The documentation of all prototypes created for this work. All documentations are divided into a general description of the approach and its purpose, an explanation of the technical realization and an evaluation of the collected data created by running multiple tests with one prototype.
7.1.1. Iteration 1: Additional purchases
The first approach that tries to stabilize the economic system of The Elder Scrolls V: Skyrim (Bethesda Game Studios, The Elder Scrolls V: Skyrim, 2011) like the game will be based on additional options of purchasable items for players. These extra items can be bought for high prices and are quickly producible by the developers. This approach can be viewed as a well-planned money sink that allows developers to continuously add items to the game. This has the goal of adding more options than a player is able to obtain, creating a high quantity of desirable items, similar to the real world, which offers more things than most people can obtain. These items will mainly consist of colour selections for items additional skins and consumables and will therefore not lead to further economic growth. But these additional purchases can also be considered totally optional which can result in a selection of players that won’t buy them. Figure 10 shows the prototype for this approach.
Figure 10
This prototype extends the basic economic system of a game like The Elder Scrolls V: Skyrim64 (Bethesda Game Studios, The Elder Scrolls V: Skyrim, 2011) by adding an additional option to spend high amounts of currency on something that has no further impact on the economic system of the prototype. The price for additional items bought this way will increase the amount of currency the player has to spend to obtain them and the likelihood for the player to buy them will be reduced. This symbolizes that items bought utilizing this system will increase in price depending on the amount of currency that must be removed from the game.
Figure 11
If the results of Figure 11, which showcases the results of this approach, are compared to the results of Figure 6, which showcases the results of the base system, it becomes apparent, that this approach is capable of reducing the maximum amount of currency players own as well as reducing the average amount of currency players own. But the differences between the amounts of currency players own compared to each other don’t change much. Because of that can this system considered to be unstable. It is also possible, that some players are less affected by this approach than others. Repeating this test for more users will likely result in some users that aren’t affected at all, leading to even higher differences between players. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration1_ExcelDocumentation.7z.
This approach is only possible for games that are supported over a larger period of time since new items must be added periodically.
7.1.2. Iteration 1: Consumable currency
For this approach, the currency of a game is converted into some form of consumable good. An example of this could be food that is necessary because of a nutrition bar that reduces the players’ health when empty or a currency that can be directly converted into health. An example of this is the bullets from the Metro (4A Games, 2010) series. These bullets are utilized as currency but also as ammunition. This approach allows giving a currency a fixed value based on the value the player gets from utilizing it. Figure 12 shows the prototype for this approach.
Figure 12
This approach adds an additional currency drain to ‘PlayerMoney’. This drain has a random value, but the maximum depends on the progression level of the player and is triggered randomly with a decreasing chance based on the player’s skill level. When used for a real game this system can be adjusted by utilizing levels that offer more currency but also have a higher level of difficulty, creating high risk-high reward areas or simply moments that feel very intense.
Figure 13
If the results of Figure 13, which showcases the results of this approach, are compared to the results of Figure 6, which showcases the results of the base system, it becomes apparent, that this approach is capable of reducing the maximum amount of currency players own as well as reducing the average amount of currency players own. But the differences between the amounts of currency players have compared to each other don’t change much. Because of that can this system considered to be unstable. This is because players are only inclined to consume currency if the game becomes too difficult. But difficulty depends heavily on the players’ level of skill. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration1_ExcelDocumentation.7z.
7.1.3. Iteration 1: Fixed enemy count
In this approach the number of enemies is fixed or limited, this means that enemies won’t respawn or only respawn a limited number of times. This limits the amount of currency and items the player can obtain within the game world. Utilizing this approach creates a natural maximum of obtainable goods for each area of the game. This makes it easier to predict how much currency a player has at a certain point of progress. Because of that prices in different areas can be adjusted accordingly. Figure 14 shows the prototype for this approach.
