作者：Keith Burgun

很久很久以前，我们便已经依赖于各种类型的随机性去帮助我们的互动系统的运行。尽管在所有类型的互动系统中总是存在随机性的一席之地，但我认为现在关于策略游戏的随机性的假设却是错误的。

我想强调的是充斥于玩家的选择与结果（在这里便是创造出随机性）之间的噪音并不会被归入策略游戏中。

什么是随机性？

基于本文的目的，随机性指的是“不可预测并且会影响游戏状态的信息。”随机信息的生成过程是人类永远都不可能想到的。随机系统的经典例子便是掷筛子，洗牌或随机数生成程序。

从技术上来看，掷筛子模式并不是真正的“随机”。这只是对于实体的回应，计算机可以获得丢筛子的信息并预测将会出现怎样的数字。而我们会提到筛子是因为人类并不能像计算机那样做。实际上，当我们将筛子整合到游戏设计时，我们便是假设没有人能够（或不可能）预测到结果。

实际上，尝试着预测筛子将如何滚动，并基于一个计划好的轨道去投掷它从而出现你想要的一面的做法被观察者们当成是一种“作弊”手段。你不应该知道筛子的整体理念。

一部分原因是我们正在面对包含随机性的游戏中两个独立的封闭系统。滚动筛子是一个封闭系统，与优秀的游戏系统并无关系。

这与其它类型的“不可预测”或“不确定”的事是不同的。就像象棋吧，它会限制玩家能够预测的圈数。此外，会出现什么事也是玩家预测不到的。然而，玩家可以通过游戏而不断学习并进一步分析可能性。我们可以使用部分象棋技能去探索不断增加的可能性空间并创造出更加可预测的能力。

所以尽管象棋具有不可预测性，但却不能说它具有随机性。为了运行，所有游戏都必须具有某种类型的不可预测性，但随机性却不是帮助它们做到这点的唯一方法。相机的不可预测性的来源与随机性的来源是不同的，因为玩家可能逐步减少并理解这种不可预测性。

随机性的类型

随机性可以被划分成两种类别：输入随机性和输出随机性。

输出随机性—-当我们想到游戏中的随机性时，它们便是输出随机性。输出随机性夹在玩家决策和结果之间。例如“Risk”（游戏邦注：古代流传下来的征服世界的游戏）或《Memoir ’44》中的掷筛子战斗，以及《X-Com》或《FTL》中的随机数字生成战斗。我将把不具有这类型随机性的系统称为“确定性”。

输入随机性—-这种类型的随机性会在玩家做决定前给予他们暗示。典型的输入随机性是《文明》或《Rogue》中的地图生成，或《Puerto Rico》或《Agricola》等职工安置游戏中的面朝上的砖块或纸牌。（人们经常使用“程序生成”这一词去指代数字游戏中的这类型随机性。）本文将不讨论这类型随机性，但是你们有必要了解它们的区别。

有趣的是，尽管这两种类型是截然不同的，但从技术上来看它们却是处于一个连续统一体中。在此我们需要注意的是，不负责任地使用输入随机性将会引起与输出随机性所遭遇的同样问题。

策略游戏学习引擎

策略游戏是一种让我们能够理解的引擎。就像我们玩一款游戏，不管获胜或失败，我们都是相连接的。当我们清楚系统如何运行时我们便会说“喔，我知道了！”为了发展，我们发现这一过程非常有价值且具有娱乐性。这是策略游戏的“必要乐趣”。

让我们进一步分解这一过程。

告知玩家—-玩家着眼于游戏状态，尝试着明确该如何行动。游戏通过他的“技能”基础提供给他信息—-这是关于系统以及它是如何运行的整体观察内容。

决定行动—-在剩下的游戏过程中，系统将对这一输入内容做出回应。在决定后将发生一系列事件，包括最终的胜利/失败；所有的实践都将作为游戏对于玩家的反馈，突出它们之间的一些休闲的关系。反馈同样也遵循着一个策略。

