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阐述游戏机制的定义及其改进方法

发布时间:2011-08-22 13:57:45 Tags:,,,

作者:Danc

神秘的游戏机制是所有游戏的核心,是让玩家快乐和紧张的源泉。

卓越的游戏需要良好的机制作为构成基础,但我从来没有见到对这种游戏机制的统一定义。在此,我提出一种简单的定义,其中一些主要原理是出自Raph Koster所著的《A Theory of Fun》。

游戏机制是以系统/模拟为基础的规则,这种规则促进和鼓励用户通过反馈机制探索和学习游戏机制的可能性空间。

这个简单的定义可以帮助我们了解游戏的运作和改进游戏机制的方法。

反馈环路(Feedback loops)

这种游戏机制的模式的核心内容是,鼓励学习的反馈环路。以下图表更加形像地解释了反馈环路的概念:

game feedback loop(from lostgarden)

游戏反馈环路(from lostgarden)

玩家采取行动。

行动在模拟的游戏世界中会产生某种影响。所谓模拟包括公共/私人的指示符和影响指示符状态的因果规则。玩家极少意识到这种规则,所以几乎不可能立即形容出由规则描述的完整可能性空间。模拟的未知部分是玩家必须努力破解的“黑盒”。

玩家收到反馈

玩家使用手头上的新工具和信息采取另一个行动。使用已学会的知识技能追求其他乐趣。

连接游戏机制以创造系统的系统

游戏机制的互联网络组成了游戏的整体。你可以想象游戏是一系列相互联系的谜题,解开一个谜题的同时触发另一个谜题的线索。

游戏提供给玩家的信息不一定能用于破解手头上的黑盒。人类收集潜在的有用信息,好比松鼠收集过冬的坚果。我们会长期储存大量的线索,目的是为解决下一个阶段的黑盒扩充我们的知识。

传统的介质游戏可以解释一个黑盒如何发展到另一个黑盒。在游戏机制临时排列而成的层级中,快速反馈回路(游戏邦注:这通常是基础控制组合的一部分)为掌握长期反馈环路提供了工具。连接游戏机制的潜在模式几乎是无穷无尽的。这是未来研究的重要区域。

人类的信息贪欲(infovores)

人类兴奋地破解黑盒问题。这是我们神经学习系统的基本方面。当我们解决了一个难题或得到我们认为会有助于解决黑盒的信息时,我们得到真实的愉悦感。世世代代的进化都选择了这种行为,是因为这是一种鼓励

使用工具和技术的生物奖励系统。如果人类不迷恋解决问题的行为,我们就不会有农业、医药、建筑学和其他基础生存技术,人类也不会成为地球上最成功的物种。

这个模式的关键部分是游戏积极地鼓励学习。假设,我在桌上放一个带隐藏按钮的黑盒。不知情的玩家按下这个按钮一定次数后,黑盒会喷出一千个闪闪发光的银币。这不是一个游戏,而是一个奇特的小发明。

为了把这个小发明变成游戏,游戏设计师需要做以下几件事:

鼓励发现。第一,明确地指示玩家摁下此按钮。人类天生就是好奇心很重的动物,但作为游戏设计师,我们需要明确地指示他们采取何种行动。

鼓励探索。第二,设计师要在这个小发明前安放一个计算器,以便用户知道自己的行为对系统有所影响。计算器只负责提供积极的反馈,但其结果要由用户自己解读。

掌握工具。第三,设计师要标注“支付:1000钱币!”并非所有游戏都需要明确的获胜条件,暗示未来的效用开始与特定的游戏机制产生相互作用,是一种非常实用的、鼓励玩家的手法。

我们已经把一个小发明变成一个简单的机会对策。此二者的区别在于我们的老虎机显然是为了鼓励玩家学习。

现存的游戏充满鼓励学习的技术。有些你马上就能想到:

·由复杂的系统组成的、鼓励玩家依次探索和掌握可能性空间各个方面的关卡。

·得分、钱币收集和经验值都是简单的反馈机制,可以让玩家知道自己正在进步。

·《塞尔达传说》中的“查看暂时得不到的宝箱”就在预示着一种未来效用。

单纯的系统算不上游戏。大量的信息也不能等同于游戏。通过强大的反馈机制鼓励学习的系统才是游戏。

塞尔达传说(from wllapapers)

