作者：Daniel Cook

我们该如何让玩家按照自己的时间安排玩到一起？这是设计师创建多人游戏时面临的一个关键性挑战。

前提条件

我们看到了创新多人系统的繁荣发展。在之前的年代有许多游戏可能会用到的默认模型（配对，通过邮件玩游戏），今天的游戏处于一个完全同时在线至完全异步，以及介于两者之间的状态。像《暗黑之魂》这种游戏之前是单人游戏，但包含异步交互性（留下信息，死亡）以及完全同时在线（另一玩家加入你的游戏，参与PvP或合作战斗）。

我们进入了一个多人游戏玩法的黄金时代。伴随着云计算的出现，服务器成本急剧下降。宽带网络和不间断的移动连接在全球迅速扩展。游戏内付费、众筹和服务型游戏等高业模式也在不断发展，支持都大规模的长期活跃社区。设计师要在这些新性能下创造新型多人游戏体验。

挑战

但多人模式制作成本昂贵，并且具有极高的失败风险。通常团队需投入50-100%的开发预算来创造一个多人模式。这看起来很值当。在开发过程中，团队每周就会一起玩游戏并获得乐趣，以确信该多人游戏将成为下一款《英雄联盟》或《反恐精英》。

当游戏进行入测试阶段，就要面临大把真正的玩家群体的考验。在刚发布时，多人游戏通常只有几周时间会有活跃的多人模式活动。一开始太多人出现，之后却是人气不足了。玩家会偶发性地访问游戏，玩家体验也不太可靠。活跃配对也降至空无一人。传统的配对大厅（始于90年代的设计）空无一人，并且永远不再门庭若市。游戏的多人模式部分似乎已经沦陷为死寂。

我认为这是一个后勤上的挑战。有不少想玩游戏的玩家。但游戏却将这些玩家引向脆弱而难以自继的社会。

有没有能够让我们以一种更为严格的方式创造新系统的多人模式后勤原子元素？简单地照搬之前的多人模式并不管用。而要创造新多人模式，我们必须拥有允许我们清楚而准确地操纵类似后勤、同时在线和互动时间安排等话题的概念性工具。

关于多人模式的概念

以下是我认为设计一款多人游戏所涉及的概念。

互动

你可以将任何多人系统拆分为一系列互动行为。互动是玩家通过游戏系统与另一玩家交互的行为（游戏邦注：这可以是聊天，也可以是攻打他人）。这些就是你游戏的多人模式动词。通常游戏有一系列单人玩家动词（移动、退出等），并混合另一系列多人交互行为。互动有一个广泛的多人属性，例如频率、范围、模式等。

如果你以时间来反映互动，可能就是以下情况：

*玩家开始互动

*他们结束互动

*他们等待回应

*如果没有回应，他们就会离开

互动并非新鲜事物。其结构与原子游戏循环中的结构相似。但它至少有两名参与者，绝非一个单人循环。这些理念可以传达Chris Crawford调整80年代游戏设计理论时所提出的理念。这也是所有专业游戏设计师应该知道的基础内容。

初始循环：

*模型A：玩家规划一个行动，以及一个目标玩家或群体。

*行动A：玩家执行一项操作。

*规则：操作结果由游戏逻辑来裁决。

*响应A：玩家A看到由游戏产生的直接结果。

*响应B：玩家B看到游戏产生的直接兴奋剂果。注意玩家B所看到的情况与玩家A所见的一切并不相同。这会导致相异的心智模式，并产生隐藏信息等玩法概念。

往复循环

*模型、行动、规则、响应B：目标玩家试图理解发生什么情况，并计划一次回应。

*由此开始该循环反复在参与者之间发生。

互动频率

产生同时在线印象所需的必要互动频率是多少？你可能会发现自己在类似《文明》的战略游戏中需要每5分钟就互动一次，而在《反恐精英》式的动作游戏中却需要每200毫秒就互动一次才能产生同样的印象。

总体而言，更高的互动频率，意味着玩家之间交流更多的信息。这可以增加形成关系的速度。

有了如此多互动变量，玩家认知就会随着频率到达阀值而发生独特的相变。简单地更改互动间的时间，我们可以得到极为不同的玩法形式（以及相关的后勤挑战）：

*即时：当频率到达一个点时，玩家会认为互动是“即时”的——玩家开始和结束一项互动，之后在转向下一个代务前看到一个回应；以及互动重叠之时。例如，聊天可以让人获得即时感，尽管两者的回应之间通常相隔1分钟以上。即时系统对持续性的要求并不高，但运营和开发成本却甚为昂贵。

*异步互动：玩家开始和结束一次互动，之后在无需看到回应时退出游戏时的频率可以视为异步行为。通常情况下，你可以创造一些持续性，以便玩家在过后登录查看互动结果，并规划下一次回应。

互动类型

互动类型多种多样。可以将它们视为玩家互动的方式。

*空间虚拟角色互动：至少两个虚拟角色彼此交互。《雷神之锤》中的射击玩家就是一个绝佳典型；在《Journey》中追随一名玩家亦是如此。

*空间环境互动：玩家通过中间环境进行互动。在《Minecraft》中，玩家创造了可供其他玩家探索的城堡。例如在《Bomberman》中，玩家设置可以烂开走廊或者伤害他人的炸弹。

*装饰和显示：玩家通过自己的穿着或武器、宠物和房屋的装饰来传递身份、联盟和历史信息。

*经济：玩家给予不同资源、或为其交易、付费以进行改造或将其转移至另一玩家。例如将宝剑出售给另一玩家以换回金币。或者为附近的一名玩家购买提升其命值的道具而支付魔法值。