Figure 14
For this approach, the infinite sources for currency and items have been replaced with finite pools of money and items. The only infinite source remaining is the currency the NPC merchants have access to. In addition to that the converters have been replaced with traders to make sure, that currency and items are correctly reallocated to the right pools.
Systems like this prototype can run into the problem that all the currency and items are removed from the game or a certain area in the game. This creates a fail-state in which the player can no longer continue playing the game.
Figure 15
Comparing Figure 15, the results of this approach, to Figure 6, the results of the base system, it becomes apparent, that this approach has no effect on the stability of the economic system at all. It allows for more control over the maximum amount of currency a player can have at a certain point of progression, but has no impact on any amount of currency that is below that maximum. This in addition to the previously mentioned problem of the creation of a fail state that stops the player from playing the game leads to this approach getting no further iterations. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration1_ExcelDocumentation.7z.
Considering this fail-state and its limited effectiveness. It becomes safe to assume that this approach should be limited to games that allow the player to restart every time they lose, similar to roguelikes. Because of its limited effect, it is also recommended that this approach should have an additional purpose rather than fixing a game’s economic structure. Examples of this could be: Incentivising the player to move to new areas or allowing the player to remember previous events when returning to an area.
7.1.4. Iteration 1: Fixed valuables count
For this approach, all valuables the game has are fixed. This means that all NPC merchants have a fixed amount of currency and items. All crates, chests, herbs, etc. found within the game world can only be looted once and enemies won’t respawn or at least won’t drop additional loot after being killed once. This is, unless they take the loot from a fixed pool that can only refill when the player consumes items. Because of this the maximum amount of currency is fixed and the game economy becomes more predictable for the same reasons mentioned in ‘Iteration 1: Fixed enemy count’. Figure 16 shows the prototype for this approach.
Figure 16
For this approach, all infinite sources for currency and items have been replaced with finite pools of money and items. In addition to that the converters have been replaced with traders to make sure, that currency and items are correctly reallocated to the right pools.
Similar to the prototype in ‘Iteration 1: Fixed enemy count’ can this system run into the problem that all the currency and items are removed from the game or a certain area in the game, which creates a fail state in which the player can no longer continue to play.
Figure 17
Similar to ‘Iteration 1: Fixed enemy count’ Comparing Figure 17, the results of this approach, to Figure 6, the results of the base system, shows that this approach has no effect on the stability of the economic system at all. It allows for more control over the maximum amount of currency a player can own at a certain point of progression but has no effect below that maximum. This in addition to the previously mentioned problem of the creation of a fail state that stops the player from playing the game leads to this approach getting no further iterations. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration1_ExcelDocumentation.7z.
Similar to ‘Iteration 1: Fixed enemy count’ if one considers the fail state and the limited effectiveness of this approach, it becomes clear that it should be limited to games that allow the player to restart every time they lose. Because of its limited effect, it is also recommended that this approach should have an additional purpose rather than fixing a game’s economic structure. Examples of this could be: Incentivising the player to move to new areas or allowing the player to remember previous events when returning to an area.
7.1.5. Iteration 1: Scaling costs
In this approach, the costs the player can expect during the game scale with the amount of currency the player owns. Because of this gaining more currency becomes harder depending on the amount of currency the player already owns. This creates a negative feedback loop (Salen & Zimmerman, 2004, p. Chapter 18) for obtaining currency for any specific value the economic system is balanced for. Figure 18 shows the prototype for this approach.
Figure 18
In this prototype, the amount of currency the player loses from random events in ‘MoneyLoss’ and the amount of currency the player obtains or loses from selling and buying in ‘BuyItem’ and ‘SellItem’ items scales negatively with the current amount of currency the player owns.
Figure 19
Comparing the values from Figure 19 to Figure 6 it becomes apparent that this approach effectively increases the stability of the economic system of the game, since the different values for the currency don’t fluctuate much between the different playthroughs and the max amount of currency is limited to a more stable amount. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration1_ExcelDocumentation.7z.