塞尔达传说(from wllapapers)

副效应

我刚描述了游戏机制的基础。现在让我们深入了解几种副效应。当你试图实现系统时,这几种副效应就会马上出现。

竭尽

挤压

不相干的事

人为因素

竭尽的定义

往虚拟的弹珠机里掷钱币,然后收获大量信息,最后玩家就会彻底理解游戏的系统。游戏机制仍然提供信息,但对玩家已不再有吸引力,因为这些信息不再响应我们正在解决或已经解决的问题,不能激活我们头脑中的好奇网络。我们开始下意识地过滤来自这些机制的反馈。竭尽是一种完成学习的状态,在这种状态下,玩家终于断定某种行为不再产生有意义的结果。

在街机游戏《超级猴子球》中,研究人员惊讶地发现,最大的快乐来自角色的坠崖而亡。人们喜欢这样!如果你把坠崖而亡当作一次充实的学习体验,感觉当然不错。然而,当画面重演时,人们又讨厌它。相同的刺激,完全不同的反应。坠崖而亡的画面在第二次重演时失去了教育功能。最后,玩家下意识地不断问自己:“这值得我费时间么?对我有什么用呢?”

超级猴子球(from digitalspy.co.uk)

超级猴子球(from digitalspy.co.uk)

不成熟的竭尽

学习的路径有很多,但并非所有的方式都受游戏设计师的推崇。我们想像,彻底了解系统的结果是,完全掌握系统。事实上,“彻底了解”意味着用户已经对系统建立了固定的心理模式,且不再改进它。这种模式可能简单,也可能复杂,取决于用户的喜好倾向。

在纸牌游戏《二十一点》中,复杂模式可能根据已出现的纸牌考虑其他纸牌出现的概率。简单的模式可能推断纸牌出现的方式非常随机。用户还可以进一步探索游戏的深度。但如果他们是休闲玩家,说纸牌是随机出现的已经足够判定游戏情形了。

对游戏设计师而言,一个比较大的挫折是,许多用户选定过分简单的模式。因为设计师误判了玩家对游戏机制的反应,玩家会认为游戏不公平或难度太大,然后将其弃置一旁。

有些机制有着非常高的可预测竭尽率。也就是说,大多数玩家马上明白再多看几次过场动画也不能得到更多信息。其他机制显示,竭尽率的波动取决地于玩家和他们的个人喜好、对上瘾的处理方式。有些玩家只玩过一次老虎机就不再尝试了。而有些玩家从未意识到这个真相——这种机器的存在意义只是掏空他们的钱,而不是给他们送钱,所以他们就在不断追求下一个奖励中沉迷、堕落,最终毁掉了自己的人生。

影响竭尽的因素非常多:

·个性

·个人经历

·因精通技能而产生的潜在奖励具有实际价值

·机制表明玩家对系统的精通程度仍有上升空间

单纯地运用自己的聪明才智无法产生头两个因素,深刻地理解目标受众的心理才是最实用的策略。后两个因素很大程度上受到设计师的控制,可以通过频繁地设计原型和观察玩家行为而得到提升。

挤压:学习到使用工具的过渡

如果竭尽是因无用而被玩家抛弃游戏机制的时期,那么挤压的阶段就是玩家掌握某种游戏机制后的很长一段时间内,因为该机制还能产生价值而仍然不断运用它。

当玩家学会一种系统,他们通常会保持与其互动作用。乍一看,这好像有点不正常,好比不断地咀嚼早就没有味道的口香糖,无论再怎么执行这个动作,你也不能指望从中获得任何学习价值。

然而,游戏是连接游戏机制的网络。为了给探索另一个黑盒创造有利的游戏状态,玩家将与已经掌握的游戏机制保持互动作用。掌握给予玩家可测的实用工具,借助这种工具,玩家在其他游戏环节中不断进步。设计师应以在游戏其他部分中产生的学习和掌握奖励玩家,以此刺激玩家重访旧游戏机制。