*文本：网络游戏最常用的就是文本介绍语言。因为这种设置成本较低，并且有大量处理常见问题的工具（例如，垃圾邮件过滤器、格式惯例）。以键盘配合使用最佳。

*声音：声音可以提供包括情感、年龄、性别等更多微妙的额外信息。但具有群体规模、带宽等限制，并且在过滤情况下最为脆弱。

*肢体语言：在沙发或圆桌等本地空间中，我们会挑选高带宽的交流方式，例如面部表情、姿势、身高和实体存在感。当一名高个子帅哥以你四目相对，要求你与之交易你的珍宝时，你可能会从中感受到与其他交互类型不同的信号。这就创造了丰富的即时多人玩法。但是，这也难以明确调整及合并到游戏系统中。

社区大小

在你增加社区参与者数量时，游戏中也会发生大规模的相变。

*1名玩家：精通、进展、探索、叙事均是可行的设计工具。

*2名玩家：交流、关系、地位、赠礼、交易、合作以及竞争就具有可行性了。

*3-4名玩家：结盟、政治、八卦、形成规则便具有了可行性。

*小型团体（5人以上）：群体vs群体互动，官方领袖、角色分化、官方惩罚。

*中型团体（12人以上）：派系、物物交易经济以及流放。

*大型团体（40人以上）：等级制度（领袖和亚领袖）、基于现金的经济、角色执行。政体、公众编撰的社会准则。

*极大型团体（200人以上）：商人阶级、基于市场的定价、法制政府、下层阶级、名人、宣传机构。此时玩家已经无法认识游戏中的每一个人，并且官方系统也要求社会法则行之有效。

*超大型团体（1000人以上）：投票、城市规模的生产投入。在这种规模中，游戏中很少发生动态变化。

我是在玩家互动情境下定义这些群体。实际的游戏制作可能更为庞大。例如，《Realm of the Mad God》的交易中，我们可以看到成百上千人群中的两位玩家进行交易互动。在考虑团体大小时需要考虑的两个法则就是：

*这一行为影响或瞄准谁？这可以大概估测你的系统需要支持的团体大小。

*这一行为有必要用上更大的群体规模吗？如果不是，你通常可以通过瞄准一个小型群体规模的不同实例来进行设计。

这些数据的实际转移点会根据情境因素而波动。例如，如果游戏中的交流渠道非常差，抑制了玩家保持联系的能力，那么极大型团体的动态转型可能变为60或70人。

此外，大型团体不可避免地由小型团体组成。所以一旦添加了系统，数量较少的群体动态仍然存在。

大型团体的风险

不少设计师很容易陷入制作理论上可支持成千上万玩家在同一个房间中互动的大型多人游戏这种倾向中。但是，技术和设计成本很高，其益处却很微小。超过150-250人后，你的游戏就超过了Dunbar所提出的维持有意交联系的生物极限理论。而这些多余的用户最终会被玩家视为一些毫无意义的数字或抽象内容。使用一个简单的模拟或投票系统通常可以获得下一个最大群体的主要益处。

《Realm of the Mad God》作为一款拥有40-80名玩家动作系列，以及150人的交易/互动中心这种极具可玩性的MMO游戏。玩家不会淹没在上千名其他玩家中。

这一现象产生了关于设计强调“大型多人”体验的严肃问题。一个听起来令人兴奋的理念（例如“让100万玩家创造一个新的社会！”）并不意味着它就是一项明智的设计。

互动范围

有多少人会产生单次互动影响？玩家可以同一个人进行互动，或者与以下群体中的一者互动：

*瞄准小团体中的玩家互动：在较小团体中，你可以展开类似于对话的交流。这里有一个可以清楚定义能够快速稳定一系列共享词汇和社会准则的互动循环。

*瞄准大团体中的玩家互动：在较大团体中，你可以看到更多广播场景，以及更广泛但较少针对个人的互动。与大型团体互动时，经常可以看到大量包含海量信息涌向接收者的回应。由于人们之间的传话，极端的回应也更为常见。

互动程度

*平行：玩家的行为彼此不同。一辆幽灵跑车极少对另一名玩家产生影响。这里的好处通常在于它可以产生一种存在感，但也可以同积分排行榜此类更低频的零和互动相绑定。

*零和：一名玩家的行为会阻挡或减少另一玩家的互动。在《哈宝旅馆》中，由于一名角色的占位符会阻碍另一角色占领同个地点，因此其中的移动就是一种零和互动。

*非零和：一名玩家的行为会造福另一玩家。在《Realm of the Mad God》中，射击一名敌人会让该敌人更倾向于杀死另一玩家。杀死敌人可以让屏幕中的所有人都得到一定的XP。

配对

配对是由电脑将一名玩家介绍给另一玩家，以便他们进行互动的仲裁行为。

这是配对的广泛定义，但在范围庞大的多人系统中却甚为管用。例如，传统主机游戏可能会通过要求玩家在一个共享大厅中手动加入某一特定游戏而为其配对。在《Realm of the Mad God》中，玩家会注意到共享地图上的玩家群体，并瞬间移动到他们那里。这两种都属于配对形式，但他们在玩家眼中的出现形式截然不同。

你可以在等待时间中将配对抽象为另一种互动形式。

配对窗口

在这个时间中，你要将一名寻找多人体验的玩家介绍给另一名玩家。如果该窗口太长（并且玩家在这个时间段中无事可做），那么他们就会离开游戏。

配对失败

当玩家上线，但却没有看到另一名玩家即时在线时，玩家就会很快生乏并离开游戏。这通常是有趣的多人体验生成的一种默契，如果你无法在数秒内传递这一体验，你的游戏就可以判断为失败的产品。

让开发者抓狂的是，另一玩家在一分钟后上线了，也体会到了同样的事情。如果一名玩家在游戏中逗留得够久，就会等到另一名玩家。

计算日常失败阀值：如果配对窗口在数分钟内是W，那么当日常活跃用户少于1天内的分钟/W时，游戏就会失败。打个比方，如果人们只肯等半分钟，那你就需要1440/0.5或者说2880名的日活跃玩家。因为我们要以统计过程以及玩家在特定时间的高峰期来处理这一情况，所以实际结果不尽相同。

