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Outline

Title
Abstract
Figures
Introduction
Literature Review
Computer Games Design Approaches
Research Methodology
Game Theory for Computer Games Design
Research Results
Adapted Decision Trees for Computer Games Design
Conclusion
References
All Topics
Sociology
Communication

Game Theory for Computer Games Design

Profile image of Denis ReillyDenis Reilly

Games and Culture

https://doi.org/10.1177/1555412017740497
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15 pages

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Abstract

Designing and developing computer games can be a complex activity that may involve professionals from a variety of disciplines. In this article, we examine the use of game theory for supporting the design of gameplay within the different sections of a computer game and demonstrate its application in practice via adapted high-level decision trees for modeling the flow in gameplay and payoff matrices for modeling skill or challenge levels.

Figures (3)
Figure 1. Example of adapted decision tree for computer game design. represents the design of a simple example of a very small segment of a hypothetical game scenario. This shows the game objects with which the player may interact, and the nature of the interaction in terms of (in this instance) how fast and how accurately the game characters (Opponent 1 and Opponent 2) will respond.
Figure 1. Example of adapted decision tree for computer game design. represents the design of a simple example of a very small segment of a hypothetical game scenario. This shows the game objects with which the player may interact, and the nature of the interaction in terms of (in this instance) how fast and how accurately the game characters (Opponent 1 and Opponent 2) will respond.
proposed by a game designer, in terms of the variables against which the outcomes will be measured, and initial estimates of the likely outcome probabilities. However, in order to achieve appropriate gameplay experience for a variety of game players, it would be practicable to undertake experimentation in terms of adjustments of the variables involved and detailed playtesting, in order to ensure that the payoff matrices represent a statistically representative assessment of the gameplay. Figure 2 shows a simplified payoff matrix that could be proposed by a game designer to determine what skill level (or level of challenge) would be required with regard to the game play involving either of the two software based opponents (or players) from Figure 1. In terms of the skill level or challenge level of the different sections of a computer game, the payoff matrices could be used to apply the concept of the Nash equilibrium (Nash, 1951) in which the player (and the software based opponent player) would be assumed to be aware of the equilibrium strategy of the other player, and neither player would have anything to gain by changing strategy. An equilibrium state could represent an “average” game player. Novice, and skilled player levels could then be set in terms of, for example, the speed and accuracy of the player inputs that would lead to outcomes more favourable or less favourable to the player.
proposed by a game designer, in terms of the variables against which the outcomes will be measured, and initial estimates of the likely outcome probabilities. However, in order to achieve appropriate gameplay experience for a variety of game players, it would be practicable to undertake experimentation in terms of adjustments of the variables involved and detailed playtesting, in order to ensure that the payoff matrices represent a statistically representative assessment of the gameplay. Figure 2 shows a simplified payoff matrix that could be proposed by a game designer to determine what skill level (or level of challenge) would be required with regard to the game play involving either of the two software based opponents (or players) from Figure 1. In terms of the skill level or challenge level of the different sections of a computer game, the payoff matrices could be used to apply the concept of the Nash equilibrium (Nash, 1951) in which the player (and the software based opponent player) would be assumed to be aware of the equilibrium strategy of the other player, and neither player would have anything to gain by changing strategy. An equilibrium state could represent an “average” game player. Novice, and skilled player levels could then be set in terms of, for example, the speed and accuracy of the player inputs that would lead to outcomes more favourable or less favourable to the player.
Figure 3. Simple example payoff matrix for the skill or challenge level of a sectior within a computer game.
Figure 3. Simple example payoff matrix for the skill or challenge level of a sectior within a computer game.

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