确保玩家已掌握的系统仍然能带来实用价值,可以延长游戏机制吸引玩家的时间。应该引进可带来更多玩法, 同时又少耗开发工时的设计技术。

不相干的事:黑盒外部的游戏

玩家认为有效的黑盒网络可以大大拓展游戏本身的系统。玩家通常会收集少量对游戏机制无实际影响的信息。这些信息残片就像一堆奇怪的钥匙,但它们能开启的锁我们可能永远也找不到。

为了显示游戏的深度,游戏设计师可以用不存在的系统线索戏弄玩家。在过场动画里,某些角色长着狡猾的拱形眉,我们就把它当作一连串有企图的警报。我们的大脑对人、脸、关系、繁殖机会和政治相当敏感!眉毛是很重要的暗示信号?所以玩家贪婪地储存这类记忆。

已收集的信息对玩家的游戏玩法有何影响?没有。对玩家的生活?极少。他们也不会见到过场动画的虚拟人物。但我们的大脑还没进化到能应对这种事的程度。作为灵长类,来自小部落的同伴所流传的弓形眉传说总是象征着非常非常重要的事物。所以我们的大脑把愉悦感作为发现这种“显著”潜在信息的奖励。

设计师设法暗示系统,然后从中获利,实际上并没有构建该系统。油画、雕塑、电影和电视都是在我们大脑学习系统的怪癖上成长兴旺起来的,这并不是太夸张的说法。

这种不相干的事,缺点是竭尽得很快。我们的大脑变得相当擅长识别错误、无用的信息。几乎没人会多看一次过场动画。这是为什么?

我个人倾向于保守地使用不相干的游戏机制。作为游戏设计师,我们有着高明的处理技巧。我们可以裁剪出有效的反馈环路的电子层级,足以在电脑和玩家之间制造出复杂的二重互动活动。这种系统可以高效地触发内在的愉悦感,鼓励长期的学习。游戏开发者实际上是在指挥一场庄严肃穆的交响乐,这是一场系统的学习和令人入迷的交互性的交响乐,是任何静态媒体都无法组织的交响乐。

虽然有时候,用大笔触效果来表现宏大的神秘感也是值得一试的。背景、角色设计和情节可以是关键的引子,在玩家敲击任何按钮前就传达游戏的意义。

人为因素:强调游戏的人性

有些人读到模式,马上就把它们当作把艺术游戏中的人性、灵魂部分剥离的简化机制。我所描述的游戏机制回避了这一点。除了纯粹地分析性问题,它们还明确地展示了社交性、叙述性和情绪性元素。所有的人类经验部分(游戏邦注:它们影响我们处理和学习刺激物的能力)都落入潜在的游戏玩法的范畴之中。

游戏设计的定义比当前市场的游戏范围要广泛得多。尽管用这种定义非常适合解释生命值、猛击按键和高分榜,但这种定义的广度是为了鼓励更广泛地探索人类学习的范围。这种模式直接显示了以下开放性问题:

·影响学习关系、爱、恨或精神的反馈机制是什么?

·我们如何围绕影响这些反馈机制的话题制作游戏?

·我们根据现有的游戏得到了实用的知识基础,从而利用可靠的形式制造相同的事物。好的理论框架可以帮助游戏设计师,创造包括更加广泛的人类经验在内的未来游戏。

结论

任何名副其实的游戏设计模式的目标是,既解释现存的行为,也预测未来的媒体行为。就目前我的个人经验而言,就解释几乎所有现存的游戏(棋盘游戏、老虎机和社交游戏等)而言,这种模式看起来还相当粗糙。这种模式仍然存在改进空间,但却已足够实现我的主要目标。

我认为理想的实用模式,必须允许业内人士以更严谨的措辞描述游戏设计。这种模式应该深入地解释原型失败的原因;允许设计师用最接近问题的简写语言讨论潜在问题和解决方案。好的预测模型支持更智能的设计决定,可以减少浪费和不必要的返工。

我认为该模式有以下几个实用点:

1、将游戏机制当作意义明确、综合的原子单位。这些单位可以进行个别讨论,也能够以更有意义的方式联系起来。

2、明确识别用户的价值。乐趣不是近乎自发的精神活动,它具有可实验的神经学基础。

3、存在可识别的显性输入和输出。当某种游戏机制包含所有组成元素,如动作、规则、记号和反馈系统等,你可以轻易地识别出来。通过观察,你可以确定玩家对各个机制的反应,然后调整其影响。

总而言之,我希望,这种游戏机制的模式能够给未来的游戏设计探讨打下良好的基础。今后,当我继续漫谈关于游戏设计的文章时,我还将频繁地引用这个模式模式。(本文为游戏邦/gamerboom.com编译,如需转载请联系:游戏邦

What are game mechanics?

The phrase “game mechanics” sends a pleasant shiver down my spine. At the heart of every game are these mysterious whirring clicking mechanisms that deliver to the player pleasure and thrills.

We use them, we build them, but I’ve never seen a good unified definition of game mechanics that gives us a practical base upon which to build great games. Here is one. It is clobbered together from a variety of influences though many of you will recognize some central tenets from ‘A Theory of Fun’ by Raph Koster.

Game mechanics are rule based systems / simulations that facilitate and encourage a user to explore and learn the properties of their possibility space through the use of feedback mechanisms.

It is a simple definition, but it offers a good amount of insight into why games work and how we can make them better.

Feedback loops

Central to the model is the concept of feedback loops that encourage learning. Here is a diagram that should explain the concept in a more visual format:

Player performs an action.

The action causes an effect within the simulated game world. The simulation contains public and private tokens and the causal rules that affect the states of the tokens. The player rarely knows all the rules and is highly unlikely to be able to instantly describe the complete possibility space described by the rules. The unknown portion of the simulation is a “black box” that the player must attempt to decipher.

The player receives feedback.

With new tools and information in hand, the player performs another action. Using what we’ve learned, we pursue additional pleasure.

Linking game mechanics to create a system of systems

Interconnected networks of game mechanics make up the game as a whole. You can think of the game as a set of interlinked of puzzles where solutions to one puzzle lead to clues that help on additional puzzles.

The info treats that a game provides to the user need not be used to solve the immediate black box at hand. Humans horde potentially useful information like squirrels horde nuts for the winter time. We’ll store hints in our copious long term memory in the hope that there will be another black box down the line that will yield to our improved tool chest of knowledge.

The traditional metagame that sits on top of a game’s core mechanics is a good example of how one black box feeds into another. In this situation, the game mechanics are arrange in a temporal hierarchy where rapid feedback loops (often part of the basic control scheme) provide tools that enable the mastery of longer term feedback loops. The potential patterns of linking game mechanics together are nearly endless. This is a wonderful area of future study.

Humans are infovores

Humans are wired to solve black boxes. It is a fundamental aspect of our neurological learning wetware. We get real chemical rewards when we grok a problem or gain information that we suspect will help in grokking a black box. Evolution has selected for this behavior over thousands of generations since it is the biological reward system that encourages tool use and technological adoption. Without this built in addiction to problem solving, we would lack agriculture, medicine, architecture and other fundamental survival techniques that make the human species such a remarkably successful animal.

A key aspect of our model is that games actively encourage learning. I can put a black box on the table with a hidden button. Unbeknownst to a potential user, pressing the button enough times and the black box will spew out a thousand shiny silver coins. This is not a game. This is a bizarre gizmo.

To turn it into a game, a game designer would need to do several things.

Encourage Discovery: First, make it obvious that the button in meant to be pushed. Humans are naturally curious creatures, but as game designers, we need to explicitly direct them to take certain actions.

Encourage Exploration: Second, the designer would put a counter on the front of the machines that lets the user know that their actions are having some impact on the system. The counter provides delightful drips of feedback and it is up to the user to interpret that feedback

Provide Tool Mastery: Third, the designer would post a note “Payout: 1,000, coins!” Not all games need explicit winning conditions, but hinting at future utility is a highly useful technique for encourage the player to begin interacting with a particular game mechanic.

We’ve turned a gizmo into a simple game of chance. The difference between the two is that our primitive 1-armed bandit is explicitly designed to encourage player learning.