这看起来相当合理，但如果你主要是在小团体的朋友中配对，玩家可能会觉得自己所认识的人好像都不在线。

分裂

当玩家人口被社交群体、游戏模式、玩家技能水平、时间及其他因素所分隔时，游戏世界就开始分裂了。这就减少了配对系统可寻找的同时在线玩家人数，从而增加了配对失败的概率。

分裂案例：假设一款游戏拥有3个多人模式，并将玩家配对划分为10个技能种类。如果日常玩家在线的失败阀值为2880（引用上述例子），那么最糟糕的情况就是，你需要3*10*2880或者说8万6400名同时在线玩家，才可能让大家都找到自己的第一个选择。

分裂还会蔓延到设计中。有些人想添加另一事件或另一游戏模式。代码是免费的，那么为什么不这么做呢？当然玩家会自我筛选。但他们很少自己动手，多数时候是困惑为何配对体验如此令人痛苦，之后在抓狂中离开游戏。因此要避免分裂蔓延的趋势，尽量将玩家全部集中在一个容易配对的桶里。

同时在线比率

任何游戏都有一定数量的活跃帐号，以及同时在线的玩家数量。玩家无法持续玩游戏，经常会不在线。例如，一款MMO游戏可能有100个活跃注册用户，但仅有10名玩家会一直在线。其同时在线的玩家比率就是10:1。

通常游戏的同时在线比率如下：

*MMO——10：1

*在线主机服务（例如Xbox Live）——25：1

*个人主机游戏——150：1

*Flash游戏——250：1

*沙发多人游戏——1000：1（这一点非常滑稽，很多玩家一年只玩几次沙发多人游戏）

活跃用户陷阱：开发者的一个普遍误区在于，认为健康的多人游戏社区会有较高的活跃玩家数量。但是，你应该去看看实际的同时在线用户，因为许多游戏类型拥有极端的同时在线比率。一款游戏可能拥有1000名玩家，但这些玩家每次登录时间仅有5分钟，并在一周时间中逐渐延伸，你的平均同时在线玩家就是0.5。如果你的配对系统不能很好地处理这些不定时、零星出现的玩家群体，那么游戏就会走向死路。

关系强度

由于玩家之间的独特关系，并非所有玩家互动都是相等的。玩家会在游戏内外创造与其他玩家间的复杂社交模式。玩家是通过简单、传统的模式认识陌生人，而通过成百上千万分钟的往复序列而建成的复杂个人模式才能结成密友。

创建他人的心智模式具有昂贵的生物运行成本。我们似乎只能保证5-9种详细模式在任何时候的活跃性，虽然我们可以存储更多不同层次的细节。友谊是通过长期交往而建立起来的珍贵、复杂关系。

与陌生人、好友玩游戏具有多种好处和权衡，基于友好情感的玩法通常最受欢迎。游戏可以通过促进重复的积极互动而创造好友。其频率越高，就越快形成好友关系。关系强度存在一定范围，但通常有两种普遍类型：

*与陌生人的多人游戏

*与好友的多人游戏

与陌生人的多人游戏

让我们先处理陌生人在线多人游戏的问题：

优势：

*任何玩游戏的人都可以无需考虑原来的社交关系，与任何人配对玩游戏。这种模式在玩家基数较少的时候极受欢迎。通常这意味着如果有10名玩家在线，那么这10个人就可以一起玩游戏。

*陌生人，尤其是年轻男性，通常很容易同他人竞争。这意味着强调公开战斗的PvP游戏很容易为某些陌生群体创造趣味。

劣势：

*陌生人之间的关系脆弱，难以自然促进合作等有利社会的活动。

*如果玩家想相互竞争，就要考虑到技能差异的问题。这会迫使开发者将新手与骨灰级玩家相隔离，从而分裂玩家群体。

*并非所有玩家都喜欢竞争过于激烈的玩法。有些玩家喜欢合作，有些喜欢通过操纵社交关系，为地位而竞争。这在陌生人情境中很难实现。

与好友的多人模式

优势：

*玩家更容易安排游戏时间。

*可以促进需要合作和交流的趣味活动。

*更易于实现不同技能水平之间的指导。

*竞争玩法仍然具有可行性。

劣势

*现成社交群体未必都对特定某款游戏感兴趣。

*现成社交群体未必都有统一的游戏时间安排。

*好友圈子很小。粘性高的玩家一般拥有5-9个密切的好友。随机型的熟人数量更高，但实际行动更像陌生人。如果你有10名好友，而游戏的同时在线率是25：1，你很可能无法遇到他们同时在线的情况。

解决多人模式后勤问题的工具

目前，我所提到的还只是多人模式背后的概念。现在我们将深入探讨一些可用的普遍模式。以下是三个广泛的框架：

*基于配对的游戏

*基于房间的游戏

*异步游戏

工具：基于配对的游戏

由于体育运动和桌游基于事件配对的漫长历史，电脑多人游戏通常也会让配对玩家在特定时间开始，特定时间或到达胜利条件时结束。

配对是用于许多主机和PC网游的默认后勤模式。它们存在极大的问题。配对互动的时间太短暂，而在此期间却要求大量玩家出现，以便大家成功进入游戏。如果你进不去，那就需要再等再一轮配对。如果这个时间超过了等待窗口，你就得退出。考虑到同时在线比率，分裂问题以及微小配对窗口的负担，只有最热门的配对型网络游戏才能幸存下来，这一事实就一点也不令人意外了。

时间安排事件

要求人们同时出现。这实际上更改了游戏时间，这样他们才能同时出现。时间安排对于玩家来说却是一项成本昂贵的计划活动。你将因此得到很低的游戏粘性，但那些参与游戏者却很可能去找其他东西来玩。MMO游戏中出现的万圣节boss就是一个时间安排事件。