This approach is a good start for the creation of a stable economic system but is likely that it has a more mental limitations. This is because of the expectation that players will not enjoy the basic concept of this approach since directly increasing prices based on the amount of currency a player owns can feel very unfair or undesirable. But this approach can also be achieved in another way. For example, players that have a lot of currency will be attacked by bandits more often. Now even if a player manages to defeat the bandits every time without losing the owned currency, it is likely, those extra consumables are used during the fights, which effectively removes valuables from the player. Another way to properly include this approach into a game would be by utilizing taxes or upkeep costs that scale with a player’s possessions.
7.1.6. Iteration 1: Stop scaling gains
In this approach, the increasing amount of valuables the player is able to obtain is limited at a certain point. This caps the amount of income a player can generate. Figure 20 shows the prototype for this approach.
Figure 20
In this prototype the maximum amount of ‘ItemRewards’ income is capped to 60%, the maximum amount of ‘MoneyRewards’ is caped to 40% and the maximum ‘Player Level’ is caped to 251. This reduces the maximum income a player can generate.
In a game this effect could be achieved by for example: Keeping the amount of value in loot on enemies the same over multiple levels, which can be achieved by reducing the value of loot on most enemies and allowing a small percentage to drop very valuable items.
Figure 21
Comparing the values from Figure 21 to Figure 6 it becomes apparent that this approach effectively reduces the overall fluctuation between currency values, but comes with the disadvantage that it features several playthroughs with extreme cases. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration1_ExcelDocumentation.7z.
This approach shows great theoretical potential since it should be possible to stop the scaling of gains within specific progression levels, allowing the designer to create a stable system that fits the offers of NPC merchants for certain areas of the game. But the problem with this system is, that it is hard to predict at which point the scaling of the economic system of the game should stop to create interesting prices for the NPC-merchants’ offers at the players’ progression level. Because this not automatically adjusting the nature of this approach, further, development is necessary. But it should be mentioned, that this approach is a great addition to other approaches for the creation of a stable economic system and will be utilized in this way in upcoming prototypes.
7.1.7. Iteration 2: Additional purchases with a higher chance based on game progression
In this iteration, the chance that the player purchases the additionally added items increases over the progression of the game. This has the intention to make this approach more reliable. Figure 22 shows the prototype for this approach.
Figure 22
For this iteration, the ‘ArtificialPlayer’ has been altered in a way that increases the progression level and also increases the chance that ‘AdditionalContentSpendings’ will be triggered. Since the system makes it less likely that ‘AdditionalContentSpendings’ will be triggered depending on how much currency the player has already spent on it and more likely that ‘AdditionalContentSpendings’ will be triggered depending on how much currency the player has still exists, is this just an additional element to allow to balance how often a player spends currency on additional items.
In a real game, the likelihood of a player spending currency on additional items could be increased by adding more options for the player to choose from. But different players have different preferences which make it difficult to predict what is necessary to increase the chance for a player to purchase any of the additional items.
Figure 23
Comparing the values of Figure 23 to Figure 11 shows that this iteration has no large-scale effect compared to the first iteration. However, it should be noted that the total amount of extreme cases has been reduced. Because of the difficulty to determine how the chances for a player to purchase any additional items can be increased and the minuscule effectiveness of this iteration will this approach not receive any additional iterations. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration2_ExcelDocumentation.7z.
The use cases for this iteration are the same as the use cases of the first iteration.
7.1.8. Iteration 2: Consumable currency with scaling difficulty based on currency amount
For this iteration, the difficulty of the game changes based on the amount of currency the player has access to. This works well in combination with consumable currency since the player is able to use it if the game gets too hard to overcome the challenge, which then reduces the amount of currency the player has and with that the difficulty of the next encounters. This system can easily be explained to the player in the ways mentioned in ‘Iteration 1: Scaling costs’. This iteration has the intention to make this approach more reliable. Figure 24 shows the prototype for this approach.