记录技能—-玩家将观察并记录这种因果关系并将其记录到自己的数据库中。之后玩家便可以使用技能而行动。（需要注意的是，这时候便会出现策略游戏的必要乐趣，但这当然也是依赖于其余功能。）

当一名玩家在玩游戏时，他将创造这种“技能文件夹”并变成一名强大的玩家。一款肤浅游戏中可能不具有多少这样的时刻，而一款具有深度的游戏将能够持续传达这样的时刻。这便是为何我们会基于策略上的“深度”去衡量一款游戏的质量。

那么我们该如何达到这种深度呢？首先，也是所有游戏设计师都意识到的意外的复杂性。为了创造复杂性，我们让游戏能够在玩家的游戏过程中生成复杂的意外情境。主教，骑士和赌棍与三个士兵和一名女王相对坑并不具有内在的复杂性；这里存在少量的可能数据。然而如果在棋盘上同时出发这两边势力，那么便可能出现许多不同的情境。

复杂性效率

而关于达到这种深度的第二种方法却还未被大多数设计师所察觉。这种方法包括识别复杂性的有效性：状态和过去状态的历史之间的相关性。

策略游戏在比赛过程中只拥有有限的状态。据我所知，象棋游戏中的平均数值是在40左右。而实时游戏并未真正分离“回合”，也仍然存在一些有限的有意义的状态，不管你如何进行划分。

如果你的游戏是由一系列相互联系的事件所组成，那么你便能够最大化可能出现的独特情境。我认为这一理念违背了许多认为随机事件的出现将提高独特情境的数量的人的想法。然而这却是事实。

拥有一个系统将能够推动你的意外复杂性达到最高效率。因为每种意外情境都考虑到了之前和之后出现的所有事件的最大差别量。

这里的粉色的蛋代表当前的游戏状态。在决定性游戏中，玩家将顺着比赛的时间线通过不同时间获得它们。而在随机性游戏中，时间线是相互独立的，即当前的游戏状态并不会受到影响。

在决定性游戏中，当前的游戏状态与整体时间线的每一部分是相互联系着。正因为这样，它被整合到了更复杂且更独特的结构中。这是以环境的形式表现出来，并提供了有关游戏状态的解释。

当然了，具有更高随机性的游戏拥有一些决定性元素能够提供给游戏状态一些环境。例如在像《召唤师之战》（游戏邦注：包括掷筛子战斗的回合制战斗游戏）等游戏中，你的召唤师的生命值以及单位的位置都是相互联系的，并且能够为游戏状态提供一些环境。

然而，游戏中的大量环境信息将不再具有意义。我攻击了你的单位，我滚动了筛子。我有可能会错过机会，而下一个回合你便会杀死我的单位。这一事件（即你杀死我的单位）并不是真正与我之前采取的任何行动随机联系在一起。这里所发生的情况是，我采取一个行动，然后随机发生了一些事，然后你便采取行动。这种关系是相互独立的，我们不能再使用我的行动作为当前游戏状态的环境差别。现在你的游戏已经不再是“A，因此出现B，因此出现C”。而变成“A，然后出现B，然后出现C”。

最重要的反馈是目标状态。一旦一场比赛结束，胜负条件将通过事件的发生过程传送一个命令，并为促成这一结果的每个事件揭示一个积极或消极的命令。这一行动还不错，因为它能够引出这一结果，并且这一结果能够引出其它结果，并因此成就了我的胜利。

这并不是说当玩家获胜时，他的所有行动都是正确的行动。然而这却提供了一个定位点能够传达每个其它行动。当然了，玩家的每一个行动都是为了更加接近获胜状态。一旦比赛结束，我们便能够看到这些行动是否有效及其原因。（正因为如此，玩家能够从观看回放并分析而获得许多同样的乐趣。）

总的来说，在玩了一款决定性游戏后，玩家可能会看到基于时间轴的一副连贯的战略图。而玩家可能会认为非决定性游戏是由一些不完整的图片所组成。基于这种方式，决定性游戏能够最大化其复杂性效率，而非决定性游戏却不能。就像非决定性游戏在添加复杂性的同时，决定性游戏则在加倍繁殖这些复杂性。

