Game Mechanics is aimed at game design students and industry professionals who want to improve their understanding of how to design, build, and test the mechanics of a game. Game Mechanics will show you how to design, test, and tune the core mechanics of a game—any game, from a huge role-playing game to a casual mobile phone game to a board game. Along the way, we’ll use many examples from real games that you may know: Pac-Man, Monopoly, Civilization, StarCraft II, and others. The authors provide two features. One is a tool called Machinations that can be used to visualize and simulate game mechanics on your own computer, without writing any code or using a spreadsheet. The other is a design pattern library, including the deep structures of game economies that generate challenge and many kinds of feedback loops.
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This paper discusses the potential application of procedural content generation to a game about economical crises, intended to teach a large general audience about how banks function within a market-guided economy, and the financial risks and moral dilemmas that are involved. Procedurally generating content for abstract and complex notions such as inflation, market crashes, and market flux is different from generating spatial maps or physical assets in a game, and poses several design challenges. Instead of generating these kinds of phenomena and other macro-economic effects directly, the designers aim to let them emerge from automatically generated game mechanics. The game mechanics we propose include generic business models that can be parameterized to model the behavior of companies in the game, while the player controls the actions of a bank. What makes generating these game mechanics particularly challenging is the interaction between phenomena at different levels of abstraction. Therefore, relevant economic concepts are discussed in terms of design challenges, and how they could be modeled as emergent properties using generative methods.
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This paper presents the latest version of the Machinations framework. This framework uses diagrams to represent the flow of tangible and abstract resources through a game. This flow represents the mechanics that make up a game’s interbal economy and has a large impact on the emergent gameplay of most simulation games, strategy games and board games. This paper shows how Machinations diagrams can be used simulate and balance games before they are built.
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Video game designers iteratively improve player experience by play testing game software and adjusting its design. Deciding how to improve gameplay is difficult and time-consuming because designers lack an effective means for exploring decision alternatives and modifying a game’s mechanics. We aim to improve designer productivity and game quality by providing tools that speed-up the game design process. In particular, we wish to learn how patterns en- coding common game design knowledge can help to improve design tools. Micro-Machinations (MM) is a language and software library that enables game designers to modify a game’s mechanics at run-time. We propose a pattern-based approach for leveraging high-level design knowledge and facilitating the game design process with a game design assistant. We present the Mechanics Pattern Language (MPL) for encoding common MM structures and design intent, and a Mechanics Design Assistant (MeDeA) for analyzing, explaining and understanding existing mechanics, and generating, filtering, exploring and applying design alternatives for modifying mechanics. We implement MPL and MeDeA using the meta-programming language Rascal, and evaluate them by modifying the mechanics of a prototype of Johnny Jetstream, a 2D shooter developed at IC3D Media.
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In early game development phases game designers adjust game rules in a rapid, iterative and flexible way. In later phases, when software prototypes are available, play testing provides more detailed feedback about player experience. More often than not, the realized and the intended gameplay emerging from game software differ. Unfortunately, adjusting it is hard because designers lack a means for efficiently defining, fine-tuning and balancing game mechanics. The language Machinations provides a graphical notation for expressing the rules of game economies that fits with a designer's understanding and vocabulary, but is limited to design itself. Micro-Machinations (MM) formalizes the meaning of core language elements of Machinations enabling reasoning about alternative behaviors and assessing quality, making it also suitable for software development. We propose an approach for designing, embedding and adapting game mechanics iteratively in game software, and demonstrate how the game mechanics and the gameplay of a tower defense game can be easily changed and promptly play tested. The approach shows that MM enables the adaptability needed to reduce design iteration times, consequently increasing opportunities for quality improvements and reuse.
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Many research projects have assessed the possibility and effectiveness of implementing games as health interventions. Recent literature shows generally positive results in specific case studies. However we acknowledged that research projects in this field regularly seem to disregard the connection to possible effective game mechanics, design principles and behavior change theories to underpin such results. Evidently most of these studies were intended and designed solely as randomized controlled trials (rct’s) to validate effectiveness of health interventions. We propose a theoretical framework to assess whether and on what grounds certain behavioral effects may be attributed to particular game mechanics and game play aspects. Our model is founded on the Elaboration Likelihood Model of Persuasion (ELM), which is quite appropriate to guide the evaluation structure for interventions that either aim at short term or long term attitude and behavior change. By means of an analysis of the working principles in the renowned game-based intervention Re-Mission we propose a small step towards such a framework.
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This paper addresses the procedural generation of levels for collaborative puzzle-platform games. To address this issue, we distinguish types of multiplayer interaction, focusing on two-player collaboration, and identify relevant game mechanics for a puzzle-platform game, addressing player movement, interaction with moving game objects, and physical interaction involving both players. These are further formalized as game design patterns. To test the feasibility of the approach, a level generator has been implemented based on a rule-based approach, using the existing tool called Ludoscope and a prototype game developed in the Unity game engine. The level generation procedure results in over 3.7 million possible playable level variations that can be generated automatically. Each of these levels encourages or even requires both players to engage in collaborative gameplay.
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The design of health game rewards for preadolescents Videogames are a promising strategy for child health interventions, but their impact can vary depending on the game mechanics used. This study investigated achievement-based ‘rewards’ and their design among preadolescents (8-12 years) to assess their effect and explain how they work. In a 2 (game reward achievement system: social vs. personal) x 2 (game reward context: in-game vs. out-game) between-subjects design, 178 children were randomly assigned to one of four conditions. Findings indicated that a ‘personal’ achievement system (showing one’s own high scores) led to more attention and less frustration than a ‘social’ achievement system (showing also high scores of others) which, in turn, increased children’s motivation to make healthy food choices. Furthermore, ‘out’-game rewards (tangible stickers allocated outside the game environment) were liked more than ‘in’-game rewards (virtual stickers allocated in the game environment), leading to greater satisfaction and, in turn, a higher motivation to make healthy food choices.
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Binnen dit onderzoek is gekeken of het mogelijk is het creatieve denkproces van een hoogbegaafde leerling inzichtelijk te maken voor de leerkracht door middel van het spelen van een digitale game
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An important step in the design of an effective educational game is the formulation of the to-be-achieved learning goals. The learning goals help shape the content and the flow of the entire game, i.e. they provide the basis for choosing the game’s core (learning) mechanics. A mistake in the formulation of the learning goals or the resulting choice in game mechanics can have large consequences, as the game may not lead to the intended effects. At the moment, there are many different methods for determining the learning goals; they may be derived by a domain expert, based on large collections of real-life data, or, alternatively, not be based on anything in particular. Methods for determining the right game mechanics range from rigid taxonomies, loose brainstorming sessions, to, again, not any method in particular. We believe that for the field of educational game design to mature, there is a need for a more uniform approach to establishing the learning goals and translating them into relevant and effective game activities. This paper explores two existing, non-game design specific, methods to help determine learning goals and the subsequent core mechanics: the first is through a Cognitive Task Analysis (CTA), which can be used to analyse and formalize the problem and the knowledge, skills, attitudes that it is comprised of, and the second is through the Four Components Instructional Design (4C-ID), which can be used to determine how the task should be integrated into an educational learning environment. Our goal is to see whether these two methods provide the uniform approach we need. This paper gives an overview of our experiences with these methods and provides guidelines for other researchers on how these methods could be used in the educational game design process.
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