Figure 24
In this iteration, the chance for the player to trigger ‘ConsumeMoney’ is increased based on the amount of currency in ‘PlayerMoney’.
Figure 25
Comparing the values of Figure 25 to Figure 13 shows that the average between the players that have stable amounts of currencies has not changed much. But the strength and amount of extreme cases are reduced by a large number. This comes from the now-introduced negative feedback loop (Salen & Zimmerman, 2004, p. Chapter 18) for the amount of currency a player has access to. As an added bonus, This approach is capable of automatically adjusting its difficulty depending on the amount of currency the players are forced to use. This passively increases the difficulty of the game for skilled players. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration2_ExcelDocumentation.7z.
The use cases for this iteration are the same as the first iteration with the limitation that the game must be able to increase its difficulty curve adaptively.
7.1.9. Iteration 2: Scaling costs and reverse scaling gains
For this iteration, an additional scaling that allows the player to receive more currency if the total amount of currency is low has been added to the prototype. The objective for this is to create a double negative feedback loop (Salen & Zimmerman, 2004, p. Chapter 18) for having too much or too little currency. This has the goal of giving the player access to a specific amount of currency most of the time. Figure 26 shows the prototype for this approach.
Figure 26
An additional way of obtaining income has been created in the form of ‘ExtraRewards’ the income created from this source is reduced dependence on the amount of currency the player has in ‘PlayerMoney’.
In a game, this form of income is hard to explain without removing the power fantasy from the player, but maybe that is exactly what it could be used for to create a stronger contrast between success and failure in games.
Figure 27
Comparing the values of Figure 27 with Figure 19, it becomes apparent that the overall stability increase of this iteration compared to the first iteration is fairly similar, but the differences between the playthroughs have been reduced to stable values. This makes this a promising approach for the creation of a stable economic system for games. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration2_ExcelDocumentation.7z.
The use cases for this iteration are the same as the first iteration, but with the added limitation that it is also necessary to explain that having less currency leads to gaining more currency. If the game is able to properly do so this system should be a good fit to create a stable economic system.
7.1.10. Iteration 2: Scaling costs and stop scaling gains
For this iteration the approaches scaling cost and stop scaling gains have been combined, to create a stable economic system that can be adjusted to specific levels of progression. This iteration has the goal of creating a more stable economic system in the way ‘Iteration 1: Scaling costs’ has already demonstrated, while also reducing the amount and strengths of extreme cases by limiting the amount of income the player can generate at specific levels of progression. Figure 28 shows the prototype for this approach.
Figure 28
For this iteration all changes of ‘Iteration 1: Scaling costs’ and ‘Iteration 1: Stop scaling gains’ have been combined.
Figure 29
Comparing Figure 29 to Figure 19 and Figure 21 it becomes clear that this iteration is success full in reaching its goal of combining the stability increase from ‘Iteration 1: Scaling costs’ with the reduction of extreme cases from ‘Iteration 1: Stop scaling gains’. Since this confirms the effect that ‘Iteration 1: Stop scaling gains’ has on other approaches, This system will not receive any further iterations but is instead combined with other approaches. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration2_ExcelDocumentation.7z.
This iteration is limited to games that can support ‘Iteration 1: Scaling costs’ and ‘Iteration 1: Stop scaling gains’. It also showcases that ‘Iteration 1: Stop scaling gains’ can be utilized for any game that supports its implementation to reduce the number of extreme cases. In addition to that, it appears, that ‘Iteration 1: Stop scaling gains’ is heavily improved when combined with another way that stabilizes the games’ economy based on its current amount of destabilization. This is likely because it is almost impossible to guarantee that all forms of gains are accounted for or balanced correctly.