想象的深度

输出随机性并不能提升游戏的深度。在掷筛子中我们探索不到任何内容。我们只知道任何一面出现的几率是1/6。除此之外别无所知。

实际上这也模糊了结果。你可能玩得很好，但却仍然输掉了游戏。游戏让你做了徒劳无益的事，想想你是在哪个环节搞砸了，在什么时候你的游戏其实并不具有任何问题；这里的关键在于掷筛子。

因为徒劳无益，游戏似乎变得更加复杂了。游戏提供了不可靠的反馈，只有在玩了无数次游戏后你才清楚自己该忽视哪些反馈。从根本上来看，随机游戏因为将错误的信号整合到引擎中而延迟了学习过程（这也是游戏的必要乐趣）。这是创造深度表象的超级廉价的方法，这也是游戏设计师会被吸引的原因。

人类是追求模式化的动物。我们会观察云端中的数据，我们会观察静态的图像，我们会观察存在巧合的阴谋。这都是因为人类的进化不断推动着我们基于这种方式进行思考。同样的原因也导致人们认为自己在一些灌木丛中看到的影子是狮子等野兽。随着时间的发展，那些认为自己看到狮子的人变成了那些在真正看到狮子时会逃跑的人。而我们也继承了这些人的基因。

基于这一原因或其它原因，现在的我们会注重我们所看到的任何模式，并且只要存在游戏，游戏设计师便会一直利用这些模式。

就像赌博机一直都是利用心理技巧去引诱人们进行尝试。为了让任何人想要玩与老虎机或轮盘赌一样无趣的游戏，于是便出现了某种程度的自我欺骗行为。在某种程度上，玩家会觉得如果自己获胜了，那么功臣便是自己。否则他们怎么会倾注于此呢？从古代的宗教迷信到一些更加现代的想法，如“在筛子上吹口气”，或轻吻“幸运”物，或其它自欺欺人的赌徒谬误，我们发现为这些事件分配意义的方法其实就是在制造噪音。

具有较高随机性的策略游戏中的认真的玩家会在玩《召唤师之战》和《Hearthstones》时怀疑同样的技巧是否能够发挥作用。但为什么呢？如果玩家能够在不具有任何策略的系统中执行这一技巧，他们应该很容易相信这样的技巧能够作用于整体系统中的一部分内容。实际上，整合随机元素到一款策略游戏中能够让它更轻松地合并噪音和策略反馈，因为游戏中所发生的一些事件是真的具有策略性和决定性！

在这些游戏中存在一些真正的游戏技能，但同时也有一定量额外的“幻想技能”，并且正是这些幻想技能让游戏变得比实际更具深度。实际上，大多数玩家会利用系统去快速解决某些内容，而随机性便是其中的决定性元素。

反论

我研究了这一问题好几年了，而随着时间的发展我遇到了一些反论。

游戏设计师兼博主Danc（他的网站是Lost Garden）在多次回应我的论据时都是这么对我说的：“输出随机性只是下一回合的输入随机性。”从根本上来看他认为输出随机性和输入随机性之间不存在真正的区别。

该论据带有两个主要的缺陷。一个缺陷是它好像未察觉到更大的策略视图的可能性，即能够提供你正在错过的许多复杂性效率。

另外一个缺陷便是即使那输出随机性真的是下一回合的输入随机性，这也是我所谓的“不公平的输入随机性”。它们如此接近你，从而导致你根本没有时间做出回应。现在你拥有完全不同的游戏状态，但却不存在可识别的原因。在某些游戏中，你可能玩得很好，但如果整合了这一论据，你可能就会遭遇失败。在其它游戏中，你可能会因为掷筛子而未利用这一论据。当游戏向玩家近距离提供输入随机性而导致他们无法做出规划时，它便等同于输出随机性。所以反馈是因为人为原因而延迟。

