作者：Robert Yang

今天我们将谈论亚里士多德哲学——现今风靡的更现代的以玩家为中心理论。（点击此处阅读本系列第一、三、四部分）

但首先，有些历史对我们认识这些理念起着重要作用：

1982年，雅达利在美国推出一款极度风靡的视频游戏掌机，但未具体规定谁有游戏发行资格——所以1983年，行业因宠物食品公司等发行的众多糟糕作品而分崩离析。玩家永远不会忘记：我们曾纠结于游戏是否“过短”或是否“值得”，而且电子游戏评论和文学、音乐电影和艺术评论不同，总会将价格考虑在内。

所以现在我们开始量化：多少武器、关卡和体验时间？你只能在NES暗盒有限的容量中融入这么多关卡，所以开发商会寻找其他方式扩充游戏时间——《洛克人》以更富挑战的方式重新设置关卡和首领元素，《最终幻想》给敌方精灵重新着色，创造更加强大的角色，由于更具难度的游戏需花费更长时间攻克，因此最终会变成更“有价值”的游戏。

mega man from internetclub91.com

但就像我们之前谈到的，很少人能够享有充分掌握视频游戏的条件：例如，拥有充足可支配收入（或零花钱）支付这些游戏，有几段较长空闲时间把握这些游戏，更不要说大把运气、技能和毅力。

这些人通常是中产年轻人，他们是传统观念中的“玩家”，但值得庆幸的是此刻板观念如今已逐步消失。这些玩家已内化1983年的崩溃局面，行业亦是如此。他们通过严格质量控制以及着眼大众“娱乐”寻求稳定（例如，“哇，PlayStation 2也可以播放DVD！”），最近则通过通俗性扩大用户基础。

所有现代玩家中心设计理念都重新定义“优秀玩家”——从亚里士多德概念中的“熟练玩家”到“所有玩家”。

现在作为哲学家，我们必须思考：什么是通俗性？

从某种“通俗性”含义来看，也许我们可以将Valve FPS益智游戏《Portal》的问世看作此方面的转折点。

《Portal》将通俗性定义成“几乎人人都能体验和完成游戏”。游戏很简洁，但依然有少数人抱怨其过于繁琐（游戏邦注：这也是橙盒版内容的组成部分，橙盒融合5款游戏，售价50美元，完全打破我们的价值观念）。

虽然早在这款游戏发行前，通俗性就是业内多年来的关注焦点，但大家从未如此彻底地将其融入整个开发过程。很多媒体采访都关注频繁测试如何取舍谜题内容。

强调收集数据——很多时候是平衡多人游戏的定量数据，是游戏设计的经验探讨。通俗性是指提出假设（“若Protoss Zealot的制作时间更久，将能够平衡早期游戏困扰”）以及收集证据确认或否定假设（“Protoss如今在4分钟Gold联盟中赢得较少比赛”）。

表格、图形、热图、死亡地图、刺杀地图、视觉追踪、心率检测和玩家分析——某游戏设计经验法称：收集玩家数据，合理诠释能够创造优质游戏。

（更深入来看，逻辑实证称：任何缺乏科学根据的内容都无法证实，因此毫无意义，这本身就是非科学论断，同时也是逻辑实证如此快速消亡的部分原因。）

但这种数据导向设计也存在类似哲学科学问题：

何时数据准确，存在关联性，你如何进行收集？

若你基于高竞争团体服务器搜集数据，或也许基于某从未玩过视频游戏的用户收集信息，这些数据是否能够有效平衡游戏？（这看情况）我们是否应该转而测试其他“普通玩家”，若是如此，谁是这些玩家？（这看情况）这是否是实现通俗目标的最佳渠道，或者是否会因试图满足各玩家，而最终落得无人问津？（这看情况）。

再来是如何诠释所收集的数据？

想象在《军团要塞 2》中，数据显示鲜有玩家选择间谍角色——这是否意味着火焰兵过于强大，工程师太难消灭，或其他信息？（我们需要更多数据）若间谍难以消灭工程师，是否由于热门地图的特定强大地点存在关卡设计问题，或者存在相关声音漏洞，间谍的斗篷声响过大，或者存在平衡问题：间谍的斗篷无法维持至穿过前线？（我们需要更多数据）。或者只有少数玩家扮演间谍角色是件好事？（这看情况）。