7.1.11. Iteration 3: Consumable currency with scaling difficulty based on currency amount while also reducing the difficulty under a certain amount of currency
In this iteration ‘Iteration 2: Consumable currency with scaling difficulty based on currency amount’ gets further developed by also reducing the difficulty of the game while the player owns less than a certain amount of currency. This has the goal of allowing the player to get to a certain amount of currency safely and by doing so concentrating the usual amount of currency owned by the player to a specific amount. In addition to this, the strength of the effect that pushes a player to consume currency is increased. This has the goal of reducing fluctuations between the average amounts of currency a player has access to across multiple playthroughs. Figure 30 shows the prototype for this approach.
Figure 30
For this iteration, the player has access to an amount of starting currency in ‘PlayerMoney’ and all implementations of difficulty scaling depending on currency work in reverse if the player has less than the amount of starting currency. In addition to that the strength with which ‘PlayerMoney’ affects ‘ConsumeMoney’ has been increased to better understand the strength that this handle gives to the designer.
Figure 31
Comparing the values of Figure 31 to Figure 25 shows that this approach has increased the average amount of currency a player owns while also reducing the maximum amount of currency a player owns. Indicating that increasing and reducing the difficulty based on the amount of currency a player owns, in an economic system that utilizes this approach, does allow control of the average amount of currency on a player towards a specific value. This iteration also proves that increasing the effectiveness of the scaling does little to temper the averages but heavily affects the maximum values. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration3_ExcelDocumentation.7z.
The use cases of this approach haven’t changed from ‘Iteration 2: Consumable currency with scaling difficulty based on currency amount’.
7.1.12. Iteration 3: Scaling costs and reverse scaling gains while also stopping the scaling of gains on a specific level of progression
In this iteration ‘Iteration 2: Scaling costs and reverse scaling gains’ is combined with ‘Iteration 1: Stop scaling gains’, to create a stable economic system that can be adjusted to specific levels of progression. This iteration has the goal of creating a more stable economic system in the way ‘Iteration 2: Scaling costs and reverses scaling gains’ has already demonstrated, while also reducing the amount and strengths of extreme cases by limiting the amount of income the player can generate at specific levels of progression. In Figure 32 the prototype for this approach can be seen.
Figure 32
For this iteration, all changes of ‘Iteration 2: Scaling costs and reverse scaling gains’ and ‘Iteration 1: Stop scaling gains’ have been combined.
Figure 33
Comparing Figure 33 to Figure 27 and Figure 21 it becomes clear that this iteration is success full in reaching its goal of combining the stability increase from ‘Iteration 2: Scaling costs and reverse scaling gains’ with the reduction of extreme cases from ‘Iteration 1: Stop scaling gains’. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration3_ExcelDocumentation.7z.
This iteration is limited to games that can support ‘Iteration 2: Scaling costs and reverse scaling gains’ and ‘Iteration 1: Stop scaling gains’.
7.1.13. Iteration 4: Consumable currency with scaling difficulty based on currency amount while also reducing the difficulty under a certain amount of currency and stop scaling currency
For this approach ‘Iteration 3: Consumable currency with scaling difficulty based on currency amount while also reducing the difficulty under a certain amount of currency’ and ‘Iteration 1: Stop scaling gains’ have been combined, with the goal of combining the stability increase of ‘Iteration 3: Consumable currency with scaling difficulty based on currency amount while also reducing the difficulty under a certain amount of currency’ with the capability of reducing the amount and strength of extreme cases ‘Iteration 1: Stop scaling gains’ offers. Figure 34 shows the prototype for this approach.
Figure 34
For this iteration, all changes of ‘Iteration 3: Consumable currency with scaling difficulty based on currency amount while also reducing the difficulty under a certain amount of currency’ and ‘Iteration 1: Stop scaling gains’ have been combined.
Figure 35
Comparing Figure 35 to Figure 31 the main difference is that the overall amount of currency and items got scaled down, but the number and strength of extreme cases have not changed much. The reason for this could be that ‘Iteration 3: Consumable currency with scaling difficulty based on currency amount while also reducing the difficulty under a certain amount of currency’ did already handle extreme cases well. The overall result of this approach is that it works well for keeping the maximum and average amount of currency and items the player has access to within a specific area while offering enough handles to the designer to properly define that area. But within that area, the maximum and average amount of currency and items can fluctuate heavily between different players. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration4_ExcelDocumentation.7z.