讽刺的是，我同意Dan关于输出随机性与下一回合的输入随机性之间不存在重要区别的观点，尽管我认为它们同样很糟糕。

为了把事情讲清楚，让我们想象你的角色拥有一个“命中的”筛子去对抗一个强大的怪兽。他滚动了筛子，但却错过了机会。没关系，毕竟这只是下一回合的输入随机性。他尝试着再次攻击，但却再次错过了机会！这时候，你可能已经输了，而这并不是因为你做出的任何决定所导致的。

“有些游戏需要输出随机性才能运行。”

如果你因为风险而抛弃了筛子滚动，游戏便不能够运行。

这意味着它们是些肤浅的游戏。这是可被理解的，因为创造具有深度的统一系统是件非常非常困难的事。然而这并不是对于随机性的辩护；这反而是在暗示你的设计有多糟糕。

“如果存在随机性，那么这便全部是关于风险管理。”

这一论据背后的理念是拥有随机元素将在游戏中添加“整合你的几率”的元素。你必须衡量结果A的发生几率与结果B的发生几率，以及结果A的利益与结果B的利益，这能让游戏变得更有趣。从根本上来看这结合了几率与估值。

这种类型的风险管理并不局限于随机游戏中。在你还未解决的任何游戏中，你所作出的每个行动在某种程度上看来都是你必须管理的风险。例如在象棋中可能存在两大策略—-策略A和策略B，但策略B拥有比策略A更高的报酬。那么这时候随机性便是不必要的。

关于“估算几率”这方面，决定几率从来都不是什么有趣的事，特别是当你在谈论像扑克游戏中的计算纸牌时。在决定性系统中估算几率可能更加困难，但是因为在一款有趣且动态的策略游戏中存在各种变量，所以这种估算变得比较有趣了。

“随机性并不重要—-你只需要尽自己所能做到最好！”

这里的论据就像是：“如果你在乎随机性，你便太过在乎输赢了。只要好好享受乐趣就好！”

这一论据并不是在为策略游戏中的随机性辩护；相反地，这是在为玩具中的随机性辩护。策略游戏拥有输赢的条件。如果你告诉我们在《FTL》中忽视这点，那就等于你在说《FTL》是个玩具。

“担忧更广泛技能范围的玩家能够彼此对抗。”

如果一位大师和一位新手一起玩象棋，那么结果便不会有趣，或者只能让其中一方感到满足。这一论据告诉我们应该丢些随机性到游戏中。

当然了，这就像将孩子与洗澡水一起倒掉一样。为了让玩家觉得自己是与同样技能水平的人一起游戏，你已经严重破坏了游戏。而关于这一问题的真正答案应该是适当地安排比赛。

“随机性将让游戏变得更像现实生活。”

为了快速反击这一论据，让我们假设策略游戏中存在一组价值，我们可以将其与模拟游戏中的价值区别开来。

“带有随机性的游戏仍然带有技能！”

没错，我也不会否认。但是问题在于，在实际情况下你将探索更少的领域，因为许多游戏都浪费在了错误的随机结果中。

其它反馈

我应该注意的是一些输出随机性类型并未被如此看待，但是因为具有相似的功能，所以它们也带有同样的问题。

同步行动—-例如在RPS中尝试着猜测敌人将做些什么便是一种有效的随机设定。实际上，这也是我们为何会使用它去决定谁清除了垃圾，我们认为这是公平的，因为这是随机的。整体原因是人们同意使用RPS作为谁清除了垃圾的决定元素，因为他们知道他们或者对手都没有办法去提升自己的几率。

执行—-游戏中的执行是关于“可以”，而不是“应该”。你是否能在我跳起来踢你的脸前按压这系列按键？也许执行仍然胜于随机性，因为你至少是擅长它的。然而在一场单独的比赛中，它们却几乎相同。“你想要做什么”以及“你的身体是否能够实现欲望的输入”之间所涉及的复杂的化学元素，紧张感，肌肉和组织等等都带有许多错误的空间。当你选择为你的《Dragon Punch》创造输入内容时，它是否真的可行？这便是有效的随机性。