游戏开发者可以安排一些事件，或者由玩家来安排这些事件。玩家安排事件具有更强的社交联系这一优势。人们晚上聚在一起玩桌游就是这类事件。其弊端在于安排会面是一个复杂的过程（因为大家都想设置超过6人可以出席的会面）。这通常需要领导能力或耐心，而这正是低粘性的玩家所缺乏的属性。

定期安排事件

如果你安排定期事件，人们就会养成在特定时间于特定地点集合的习惯。这会减少玩家的计划成本，这样他们只需在特定时间现身，而无需担心彼此时间冲突。周三晚上的公会游戏之夜就是定期安排事件的一个例子。

短期配对

如果配对时间够短（2分钟或30秒？），没有进入当前配对的玩家就可以缩短等待时间，仍然可参与下一轮配对。在线文字游戏就是这种典型，但这一方法也适用于其他游戏。

等待配对过程中的围观

如果你想让玩家观看游戏进程，可以延长配对窗口。例如《反恐精英》这类游戏就允许玩家在进入服务器和死亡期间围观他人的游戏。聊天通常是一个很好停工期活动，因为它可以培养玩家间的关系。

配对等待期间的bots

将玩家直接引到与bots过招的战斗中，以免他们在漫长的排队过程中无所事事。

让bots像人类一样行动是项棘手而有待检测的任务。不让玩家说话，设定非常狭窄的表达窗口也许能派上用场。但是当玩家意识到这一点时，他们会失去对游戏的信任，并怀疑是否所有对手都是bots。

无意识的相互依赖

创造需要大量玩家出现，齐心协作才能成功的活动。如果玩家不现身就会让团队失望，从而可增加玩家现身的社交压力。这可以通过采用明确的角色形式，或者通过限制资源来实现，这样玩家就无法独立完成大型目标。

工具：基于房间的游戏

对于较小的游戏来说，基于配对的游戏最终会产生无法克服的后勤问题。有一个很好的替代方法就是要基于房间的游戏。与拥有明确的开端和退场这种配对方式不同，基于空间的游戏创造了一个持久的游戏空间，玩家可以在游戏过程中独立加入（或者中途退场）。

这些房间拥有玩家参与的“插槽”或空间上限。当房间满员时，其他玩家就不可再加入。这就极大减少了配对的负荷。你所需要做的就只是找到一个有空位的房间，将其玩家塞进去。

但房间的一个劣势就在于，它们限制特定的游戏类型。群体起始时间会排除多数传统体育类游戏。拥有进程弧的游戏会让以不同类型起步的玩家得到不同的进展。所以你得发挥创意。

《Journey》这种游戏实际上是一个允许玩家中途加入和退出的房间型游戏。其最大空位是2，只要游戏中有两名同时在线玩家，你就可以拥有多人体验。多数MMO游戏就属于拥有极大房间的此类游戏。

中途加入和退出

游戏房间要提供这种高级后勤服务的一个原因在于，玩家可能会在任何时候加入或退出游戏。由于不太可能出现人人同时离开的情况（尤其是在具有平行互动优势的游戏中），一个人离开后马上就会有其他人跟上，你的房间中就会获得始终一致的平均人数。

纯配对型游戏通常比较罕见，因为许多热门游戏将单个服务器视为一个房间，配对元素很少记下中途加入和退出玩家的动态。

灵活的房间实例

创造和移动房间使之符合那个最大化的流通量。假如一个房间的最大上限是N，你就要创造一些新房间，以便房间数量等同于同时在线玩家/N。所以，假如10名玩家在线，你的默认房间大小是4，那就要确保玩家有3个房间可加入。

要解散一个房间，只要等到它自然清空，即玩家离开游戏或者因某些游戏内事件而将人们踢出房间之时。当房间清空后，就删除它。通过为房间分配优先权，你就可以最先填满拥有最高权重的房间，并关闭那些低权重的房间。这样几乎所有房间都是持续满员现象，只有那些无法进入房间的人被落单了。

我们在创造《Realm of the Mad God》中的世界碎片时使用了这一方法。该世界通常是满员的，即使同时在线玩家是处于波动状态。

将只有一名玩家的房间默认设置为单人玩法

基于房间的游戏存在“剩余者”问题。最大的房间很少能够在玩家群体间平均分配。如果房间大小是2，但却有3名玩家在线，那就有一名玩家要自己呆在一个房间里。

为了解决这一问题，游戏最好允许单人玩家的房间也具有可玩性，直到另一玩家进入房间。

像《暗黑之魂》这种零售游戏就考虑到了最低同时在线比率的情况，并且几乎多以单人游戏为主。同时在线配对是一个不会干预单人玩家冒险的无声平行交互系统。由于第二个玩家在合适的时间出现在合适的地点这一机率很低，因此游戏将其视为特殊事件。（游戏邦注：《暗黑之魂》以单人玩家游戏为主，玩家可选择使用皂石来进行同时在线多人体验。皂石发出成功配对的信号时，玩家就必须接受。当你混合单人模式和多人模式时，要记得游戏最初承诺的体验）

工具：异步技巧

邮寄式游戏

玩家完成一项互动之后，游戏就会向其发出信号，告知他要等待一段时间才能收到其他玩家回应。第二天，另一名玩家看到首个玩家的行动，就会编写自己的回复信息。这一来一往通常需耗费数天时间。

《Words with Friends》就是使用这一技巧的现代典型，但这种做法实际上至少可以追溯到数十年前（如果桌游也在此列的话）。这是一种适用于文本通信工具（例如即时聊天工具或电子邮件）的亲密玩法，非常适合好友之间的游戏。

但其弊端在于玩家耐心极其有限。单个回合可能不是那么令人满意，那么他们就要等上好几天才能得到一个回复，这也成为玩家留存率下降的主要问题。如果玩家分裂性过高，也仍然存在配对问题，但其漫长的等待窗口不会令玩家担心系统已经崩溃的问题（他们可能并不喜欢这种系统）。另一个劣势就是，在基于回合制的游戏中，玩家不回复就可能阻碍另一玩家的进程。