Since how much this approach influences the amount of currency and items the player gets and needs during a playthrough can be controlled very precisely by increasing and reducing the effects of the negative feedback for having low and high amounts of currency and items, should this approach work for any game that fits the requirements of ‘Iteration 3: Consumable currency with scaling difficulty based on currency amount while also reducing the difficulty under a certain amount of currency’ and ‘Iteration 1: Stop scaling gains’. That being said it is recommended that this approach is utilized for games that allow the player to remain effective independent of the skill level. This approach is best used for the creation of economic boundaries in which the player can operate freely.
7.1.14. Iteration 4: Scaling costs and reverse scaling gains while also stopping the scaling of gains on a specific level of progression with extension to items
This iteration extends on ‘Iteration 3: Scaling costs and reverses scaling gains while also stopping the scaling of gains on a specific level of progression’ by also adding a negative scaling to low amounts of items. This has the goal of further refining the already functional ‘Iteration 3: Scaling costs and reverse scaling gains while also stopping the scaling of gains on a specific level of progression’ by reducing problems with the trading system. In Figure 36 the prototype for this approach can be seen.
Figure 36
For this iteration ‘ExtraItems’ have been added to allow the player to obtain more items when the amount of owned items is low.
Figure 37
Comparing Figure 37 to Figure 33 the expected result that this iteration has not much effect on the average and maximum amounts of currency but instead creates more handles to control the average and maximum amounts of items a player has access to, gets showcased. This approach is a great starting point for the creation of predictable economic systems for games. More detailed results can be found at https://philippstenger.com/wp-content/uploads/2019/02/Iteration4_ExcelDocumentation.7z.
Since how much this approach influences the amount of currency and items the player gets and needs during a playthrough can be controlled very precisely by increasing and reducing the effects of the negative feedback for having low and high amounts of currency and items, should this approach work for any game that fits the requirements of ‘Iteration 3: Scaling costs and reverse scaling gains while also stopping the scaling of gains on a specific level of progression’ as long the requirements can also be implemented for items.
7.2. Generalizing the evaluation of all prototypes
The results for all prototypes can also be found at https://philippstenger.com/wp-content/uploads/2019/02/EconomySystems_ExcelDocumentation.7z. Based on these results it can be said, that handing more options to spend currency reduces the maximum amount of currency most players have, but this has only a small effect on the number and strength of extreme cases. Increasing the incentives for the player to utilize the options will increase the percentage of players that get affected by this approach and increase the overall economic stability of the affected players. That being said it is not controllable how many players will utilize the additional options to spend currency. The prototypes for ‘Iteration 1: Additional purchases’ and ‘Iteration 2: Additional purchases with a higher chance based on game progression’ showcase this.
Limiting the total amount of currency and or items of the game shows no effect on the rate with which the player obtains and spends currency, but it does allow for more control about at which point during the playthrough the player is able to obtain a certain amount of currency and or items. That being said these approaches can lead to a situation in which the player has obtained all the currency and items the game has to offer, which can be problematic for many games. The prototypes for ‘Iteration 1: Fixed enemy count’ and ‘Iteration 1: Fixed valuables count’ showcase this.
The other approaches focus on the creation of a negative feedback loop within the game world by adjusting the income and spending of the player depending on the amount of currency owned. These approaches create a small-scale version of the effects that inflation has in the real world and how economic entities adjust to them. The overall result of these approaches was that they increased the stability of the system by creating a stable average and maximum value of a currency and items a player has access to. But it can be hard to explain these approaches logically in the game world since players are not used to increasing prices just because they have access to more currency.
The Prototypes can be found at: https://philippstenger.com/wp-content/uploads/2019/02/EconomySystems_MachinationsPrototypes.7z.