Existing games are richly laden with techniques that encourage learning. A few that come immediately to mind:

Levels take complex systems and encourage players to explore and master one aspect of the possibility space at a time

The use of scores, coin collecting and experience points are all simple feedback mechanisms that let the user know they are making progress towards some future state.

The classic “See the treasure chest you can’t reach” in Zelda acts as a promise of future utility.

A system alone is not a game. A dump of information is not a game. A system that encourages learning through strong feedback mechanisms is a game.

Secondary effects

I’ve just described the foundation of a game mechanic. Now lets dig into several of the secondary effects that immediate appear when you attempt to put this system into practice:

Burnout

Milking

Red herrings

Human factors

Burnout: A definition

After merrily harvesting tidbits of information by plunking coins into the virtual pachinko machine, the player will eventually grok the system. The game mechanisms may still serve up information, but the tidbits are not longer as tempting. The info we receive has no resonance with problems that we are solving or problems we have solved. It activates no curious networks in the brain. We begin subconsciously filtering out the feedback from these mechanisms. Burnout is a state of completed learning where the player finally figures out that a particular action no longer yields meaningful results.

In Monkeyball, researchers were astounded to find the the biggest jolt of pleasured occurred when you fell off a cliff and died. People loved it! If you look at falling off the cliff as a huge learning experience, this makes perfect sense. However, when they replayed the animation, people hated it. Same stimulus, radically different response. The animation of falling off cliff lost its ability to teach the second time around. Ultimately, users are subconsciously constantly asking the question “Is this activity worth my time? Does it gain me anything useful?”

Premature burnout

There are multiple paths that learning can take and not all are ones that game designers desire. We would like to imagine that groking a system results in complete and utter mastery of that system. In reality, ‘grokking’ means that that the user has stabilized on a mental model of the system they no longer feel like improving further. This model can be simple or complex, depending on the inclinations of the user.

A complex model of Black Jack might take into account probabilities of cards appearing based off what has already been played.

A simple model of Black Jack might conclude that cards appear pretty much randomly. There is more depth for the user to explore, but if they are a casual player, saying it is random is ‘good enough’ to judge the game.

A big frustration to game designers is that many users settle on a very simplistic model of how a particular game mechanic works. Players will claim that a game is unfair or too difficult and immediately toss it in a rubbish bin because the designer misjudged their reaction to a game mechanic.

Some mechanisms have highly predictable burnout rates. Most players immediately figure out that watching a cutscene again isn’t going to provide much additional information. Other mechanisms demonstrate a large variation in burnout rates depending on the person who is playing the game and their personal preferences and disposition towards addiction. Some players try a slot machine once and then never again. Others will ruin their lives in pursuit of the next reward, never grokking the simple truth that such machines exist to take money, not give.

The factors that influence burnout are numerous.

Personality.

Personal history.

Practical importance of imagined future rewards that stem from mastery.

The ability for the mechanism to signal that there is additional depth of mastery possible.

The first two factors are not possible to derive by simply exercising your superior intellect. A deep understanding of your target audience’s psychology is most helpful here. The second two factors are very much under the designer’s control and can be refined through heavy prototyping and player observation.

Milking: The transition from learning to tool use

The flip side of burnout is grinding. If burnout is when a player discards a game mechanism because it is no longer useful, milking is when a player continues to exercise a game mechanic long after they’ve reached the state of mastery because the game mechanics continues to provide value.

When a player has learned one system, they will often keep interacting with it. On first blush, this seems mildly demented. The activity no longer provides burst of juicy learning. It is a bit like jawing on a piece of gum that long ago lost its flavor.

However, remember that games are networks of linked game mechanics. Player will continue to interact with a mastered game system in order to create a useful game state for exploring another black box. Mastery gives the player predictable pragmatic tools that helps them advance in other aspects of the game. The learning and mastery that occurs in other portions of the game provide the necessary reward that goads the player into revisiting old game mechanics.

You can extend the time that a player spends with a set of a game mechanics by ensuring that a mastered system still provides utility to the player. Designs techniques that build tools result in more gameplay for less development work.