邮寄式游戏的高容量

有一个解决方法就是让玩家开始玩大量邮寄式游戏。假如一个回复时间是T天，其理想的平均等待时间是W天，那么同时进行的最佳游戏数量就是T/W（如果你想让游戏每小时都弹出一次，并花24小时回复，那么你就需要同时运行24款游戏）。

这种做法的一个好处就在于玩家的响应时间是半随机式的。这就会形成随机强化程序，并且可以产生长期的留存率。

但这个技巧的劣势就在于，它要求玩家一开始就要玩很多款游戏来减少等待窗口，而促使玩家如此行事的方法却非常棘手。自动游戏配对也许是个选择。

邀请

你可以利用活跃玩家来邀请新玩家加入游戏。这些玩家通常拥有强大的玩家人际关系，可以成为潜在的新玩家来源。

与好友配对

由于多人模式的异步性有赖于玩家过后重返游戏，其游戏设计通常有赖于游戏之外的社交联系。如果你想让玩家邀请好友或与好友配对（正如《FarmVille》的做法），互惠原则的缺位可能会令玩家原来的友谊处于危险境地。因此，玩家可能会迫于不给好友留下粗鲁印象的考虑而重返游戏。

那些令玩家原来的好友关系出丑的系统可能会有疏远玩家的风险。玩家会发现游戏机械式的交互性令人讨厌，并因此不再投入游戏中。在涉及人际关系时，真诚和意图就十分关键了。

访问

在城建游戏中，你可能创造了一个永久结构（例如城镇），其他玩家可以在你不在的时候访问它。

《Clash of Clans》使用了这一方法，允许人们攻击你的城镇。城镇是一个永久结构，它可以作为其他玩家征服的一个关卡。

访问通常可以归结为简单的资源交换，但在许多人同时访问时又会产生问题，其解决方法就是提升不同的实体。

Jason Rohrer的《The Castle Doctrine》使用了独特的设计方法，令访问成为一个受限的互动行为。这就可能让被访问地区产生永久的变化。

Ghosts

记录玩家的行为，之后在相似环境中回放这些行为。这在赛车游戏这类平行互动中甚为常见。它也适用于非零和互动，就好像《Cursor 10》或《Super Time Force》等游戏一样。Ghosts可以让玩家产生一种存在感，但却移除了配对的时间限制。

其缺点在于Ghosts通常很难与阻塞或零和型互动兼容。另一个劣势就是如果Ghosts数据及其环境不同步，之后的Ghosts数据也就无效了。这可以通过跳过阻碍操作或求助于管理异常情况的AI行为来减轻这一问题。

在更抽象的层次上，Ghosts只是玩家数据的记录，可以在任何触发器上重放。你可以在比赛刚开始时，即玩家出现在屏幕上，或者当玩家使用了召唤同盟的特殊护身符时就触发这一机制。

普遍做法

本文所包含内容仍然不够完整，我将为大家留下一些简短的建议：

*不要分散配对群体。为了保证高同时在线比率，要格外关注同时在线游戏配对失败的节点。

*尽量使用基于房间方法，而不是基于配对的玩法。

*持久性是促进异步互动的重要因素。

*好友关系会增加玩家留存率。尽量培养玩家之间的关系。

*尽早制作原型，在原型阶段就解决低玩家密度的问题。

总结

我仍然对新多人游戏很感兴趣。如果你想在现代游戏行业中留名，那就制作一款出色的多人游戏吧。一定要解决阻碍玩家一起游戏的后勤问题，创造一款能够在社区中快速传播的好游戏。（本文为游戏邦/gamerboom.com编译，拒绝任何不保留版权的转载，如需转载请联系：游戏邦）

What I’ve learned about designing multiplayer games so far

by Daniel Cook

The following blog post, unless otherwise noted, was written by a member of Gamasutra’s community.

The thoughts and opinions expressed are those of the writer and not Gamasutra or its parent company.

This post originally appeared on my blog Lostgarden.com. You can follow my vitriolic tweets about Ian Bogost’s latest facial hair misteps via @danctheduck on Twitter.

How do we get players to play together in a manner that fits their schedules? This is a key logistical challenge a designer faces when building multiplayer games.

The promise

We are seeing a blossoming of innovative multiplayer systems. In previous eras there were a handful of default models that games might use (matches, play-by-mail). Games today exist on a spectrum from fully concurrent to fully asynchronous and everything in between. A game like Dark Souls is predominantly single player, but includes interactions that are asynchronous (the leaving of messages and deaths) or fully concurrent (the joining of another player into your game for PvP or Coop.)

We are entering a golden era of multiplayer gameplay. Server costs are falling dramatically with the advent of cloud computing. Broadband internet and always on mobile connections are spreading rapidly across the globe. Business models like in game payments, crowd funding and service-based gaming are evolving to the point to financially support a broad range of long-lived communities. Designers are playing with these new capabilities to invent new forms of multiplayer gaming.

The challenge

However, multiplayer is both expensive to build and has a high risk of failure. Often teams invest 50 to 100% of their development budget into creating a multiplayer mode. It seems worth it. During development, the team plays every Friday and has so much fun they are convinced that multiplayer is what will turn their game into the next League of Legends or Counter Strike.

The real test occurs when the game faces a live population of players. Upon launch, multiplayer games often see only a few weeks of active multiplayer activity. Too many people show up. Then not enough. Players visit sporadically and the player experience is deemed unreliable. The active matches trickle down to nothing. The traditional matchmaking lobbies (a design from the 1990’s) are left empty and will never be full ever again. The multiplayer portion of the game dies a sad sputtering death.

I see this as a challenge of logistics. There were players who wanted to play. However the way that the game put those players together results in weak community that was unable to self sustain.