结论

我们所收集到的有关游戏设计的随机性观点其实是长久以来都未发生多大改变的内容。而这时候我们真正需要做的便是更加认真地去思考这一问题。

我并不是在说游戏设计中不存在任何类型的随机性的存在空间。实际上，我真的非常支持多人游戏中平衡且没有太多变化的输入随机性。并且单人游戏也需要输入随机性。

然而我们却应该避免基于各种形式的输出随机性。你只有在创造博彩游戏或不满足系统深度的时候才能够使用这种类型的随机性。

（本文为游戏邦/gamerboom.com编译，拒绝任何不保留版权的转功，如需转载请联系：游戏邦）

Randomness and Game Design

by Keith Burgun

For thousands of years, we’ve relied on randomness of various kinds to help our interactive systems work. While there will always be a place for randomness of all sorts in some kinds of interactive systems, I believe the current assumptions with regard to randomness in strategy games are largely wrong.

The major point I’d like to make is that noise injected between a player’s choice and the result (here referred to as output randomness) does not belong in a strategy game.

What is “randomness”?

For the purposes of this article, randomness refers to “information that enters the game state which is not supposed to ever be predictable.” The process by which random information is generated is designed to be something that humans can never figure out. Classic examples of random systems are rolling dice, shuffling cards, or random number generators.

Technically speaking, a die’s rolling pattern is not actually “random”. It’s simply responding to physics, and a computer could take information about how a die was thrown and predict the number that would come up. We use dice precisely because a human being can’t do that. In fact, when we incorporate dice into our game designs, we do it under the assumption that no human will ever be able – nor likely even try – to predict the outcome.

In fact, trying to actually predict how the die will roll, by perhaps carefully tossing it with a specific, intended trajectory, so that it rolls to a side you intend, would likely be called out as “cheating” by any observers. The whole idea with a die is that you’re not supposed to know. It is noise that must remain noise, forever.

Part of the reason for this is the fact that we’re actually dealing with two separate, closed systems in a game that contains randomness. A rolling die is a closed system of its own that really has nothing to do with the greater game system.

This is distinct from other kinds of “unpredictable” or “uncertain” events. In chess, for example, players have some limit to the number of turns they can look ahead. Beyond that point, the events that occur are indeed unpredictable for that player. However, players can and do learn to look further and further down the possibility tree as they get better at the game. Part of the skill of chess is being able to explore that ever-increasing possibility space and come out with more predictive ability.

So while chess does have unpredictability, it does not have randomness. All games must have some kind of unpredictability in order to function, but randomness isn’t the only way to achieve that. Chess’s source of unpredictability – a highly complex game state – is unlike a random source in that it can slowly be chipped away at and understood.

Types of Randomness

Randomness can be separated into two categories: input randomness, and output randomness.

Output randomness – when we think of randomness in games, we’re usually referring to this. Output randomness is noise injected between the player’s decision and the outcome. Examples would be the dice roll combat in Risk or Memoir ’44, or the random number generation combat in X-Com or FTL. I will refer to systems that do not have this type of randomness as “deterministic”.

Input randomness – this type of randomness informs the player before he makes his decision. Typical examples of input randomness would be map generation in Civilization or Rogue, or face-up tiles or cards in a worker placement game like Puerto Rico or Agricola. (People often use the term “procedural generation” to refer to this kind of randomness in digital games.) This article will not focus on this type of randomness, but it’s important to know the distinction.

Interestingly, while these two types are certainly distinct enough from each other to warrant the classifications, they do technically exist on a continuum. Without going into much detail on it, it should be noted that irresponsible use of input randomness – where the player has very little time to respond to the new information, or where the game generates problems of wildly varying difficulty match to match – cause similar problems as output randomness.

The Strategy Game Learning Engine

Strategy games are engines that allow us to understand them. We play a game, we win or lose, and we make connections. “Oh, I see!” we say as we figure out some element of how the system works. For evolutionary reasons, we find this process enriching and entertaining. This is the “essential fun” of strategy games (largely the premise of Raph Koster’s book, A Theory of Fun for Game Design).