Red Herrings: Black boxes external the game

The network of blackboxes that the player considers valid can extend far beyond the systems in the game itself. Often, the player will collect strange bits of info that have no real impact on the game mechanics that the game designer built into the game. These pieces rattle around in our heads like a collection of oddball keys for a set of locks that we may never find.

Game designers can tease the player with hints to systems that do not exist in order to suggest depth to their games. A sly arched eyebrow in a cutscene triggers as massive cascade of meaning alerts. Our brains love people and faces and relationships and the breeding opportunities and politics! Surely, that eyebrow is important? The player greedily stores the memory away.

What impact will the collected information have on their gameplay? None. What impact will it have on their lives? Very little. This virtual person in a cut scene is no one they will ever meet. But our brains were not evolved to deal with such things. As apes, the tale of an arched eyebrow by a potential mate from our little tribe always meant something very, very important. So our brain rewards us with a little jolt of pleasure for noticing such an “obviously” beneficial tidbit.

The designer managed to suggest a system and get some of the benefits of that system without actually building it. It is not going too far to suggest that paintings, sculpture, movies and television all thrive on this simple quirk of our brain’s learning systems.

The downside is that such red herrings burnout quickly. Our brains becomes quite good at recognizing false, useless information. Almost no one watches a cut scene more than once. What would be the point?

My personal bias is to use red herring game mechanics sparingly. As game designers, we have deeper skills at our disposal. We can tailor potent electronic cascades of feedback loops that spin out a complex duet between computer and the player. Such system are highly effective at causing visceral pleasure and encouraging deep long term learning. As game designers, we conduct a majestic symphony of explicit learning and entrancing interactivity, something no static media will ever manage.

Sometimes though, it is worthwhile to suggest great mysteries with broad brush strokes. Setting, character design and plot can be crucial hooks that help make a game meaningful to players before they even press a single button.

Human factors: Emphasizing the humanity of games

Some folks read about models and immediately see them as reductionist mechanisms that strip the humanity out of the soul out of creating artistic games. The game mechanics I’ve described in this article attempts to avoid this trap. They explicitly include social, narrative and emotional elements in addition to purely analytical problems. All aspects of the human experience, that have an impact on our ability to process and learn from stimuli, fall within the domain of potential game play.

This definition of game design is much broader than the current range of games available on the market. Though it works quite well with hit points, button mashing and high scores, the breadth of the definition is intended to encourage exploration of a much wider range of human learning. Some open questions that I find immediately suggested by the model include:

What are the feedback mechanisms that impact learning about relationships, love, hate or spirituality?

How do we build games around such topics that leverage these feedback mechanisms?
Existing games give us the foundation of practical knowledge that lets us make the same thing in a reliable fashion. A good theoretical framework helps game designers create future titles that are inclusive of a wider range of human experience.

Conclusion

The goal of any model of game design worth its salt is that it both explains existing behavior and predicts future behavior of medium. In my experience so far, this model seem rather robust at explaining almost any existing game on the market ranging from board games to slot machines to social games. There is certainly room for improvement, but it is a good enough for my main goal.

I want a practical model that lets the good folks in this grand industry describe game designs in more exacting terms. The model should give insight into why their prototypes suck. It should allow them to discuss potential issues and solutions with shorthand language that cuts to the meat of the matter. A good predictive model allows for more intelligent design decisions with less waste and unnecessary rework.

So some of aspects of the model that I find useful:

It treats game mechanics as well defined, comprehensive atomic units. These units can be discussed individually and they can also be linked together in interesting ways.

Explicit identification of user value. Fun is not a nigh spiritual activity that spontaneously bursts forth from the ether. It has a testable neurological basis.

There exist clearly inputs and outputs that easily identified. You can easily tell when a specific game mechanic has all component elements such as actions, rules, tokens and feedback systems.

Through observation, you can identify the player’s reaction to each mechanism and then adjust its impact.

All and all, the hope is that this model of game mechanics is a good foundation for future discussion. It is one that I’ll be leaning on heavily as I continue to meander through this lovely little series of essays on game design.(source:lostgarden


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