effective. You can use machinations.io to simulate the outcome of your games with one click of a button. You can also run Monte Carlo simulations in Microsoft Excel, or code your own Python script, but that requires a lot more work on your part. Additionally, you don’t need to arrange large public beta tests, pull design staff away from other jobs or pay for testers in order to test your game.

How Machinations Can Help You Balance Your Game

Used by more than 2500 game studios all over the world, Machinations lets you do away with the outdated spreadsheet and design your game in interactive diagrams using a standardized, visual language.

With a hugely complex underpinning but a simple and accessible visual interface, Machinations lets you present complex and abstract concepts, such as the design of in-game economies, with ease, making sure your whole team is on the same page.

As part of Machinations’ simulation functionality, you can conduct Monte Carlo simulations, setting your own parameters, that include randomness into the equation and give you results that are far closer to real-life than basic averages. You can compare different types of player journeys and iterate mechanics in minutes.

Machinations makes it easy to balance your game economy in a manner that keeps your players engaged over the long term, giving you access to that all-important long-tail revenue.

In the video below, Matthew Morris and Cezar Cocias look at how Machinations can be used to test progression systems for different player profiles and play styles, ranging from casual players to your biggest whales. They are joined by Jeffrey Feenstra, LiveOps specialist @ PopReach who will go over how he uses Machinations to optimize existing games and improve their performance and returns.

Key takeaways:

How to use Machinations to simulate different player choices and play styles
Adjust the game balance so that all types of players get a great experience
How to use Registers to manipulate randomness and simulate different player responses
How to use Machinations to optimize live game balance and returns

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