Let’s break down the process further.

Informing the Player – The player takes a look at the game state, trying to figure out what move to make. He is informed by his “skill” database – the collective total observations about the system and how it works that he’s made up until now.

Deciding the Move – A move is chosen, and the action is taken. As a result, the game state is changed. Alternatively, this could be “deciding the strategy” – a series of moves that collectively adds up to a larger strategic gambit.

Feedback in Outcome – Over the course of the rest of the game, the system responds to this input. A series of events take place after that decision, including the final win/loss event; all of which serve as feedback for the player, highlighting some causal relationship between them. Feedback also comes following a strategy, or at the end of a game.

Recording Skill – The player observes and records this cause-effect relationship and records it to his database. The player can then use that skill to make moves in the future. (Notably, this moment is where the essential “fun” of strategy games comes from, but it of course relies on the rest of the machine to function.)

As a player plays a game, over many matches, he builds to this “skill folder” and becomes a stronger player. In a shallow game, there might not be very many of these moments, whereas a very deep game can continue delivering these moments for decades if not lifetimes. This is generally why it’s considered a good quality for games to be strategically “deep”.

How do we achieve that depth? Well, the first way, which all game designers already understand, is emergent complexity. In order to create complexity, we design our games so that they generate complex emerging situations throughout play. A bishop, knight and rook against three pawns and a queen is not inherently complex; there’s a very small amount of data there. However, unleash these two forces on each other on a chessboard, and the amount of possible situations that could emerge is huge.

Complexity Effectiveness

The second method for achieving depth is, as far as I can tell, not understood by most designers today. This method involves being aware of complexity effectiveness: the amount of correlation between a state, and the history of past states.

A strategy game only has a finite number of states throughout a match. From what I can find, it seems that the average number of moves in a chess game is somewhere around 40, for example. A real-time game doesn’t have discrete “turns” per se, but there’s still a finite number of meaningful states, no matter how you divide it up.

If your game is a continuous series of events that lead causally from one to the other, then you are maximizing the amount of unique situations that can occur. I think this idea is counter-intuitive to many, who think that random events occurring somewhere in there must increase the amount of unique situations. However, the opposite is actually the case.

Having a system be entirely deterministic causes your emergent complexity to be maximally effective. This is because each emerging situation is given the maximum amount of contextual nuance by all of the events that came before and after it.

The pink egg here represents the current game state. In the deterministic game, it is getting pulled at by the events along the timeline of the match. In the random game, the timeline is severed and the current gamestate isn’t affected by as much.

In the deterministic game, the current game state has ties to every part of the entire timeline. Because of that, it is being pulled into a more complex and more unique shape. What this is illustrating is the way that context, when causally related, provides meaning to a game state.

Of course, even highly random games do have some deterministic elements that do provide some context to game states. For instance, in a game like Summoner Wars (a turn-based wargame involving dice roll combat), the health of your summoner and the positions of units are both relatively deterministic and do provide some context for game states.

However, the vast majority of contextual information in a game no longer has meaning. I attacked your unit, and I rolled the dice. It came up as a “miss”, and then next turn you killed that unit. That event – you killing my unit – is not really causally linked anymore to the actions I took beforehand. What happened was that I took an action, then something random happened, and then you took an action. The tie has been severed, and we can no longer use my move as contextual nuance for our current game state. Your game is now no longer “A, therefore B, therefore C”. Instead, it is now “A, then B, then C”.

The most significant bit of feedback is the goal-state. Once a match has ended, that win/loss condition sends a charge backwards through the course of events, revealing a positive or negative charge for every event that led to it. This move was somewhat good because it led to this, which led to that, which led to this, which led to that, which led to my win.

This is not to say that when a player wins, all his moves were good moves. However, it does provide an anchor point that informs every other move. Of course, moves are made in an attempt to get the player as close as possible to the win state. Once the match ends, we can now see how and why each of those moves was effective. (Because of this, players can get a lot of the same kind of fun out of watching a replay and analyzing it as they can from playing the game.)