Team Fortress 2 from osxdaily.com

但现在假设你希望知道玩家为何总是跌落悬崖。

你是否追踪玩家摄像头位置和向量，创造其着眼的热图，决定他们是否注意“危险！不要掉下去！”标记，强化标记纹理的对比度是否有所帮助？

你是否基于关卡冲突模式绘制他们的运动向量（游戏邦注：也许他们的减速时不够快），或者是否提高灰尘材料的摩擦系数？

你是否询问他们，“为什么总是掉入悬崖？”

也许此行为主义概念——通过观察玩家行为推断意图，只是在回避问题。为何不直接征求玩家反馈，让他们表达自己的意图？社会自由主义认为在运用权利过程中，所有社区成员都要参与。

虽然游戏设计经验学派收集定量玩家数据，但此社会自由主张通过关注群体，调查和分析玩家邮件、论坛和博客反馈信息汇集定量玩家数据模式。

社会自由观点认为优质游戏来自尽可能倾听用户心声，合理诠释用户反馈信息。这里，“通俗性”是指分散权利，分享设计权限。

（题外话，也许新自由主义模式会支持来自团体和公会的反馈信息，或者也许是第三方供应商和游戏发行商，其对此重视程度超过个人玩家意见。最终设计改变会逐步扩散，间接帮助个人玩家）。

在《Left 4 Dead 2》中，玩家投票选择保持那些游戏模式；在《Halo: Reach》中，Bungie通过投票结果平衡多人播放列表。渐渐地，玩家如今开始通过直接民主方式做出游戏设计决策。

但不要将此类推权限延伸过大。相比身处现实宪法民主的公民而言，玩家享有较少统治权利，鲜少真正参与设计。依然是由开发商提取反馈信息，决定优劣，然后最终落实设计工作。

此外，不要基于玩家反馈信息设计游戏的原因还有一个：玩家通常会改变选择，或者完全停止体验。

让我们简单回到经验探讨和定量数据，坚信社会自由玩家反馈信息不是分享支配权限，而是分享更多数据（游戏邦注：是指定量数据）。

你如何知晓某组数据或诠释信息未来是否还会适用？有些玩家会出于某些原因突然扮演间谍角色。也许有天你的整个玩家经济形态就突然采用Stone of Jordans充当货币，而非金子或宝石。或者明天重力就会忽然停止存在。

这或多或少就是David Hume所说的经验主义的问题：我们如何获悉显著现象未来是否会继续以此方式存在？

人类比自然法则不稳定，不论是反馈信息，还是论坛言辞，还是他们古怪的体验风格，这些都会在突然读到某指南或观看某YouTube战略视频时突然发生改变。

我们无法收集更多玩家数据或征求更多玩家反馈信息，以判断所收集数据或反馈信息是否有效；也就是说，我们无法通过归纳法证明归纳法的有效性，这是个循环逻辑。

但逻辑的运用原理是系列前提基础上的归纳模式（游戏邦注：同时证明归纳的有效性，我们无法运用归纳法，这是由于这也是个循环逻辑），所以我们必须通过归纳法证明归纳法的有效性，但我们仅仅是通过归纳法证明归纳法的不可靠性！

你觉得有些困惑是人之常情，Hume自己也是。最后，他采用常识性的“观望”方式，这是实际怀疑主义的一种。“不要担心其是否能够永远适用，而是关心其当前是否有效。”

我想这是就连续游戏补丁、MMO游戏和Valve“游戏就是服务”理念的哲学论据（截至文章发布，《军团要塞2》已出现150个漏洞）。

将此态度同经典亚里士多德玩家中心主义理念比较——玩家会抱怨“《洛克人》由于Cold Fusion Man难度过大”，Capcom的回应可能是，“你如何获得这些数据？”

忽然亚里士多德不再看起来那么多元化和自由开放，而是变得固定、静止和反映迟钝。

也许我们需要接受“优质”游戏设计只能维持一段时间，直到用户数据显示其不再优秀，然后你就会重新设计，重新寻找平衡，直到其再次变得完善。这无疑是种玩家中心观念，其倡导开发者需根据玩家社区“取得成功”。

所以什么造就优质游戏？

也许是改变的意愿。

游戏邦注：原文发布于2010年10月5日，文章叙述以当时为背景。（本文为游戏邦/gamerboom.com编译，如需转载请联系：游戏邦）

Philosophy of Game Design – Part Two

by Robert Yang

To review from Part 1: Plato valued absolute truth, irrespective of player preferences, and so he argues that good games come from good developers. Aristotle had a slightly more pluralistic account of truth that was player-dependent, and so he argues that good games come from good players – and “good players” are skilled players who can beat difficult games.