Are there atomic elements of multiplayer logistics that lets us approach the topic of inventing new systems in a more rigorous fashion? Simply copying multiplayer patterns from previous eras works poorly. To invent new multiplayer modes, we must have conceptual tools that let us clearly and concisely manipulate topics like logistics, concurrency and interaction schedules.

Concepts when talking about multiplayer

Here are some concepts I think about when designing a multiplayer game.

Interactions

You can break up any multiplayer system into a series of interactions. An interaction is anytime players interact with one another via a game system (be it chat, hitting one another, etc.) These are the multiplayer verbs of your game. Usually a game has a set of single player verbs (move, quit, etc) and another set of multiplayer interactions mixed in. Interactions have a wide range of multiplayer properties such as frequency, scope, mode, etc.

If you map an interaction onto time, it looks something like this

The player starts the interaction

They end the interaction

They wait for a response.

If no response is forthcoming, they leave.

Interactions aren’t a new thing. The structure is identical to that found in atomic game loops. However, instead of a single loop you have something closer to a figure 8 with at least two participants. These concepts go back to communication theory that Chris Crawford adapted to games design theory in the 1980’s. This is fundamental stuff that all professional game designers should know.

Initial loop:

Model A: Player formulates an action and a target player or group.

Action A: Player performs the action.

Rules: The results of the action are mediated by the game logic.

Response A: Player A sees the immediate results as generated by the game.

Response B: Player B sees the immediate results as generated by the game. Note that what Player B sees is likely different than what occurs for player A. This naturally leads to divergent mental models and enables gameplay concepts such as hidden information or Yomi.

Reciprocating loop

Model, Action, Rules, Response B: The target players tries to understand what happened and formulates a response.

From here the loop ping pongs back and forth between participants.

Frequency of interaction

What is the frequency of interaction necessary to yield the impression of concurrency? You may find that you need to interact once every 5 minutes in a strategic game like Civilization while you need to interact every 200 ms to create the same impression in a twitch-based action game like Counter-Strike. See the article “Loops and Arcs” for a more detailed explanation.

In general, the higher the frequency of interactions, the more information being communicated between players. This can increase the pace of relationship formation.

As with many interaction variables, there are distinct phase changes in the players perception as the frequency hits a threshold. Simply by changing the spacing between interactions, we get radically different forms of play (and associated logistical challenges):

Real time: Players perceive interactions as ‘real-time’ when the frequency reaches the point where: A player starts and ends an interaction and then sees a response before they move onto other tasks; interactions overlap. Chat, for example, can feel real-time despite there often being more than a minute between responses. Real-time systems have less need for persistence but are often more expensive to run and build.

Asynchronous interactions: The frequency at which a player can start an interaction and end the interaction and then quit the game without seeing a response is seen as asynchronous. Generally you build in some sort of persistence so that a player that logs in later can see the results of the interaction and formulate a response.

Types of interaction

There are a variety of interaction types. Think of these as ‘how’ players interact. For a much more in depth description of all the various multiplayer interactions, see Raph Koster’s seminal talk on social game mechanics.

Spacial avatar interaction: Two or more avatars interact with one another. Shooting players in Quake is the classic example. Following a player in Journey is another.

Spacial environment interaction: Players also interact through the intermediate environment. In Minecraft, players build castles that other players then explore. For a higher frequency example, in Bomberman, players place bombs that open up passages or do damage to others.

Decoration and Display: Players signal status, affiliations and history via what they wear or how they decorate their weapons, pets and houses.

Economic: Players give, trade or pay for various resources to transform or transfer to another player. This can be a typical sale of a sword to another player for gold. Or it can paying mana for a buff that boost the health of a nearby player. See Joris Dormans work on internal economies for more on this topic.

Text: The most common method of introducing language into an online game is through text. It tends to be low cost and there’s a rich set of tools (spam filters, stylistic conventions) for dealing with common issues. It tends to work best with a keyboard.

Voice: Voice offers additional nuance including emotions, age, gender and more. It has limits for group size, bandwidth and is notoriously weak when it comes to filtering.
Body language: In local spaces like on a couch or around a table, we pick up on high bandwidth communication such as facial expression, posture, body height and physical presence. When a tall pretty boy looks you in the eye and asks that you trade your rare treasure with him, you may be getting signals that go far beyond what is found in other types of interaction. This creates rich emergent multiplayer gameplay. However, it is also hard to mediate and incorporate explicitly into the game systems.

Size of community

There are also massive phase changes that occur as you increase the number of participants in a community.

1 player: Mastery, progression, exploration, narrative are available as design tools.

2 players: Communication, relationships, status, gifting, trade, cooperation and competition become available.

3-4 players: Alliances, politics, gossip, othering/stereotyping become available.

Small group (5+): Group vs group interactions, Official leadership, role specialization, official punishment

Medium group (12+): Factions, barter economies, and banishment

Large groups (40+): Hierarchy (leaders and sub-leaders), Currency-based economies, role enforcement. Adhoc systems of government, public codification of social norms.

Very Large groups (200+): Merchant classes, market-based pricing, codified systems of government, underclasses, celebrity, propaganda. This is the point at which a players is guaranteed not to know everyone and official systems are required to make social norms work. (see Dunbar’s Number)

Massive groups (1,000+): Polling, city-scale production efforts. There are very few dynamics that happen at this scale that isn’t also explore with 200+ or even 40+ groups.

I’m defining these groups in the context of player interactions.  The actual game population may be much larger.  For example with trade in Realm of the Mad God, we saw simple trade interactions happen with as little as two people even in populations that are in the thousands.  Two good rule of thumb when considering group size is to ask:

Who does this action impact or target?  This gives a rough estimate of the group size your system needs to support.

Is a larger group size necessary for this behavior to emerge?  If not, you can usually get by by targeting your design at multiple instances of a smaller group size.

The actual transition points fluctuate around these numbers based off contextual factors. For example, the transition to the dynamics of a Very Large Group can occur as soon as 60 or 70 people if there are weak communication channels that stress a player’s ability to maintain relationships.