Overall, after playing a deterministic game, a player is left looking at a coherent strategic picture that has been painted over the axis of time. Alternatively, the non-deterministic game could perhaps be considered more like a number of incomplete pictures. In this way, the deterministic game maximizes its complexity effectiveness, and the non-deterministic game does not. The non-deterministic game is adding complexity, whereas the deterministic game is multiplying it.

Imagined Depth

Output randomness does not increase the depth of a game. How could it? There is nothing to explore in a dice-roll. We all know that the odds are 1/6 for any face coming up. There is literally nothing else to know or explore.

What it actually does is obscure the outcome. You may have played perfectly, and still lost. The game has now sent you off on a wild goose chase, thinking about where you must have messed up, when in fact your play wasn’t the problem; dice rolls were.

Because of that wild goose chase, the game seems more complex than it is. The game provides unreliable feedback, and only after playing many, many games will it become clear which feedback you should ignore. Essentially, random games delay learning – the essential fun part of games – by injecting false signals into the engine. It’s a super-cheap way to create the appearance of depth, which is why it’s incredibly tempting for game designers.

Humans are pattern-seeking animals. We see figures in the clouds, we see images in the static, and we see conspiracy where there’s only coincidence. The reason is due to the fact that it’s evolutionarily favorable to think this way. The same quality that causes a person to think he saw a ghost in some rustling bushes is the quality that causes a person to think he saw a lion in some rustling bushes. And over time, those who thought they saw a lion were the ones who escaped when there actually was a lion. Those were the people who passed their genes along to us.

For this reason and others, we’re now both cursed and blessed with seeing patterns everywhere we look, and game designers have been exploiting this in us for as long as games have existed.

Gambling machines have always relied on psychological tricks to exploit us into playing them. In order for anyone to actually want to play something as vapid as slots or roulette, some degree of self-deception has to take place. On some level, the player has to feel like he is responsible if he wins. Otherwise, how can they be invested at all? From ancient religious superstition (the Gods are angry at me!) to their more modern counterparts, like “blowing on the dice”, kissing “lucky” items, or other self-deceptions such as the gambler’s fallacy, we find ways to attribute meaning to events that are actually pure noise.

Serious players of highly random strategy games tend to be skeptical that this same trick could be working on them when they play their Summoner Wars and their Hearthstones. But why? If players are able to perform this trick on themselves in a system that has no strategy at all, it seems very easy to believe that such tricks would work on a smaller percentage of the overall system. In fact, baking random elements into a strategy game makes it all the easier to conflate noise and strategy feedback, because some of what happens in the game really is strategic and deterministic!

In these games, there is the actual skill of the game, but then there is also an additional “phantom skill” amount, which makes the game seem vastly deeper than it is. In actuality, most players probably have the system close to solved somewhat quickly, and the randomness is the deciding factor.

Counter-Arguments

I’ve been arguing this position for a few years now, and over time I’ve encountered a number of counter arguments that I’d like to address.

“Output randomness is just input randomness for the next turn.” – Game designer and blogger DanC of the Lost Garden has said this to me numerous times in response to my positions. Basically he’s arguing that there is no actual difference between output randomness and input randomness.

This position has two major flaws. One is that it seems unaware of the possibility of a larger strategic picture that could be providing tons of complexity effectiveness that otherwise you’re losing out on.

The other major flaw is that even if it’s actually input randomness for the next turn, that’s what I call “unfair input randomness”. It’s up so close in your face that you don’t have time to respond to it. You now have a significantly different game state than you did a second ago, and there’s no discernible reason for it. On some games, you might play optimally, but get put into this position and lose anyway. On other games, you don’t get put into that position because the dice rolls go your way. Input randomness, when put up close enough to the player so that he can’t plan around it, is basically output randomness. Feedback is being artificially delayed.

Ironically, I agree with Dan’s sentiment that there’s no significant difference between output randomness and input-randomness-for-the-next-turn, although I think they’re equally bad.