For Part 2, we’ll derive some additional philosophies from Aristotle’s account – some more modern, mainstream player-centric theories that are all the rage right now.

But first, some history that’s crucial for understanding those approaches:

In 1982, Atari had a wildly popular videogame console in the US, but didn’t regulate who could publish games – so in 1983, the industry crashed from the collective weight of so many poorly designed games made by pet food companies and other ilk. Gamers have never forgotten: We’re obsessed with whether a game is “too short” or if it was “worth it,” and videogame reviews, unlike their literary, music, film, and art counterparts, routinely take price into account.

So now we quantify: How many weapons, levels and hours of playtime? You could only fit so many levels into the limited memory of an NES cartridges so developers found other ways to inflate playtime – Mega Man reuses levels and bosses in more challenging ways, Final Fantasy recolors enemy sprites for more powerful variants – because a more difficult game took longer to beat, which in the end was a more “valuable” game.

But, as we mentioned before, relatively few people have what it takes to master videogames: Namely, enough disposable income (or allowance) to pay for these games and several long, uninterrupted stretches of free time to master these games, not to mention a whole lot of luck, skill and perseverance.

Such people were usually middle-class teenagers, the source of the “gamer” stereotype that’s thankfully dying today. While these gamers had internalized the crash of 1983, so had the industry. They sought stabilization though stringent quality control, an emphasis on general “entertainment” (e.g. “wow, the PlayStation 2 plays DVDs too!”) – and more recently, by expanding their audience through accessibility.

All modern player-centric design philosophies re-cast the “good player” – from the classic Aristotelian notion of “skilled player” to “every player.”

Now as philosophers, we have to ask: What does it mean to be accessible?

For one sense of “accessible,” perhaps we can take the release of Valve’s FPS puzzler Portal as a watershed moment in this field.

Portal defined accessibility as “almost anyone can play and beat this game.” It was rather short, yet few complained about its length. (It was also part of the Orange Box, a collection of five games for $50 that utterly exploded our collective notion of value.)

While accessibility had been an industry concern for many years leading up to the game’s release, never had it been so fundamentally integrated into public accounts of the development process. Much of the press and interviews focused on how frequent testing decided which puzzles to keep and which to reject.

This emphasis on collecting data – most often quantitative data to balance multiplayer games – is an empirical approach to game design. Here, accessibility means posing a hypothesis (“If the build time for a Protoss Zealot is longer, it will balance early game harassment.”) and collecting evidence to confirm or deny that hypothesis (“Protoss are now winning fewer matches under four minutes in the Gold league.”).

Charts, graphs, heat maps, death maps, kill maps, eye tracking, heart rate monitors, player analytics – an empirical method to game design argues that collecting player data and interpreting it properly makes good games.

(Taking that idea a hundred steps further, logical positivism argues that anything unscientific isn’t verifiable and thus is meaningless, which in itself is an unscientific statement, which is partly why logical positivism quickly died the way it did.)

But this kind of data-driven design is plagued by similar problems posed in the philosophy of science:

When is data accurate / pertinent, and how do you go about collecting it?

If you collect data from highly competitive clan servers, or perhaps from someone who’s never played a videogame before in their life, are those sets of data valid for balancing the game for everyone else? (It depends.) Should we instead test on some sort of “average player” and if so, then who is that player? (It depends.) Is that really the best way to achieve accessibility, or do we end up pleasing no one by trying for everyone? (It depends.)

And then how do you go about interpreting that data you’ve just collected?

Imagine in Team Fortress 2 that data indicates fewer players are playing as spies – does that mean Pyros are overpowered or that Engineers are too difficult to kill or something else entirely? (We would need more data.) And if Engineers are too difficult to kill as a Spy, is it actually a level design problem with specific overpowered build sites on popular maps, or is it a sound-related bug where the Spy’s cloak sound is too loud, or is it a balancing issue with how the Spy’s cloak doesn’t last long enough to get past the front line? (We would need more data.) Or is this a good thing, to have so few players playing as Spies? (It depends.)