Also, large groups are inevitably composed of smaller groups. So as systems are added, the dynamics of lower number groups are still present.

The dangers of large group sizes:

It can be tempting to make epic multiplayer games with thousands of interacting players that could theoretically all fit in the same room. However, the technology and design costs are high and the benefits weak. Past 150-250 players, your game is in territory beyond Dunbar’s theorized biological limit on maintaining meaningful relationships.  All those extra people end up just being treated as number or abstractions by your players. A simple sim or polling system can often capture the major benefits of the next highest group size.

Realm of the Mad God was completely playable as an MMO with action sequences of 40-80 players and trade / hub interactions of ~150.  Players did not miss the 1000s of players.

This reality raises serious questions about the need for designs that emphasize ‘massively multiplayer’ experiences. Just because a concept sounds exciting (“a million people building a new society!”) doesn’t mean it is a smart design. Human social capacities are limited and we can (and have!) over-engineer multiplayer systems.

Scope of interaction

How many people does a single interaction impact? A player can interact with a single individual or they can interact with one of the group sizes listed above.

Targeting a player interaction at small groups: With smaller group sizes you get communication similar to a conversation. There is a clearly defined interaction loop that can stabilize on a set of shared vocabulary and social norms quickly.

Targeting a player interaction at larger groups: With larger group sizes you see more broadcast scenarios and interactions are broader, less tailored to individuals. When interacting with large groups, it is common for the massive response to flood the recipient with too much information. Extreme reactions are also more common as people talk over and past one another.

Degree of interaction

Parallel: Players can behave independently from one another. A ghost racing car rarely impacts another player. Often the primary benefit here is a sense of presence though it can also tie into lower frequency zero sum interactions like a leaderboard.

Zero Sum: The action of one player blocks or reduces the interaction of another player. In Habbo hotel, movement is a zero sum interaction since the placement of one character blocks another character from occupying the same spot. This was famously used as a griefing tactic to box in players.

Non-Zero Sum: The action of one player benefits another player. In Realm of the Mad God, shooting an enemy makes that enemy easier to kill for other players. Killing an enemy gives XP to everyone on the screen.

Matchmaking

Matchmaking is the computer mediated act of introducing players to one another so they might interact.

This is a very broad definition of matchmaking, but is useful in the context of the wide range of multiplayer systems available. For example, a traditional console title might match players together by requiring players in a shared lobby to manually join a specific game. In Realm of the Mad God, players notice groups of players on a shared map and teleport to them. Both are forms of matchmaking, but they appear quite different in the player’s mind.

You can treat matchmaking abstractly as another interaction with a wait time.

Matchmaking window

The time you have to introduce a player looking for a multiplayer experience to another player. If the window is too long (and the player is not entertained during the window), they will leave.

Matchmaking failure

When a player comes online and there is not another player immediately online, the players will quickly become bored and leave. There is often an implicit promise of a fun multiplayer experience and if you don’t deliver that in seconds, your game is judged as a failure.

What can be frustrating to the developer is that another player pops in a minute later and experiences the same exact thing. If one players sticks around long enough, another player will show up.

Calculating daily failure threshold: If the matchmaking window is W in minutes, then failure will occur when the daily active population is less than Minutes In a Day / W. So for example if people are only willing to wait half a minute, you’d need a daily active population of 1440 / 0.5 or 2880 players. Actual results will be lumpy because we are dealing with a statistical process and player populations peak around specific times of day.

This may seem quite reasonable, but if you are matchmaking primarily with small groups of friends, players may feel like no one they know is ever on.

Fragmentation

When the player population is segmented by social groups, game modes, players skill levels, time playing and other factors, it becomes fragmented. This reduces the actual concurrent player numbers available to the matchmaking system and increases the chance of a matchmaking failure.

Example of fragmentation: Suppose a game has 3 multiplayer modes and matches players into 10 skill categories. If the daily failure threshold is 2880 (from the previous example), then in the worst case scenario, you’d need 3x10x2880 or 86,400 concurrent players for everyone to get their first choice.

Fragmentation creeps into a design. Someone wants to add another event or another game mode. The code is free, so why not? Surely the players will self sort. They do a little, but mostly they wonder why the matchmaking experience is so painful and then leave your game in frustration. Avoid fragmentation creep and put players together in big easily matched buckets when possible.

Concurrency ratio

Any game has a number of active accounts and a number of players that are online at once. Players cannot be playing constantly and are often offline For example, an MMO might have 100 active subscribers, but only 10 of those are on at any one time. This would result in a concurrency ratio of 10:1. Some typical concurrency ratios:

MMO: 10:1

Online Console Service (like Xbox Live): 25:1

Individual Console game: 150:1

Flash game: 250:1

Couch multiplayer: 1000:1 (This is slightly facetious. Couch multiplayer games are often played a few times a year)

The Active User Trap: One common mistake is that developers assume that high active player numbers will result in robust multiplayer communities. However you really need to look at actual concurrent users since many game types have extreme concurrency ratios. A game may have 1000 players but when each of those logins last 5 minutes and are spread over a week, you’ll average 0.5 concurrent players. If your matchmaking system doesn’t deal well with these sporadic, tiny populations, the game dies.

Relationship strength

Not all player interactions are equal due to unique relationships between players. Players build complex social models of other players both in game and out of game. Strangers are understood through simple, stereotype-based models. Close friends are understood through complex individual models built up over thousand or millions of minute reciprocation sequences.

Building mental models of another human is a biologically expensive operation. We seem to be able to keep 5 to 9 detailed models active at any one time though we can store many more at various levels of detail. Friendship is rare, complicated and built over long periods of time.