To really drive the point home, imagine a scenario where you have a character who has a “to-hit” dice roll against a tough monster. He swings, and he misses! Well, that’s ok, it’s just input randomness for the next turn, after all! He tries to attack again, and misses again! At this point, you may already have lost, and it wasn’t because of any decision you made.

“Some games need output randomness to work.”

If you were to just rip the dice rolls out of Risk, it definitely wouldn’t work.

This simply means that they are shallow games. It’s understandable, because creating a coherent system that is deep is very, very hard to do. However, this is not a defense of randomness; more an indication of a weak design.

“If there’s randomness, then it’s all about risk management.”

A favorite of poker players. The idea behind this argument is that having random elements adds a “factoring in your odds” element to the game. You have to weigh the odds of outcome A happening against the odds of outcome B against the benefit of outcome A and the benefit of outcome B, and that makes games more interesting. Essentially, it’s combining odds and valuation.

This kind of risk management is not unique to random games. In any game that you haven’t solved, really every move you make is to some degree a risk that you must manage. In chess, there could be two major strategies – strategy A and strategy B. You might figure that A is more likely to work than B, but B has a bigger payoff than A, for instance. Randomness isn’t necessary.

As to the “calculating odds” aspect of this, determining odds is never interesting, especially not when you’re talking about something like counting cards in poker. Calculating odds in a deterministic system might be harder to do, but it would certainly be far more interesting due to all of the variables at play in a good, dynamic strategy game.

“Randomness doesn’t matter – just do the best you can!”

The argument goes something like, “if you care about randomness, you care too much about winning. Just have fun!”

This argument is not actually a defense of randomness in strategy games; rather, it is a defense of randomness in toys. Strategy games have a win/loss condition. If you are telling us to ignore that in FTL, then you are saying that FTL is a toy and that’s why randomness is OK.

“Players with a wider skill range can compete against each other.”

If a grandmaster and a newbie play chess against each other, the result won’t be interesting or fulfilling for either party. That much is true! This argument suggests that the answer to that is to throw in some randomness.

Of course, that’s throwing the baby out with the bathwater. You’ve now severely damaged your game for the sake of presenting people with the illusion of more-similar skill levels. The real answer to this problem is good matchmaking.

“Randomness makes a game more like real life.”

To quickly counter this argument, let’s simply assume that there is a set of values for strategy games which we can separate from the set of values for a simulator.

“Games with randomness still have skill to them!”

True, and I haven’t argued otherwise. The issue is that on a practical level, you will be able to actually explore less of that space in your lifetime, since so many of the games are essentially wasted on false random outcomes.

Other Feedback Distortions

I should also note a few types of output randomness that are not usually regarded as such, but function so similarly that they have many or all of the same pitfalls.

Simultaneous Action – Trying to guess what the opponent will do in RPS, for example, is effectively random. In fact, that’s why we use it to decide who has to go take out the trash – we consider it fair, because it’s random. The whole reason people agree to use RPS as the determining factor for who will take out the trash is because they know that there is nothing that they or their opponent can do to increase their chances. (Sure, there’s some study that says people are slightly more likely to play rock. But did your opponent read that study, or not? You’re now back to square one.)

Execution – Execution in games is a matter of “can”, not “should”. Can you press this sequence of buttons before my jump kick hits you in the face? Execution is still slightly better than randomness probably, due to the fact that you can at least get better at it. However, inside of a single match, it’s basically the exact same thing. The complex chemicals, nerves, muscles and tissues that stand between “what you wanted to do” and “whether your body actually makes the desired input” has tons of room for error. When you choose to make the input for your Dragon Punch, will it actually work? It’s effectively random.

Conclusion

Our collective perspective on randomness in game design really hasn’t budged much in 4,000 years. It’s time that we really gave this question some serious thought.

I’m not arguing that there is no place for any kind of randomness in game design. In fact, I argue strongly in favor of well-balanced, low-variance input randomness in multiplayer games. And single player games require input randomness.

However, output randomness in all its forms is to be avoided. The only time you should use randomness of that kind is if you’re making a gambling machine, or if you’re insecure about the depth of your system.(source:gamasutra)