But now let’s say you want to know why a player keeps falling off a cliff.

Do you track the player’s camera position and vector to produce a heat map of what they look at, to determine whether they notice the “Danger! Don’t Fall Off!” sign, and increase the contrast on the sign texture to compensate?

Do you map their movement vectors against the level’s collision model – maybe their movement speed doesn’t decelerate fast enough – or do you increase the friction parameter on the dirt materials?

Do you just ask them, “Why do you keep falling off the cliff?”

Maybe this behaviorist notion, that we can deduce a player’s intention from observing their actions, is just side-stepping the issue. Why not just solicit player feedback directly and have them verbalize their intentionality? Social liberalism holds that all members of society should have (at least some) input with regards to the process of running their government.

While the empirical school of game design collects quantitative player data, this social liberal approach collects a form of qualitative player data through focus groups, surveys, and analyzing player feedback from emails, forums and blogs.

The social liberal account holds that good games come from listening to as many individual players as possible and interpreting that feedback properly. Here, “accessibility” means decentralizing power and sharing the reins of design.

(As a sort of pseudo-variant, perhaps a neoliberal approach would argue for feedback from clans and guilds, or maybe third-party vendors and game publishers, and value that over individual players’ opinions. The resulting design changes might trickle down and indirectly help individual players.)

In Left 4 Dead 2, players vote for which game modes to keep; in Halo: Reach, Bungie uses voting results to balance multiplayer playlists. Increasingly, players are now making game design decisions through direct democracy.

Don’t stretch this political analogy too far, though. Compared to citizens in real-life constitutional democracies, players have very little political power and rarely get real input on design. It’s still the developers who sort feedback to determine what is signal and what is noise, and they ultimately do the design.

Plus, there’s another reason not to base your game design on player feedback: Players often change their opinions or stop playing entirely.

Let’s return briefly to the empirical approach and quantitative data, with the mindset that social liberal player feedback isn’t actually shared governance but rather just more data – qualitative data.

How do you know that a particular set of data or interpretation will hold true for the future? Many players could suddenly start playing as Spies for some reason. Maybe one day, suddenly your entire player-based economy uses Stone of Jordans as currency instead of gold or gems. Or tomorrow, gravity could suddenly cease to exist.

This is, more or less, the core problem of empiricism as posed by David Hume: How do we know that observable phenomena will continue to act that way, consistently, in the future?

People are much more unstable than the laws of nature, whether in their feedback and rants on forums or their erratic playstyles that could abruptly change upon reading a guide or watching a YouTube video of a strategy.

We can’t collect more player statistics or solicit more player feedback in order to decide whether collecting statistics or feedback is good; that is, we can’t use induction to prove the validity of induction because that’s circular logic.

However, that very reasoning about using logic is a form of deduction from a set of premises – and to prove the validity of deduction, we can’t use deduction because that’s circular logic too – so we must use induction to prove the validity of deduction … but we just used deduction to argue for the fallibility of induction!

It’s okay if you’re confused – so was Hume. In the end, he adopted a kind of common sense “wait and see” approach, a type of practical skepticism. “Don’t worry about whether it will hold true forever, but just worry about whether it holds true for now.”

And that, I guess, is a philosophical justification for frequent game patches, MMOs and Valve’s “games as services” mantra (as of this writing, there have been 150 patches to Team Fortress 2.)

Compare this attitude to the classic Aristotelian conception of player-centrism – players might’ve complained that “Mega Man is too hard because of Cold Fusion Man” – and Capcom’s response probably would’ve been, “How did you get this number?”

Suddenly Aristotle doesn’t look so pluralistic and liberal anymore – instead, it seems immovable, static and unresponsive.

Perhaps we must accept that a “good” game design is only good for a while, until the player data indicates it isn’t good anymore – and then you redesign and rebalance it until it’s good again. This is distinctly a player-centric notion, the idea that a developer must “do right” by the community of players.

So what makes a good game?

Perhaps it’s the willingness to change it.

Robert Yang is currently an MFA student studying “Design and Technology” at Parsons, The New School for Design. Before, he studied English and taught game design at UC Berkeley. If he’s famous for anything, it’s probably for his artsy-fartsy Half-Life 2 mod series “Radiator” that’s still (slowly) being worked on.（Source：escapistmagazine）