There are numerous benefits and trade offs that come from gaming with strangers or friends and friend-based play is often highly desirable. Games can help create friends by promoted repeated positive interactions. The higher the frequency, the quicker the relationship evolves. Relationship strength is a spectrum, but there are two commonly drawn categories

Multiplayer with Strangers

Multiplayer with Friends

Multiplayer with Strangers

Let’s tackle multiplayer between strangers online first.

Pros:

Anyone playing the game can be matched with anyone else with little regard for existing social bonds.  This model becomes immensely attractive when there is a small initial player base. Often this means if 10 people are online, 10 people can be playing together.

Strangers, particularly young males, historically tend to compete with one another. This means that player vs player games that emphasize open conflict are an easy means of generate fun for some stranger populations.

Cons:

Strangers have weak bonds and will not naturally engage in prosocial activities like collaboration.

Skill differentials matter since players tend to compete. This forces developers to focus on segregating experts from newbies and fragments the population.

Not all player populations thrive on overtly competitive gameplay. Some players prefer to collaborate. Others compete quietly for status by manipulating social relationships. These are difficult in stranger scenarios.

Multiplayer with Friends

Pros

Players are much more likely to schedule time together to play.

Cooperative and communication heavy activities are considered fun.

Mentoring between divergent skill levels is more likely to occur.

Competitive play is still valid.

Cons

There’s often little overlap between existing social groups and interest in a specific game.

There’s often little overlap between existing social groups and share scheduled.

Friend groups are small. Engaged players typically have 5-9 close relationships. Casual acquaintances may be higher in number, but in practice may act more like strangers. If you have 10 friends and the concurrency ratio for a service is 25:1, you will essentially never stumble upon them online.

Tools for dealing with multiplayer logistics

So far I’ve just talked about the concepts behind multiplayer. Now we’ll dig into some common patterns that make use of these. There are three broad architectures:

Match-based games

Room-based games

Asynchronous games

Tools: Match-based games

Due to the long history of event-based matches in sports and board games multiplayer computer games often are organized into matches that start at a specific time and stop at a specific time or win condition.

Matches are the default logistics model used for many console and PC-style online games. They are immensely problematic. The matchmaking interaction has a very narrow window during which it requires a full set of players to show up in order to enter the game successfully. If you don’t get in, you need to wait till the next match starts. If this time is longer than the wait window, you’ll quit. Considering concurrency ratios, fragmentation and the burden of a tiny matchmaking window, it is not surprising that only the most popular match-based online titles survive.

Scheduled Events

Ask people to show up at the same time. This essentially shifts play times so that they are on at the same time. Scheduling is an expensive planning activity on the part of the player. You’ll get a low overall engagement rate but those who do participate are likely to find other to play with. A special Halloween boss encounter in a MMO is an example of a scheduled event.

Events can be scheduled by the game developers or they can be scheduled by the players. Player scheduled events have the benefit of stronger social ties in play. Folks that get together for a board game night are such an event. The downside is that arranging meeting is a convoluted process (as anyone that tries to set up meetings with more than 6 people can attest). It often requires leadership or persistence, attributes that are often in low supply for lightly engaged players.

Regularly scheduled events

If you can make the event regular, people will get in the habit of being at a particular place at a particular time. This reduces the cost of planning for the player and they can just reliably show up at a specific time instead of worrying about conflicts. A standard Wednesday game night for a guild is an example of a regularly scheduled event.

Short matches

If matches are short enough (2 minutes? 30 seconds?) players that don’t get into the current match wait less time than the matchmaking window and thus are still around when the next match starts. Online word games do this, but it could be readily applied to other titles.

Spectating on matches while waiting

If you can keep players entertained by letting them watch the game in progress, you can lengthen the matchmaking window. Games like Counter Strike do this upon entrance into a server and upon death.  Chatting is often tossed into this mix since it is a nice downtime activity that can build relationships.

Bots during matchmaking to fill waits

Instead of putting players in a queue where nothing happens, put them directly into a match with bots as the opponents.

Getting bots that act like humans is often a tricky Turing test to pass. Not letting players talk and having a very narrow window of expression helps. When players learn this is happening they will start to distrust the game and question if all opponents are bots.

Mechanical Interdependencies

Create activities that require multiple people to show up in order to achieve success. Not showing up lets down the group and thus increases the social pressure to show up. This can take the form of explicit roles or by limiting resources so that players can’t accomplish large goals independently.

Tool: Room-based games

Ultimately match based games result in often insurmountable logistical issues for smaller games. A favorite alternative is room based games. Unlike a match which has a distinct start and exit, room-based games create a persistent playspace that players may independently join the game in progress (or leave the game in progress)

Rooms have a maximum number of ‘slots’ or spaces for players to join them. Once the room is full, no more players may join. This dramatically reduces the load on matchmaking. All you need to do is find a room with an empty slot available and dump players into it.

The downsides to rooms is that they eliminate certain game types. Group starting times are obviously out which eliminates most traditional sports. Games with progression arcs result in players that start at different types having differing levels of progress. You need to get creative.

A game like Journey is essentially a room based game with join and leave in progress. The max slots was 2 and as long as there were two concurrent players you could have a multiplayer experience. Most MMO’s are room-based games with very large rooms.

Join In Progress, Leave in Progress

One reason why rooms offer such improved logistics over strict matches is that players may join or leave at any time.  Since it is highly unlikely that everyone will leave at once, especially in games with a predominance of parallel interactions, shortly after one person leave another person will join and you’ll get a consistent average population in the room.

Pure match-based games are often quite rare because many popular games treat the individual server as a room and the match-based elements are merely scoring atop a dynamic population of players joining and leaving in progress.

Elastic Room Instances

Create and remove rooms to fit that maximum currency. Given a room of maximum size N, you create new rooms so that the number of rooms equals Concurrent Player / N. So if 10 players are online and your default room size is 4, you’ll make sure there are 3 rooms to join.

To collapse a room, just wait until it naturally empties out as players leave the game or kick people out due to some in-game event intended to free up the instance. Once the room is empty, delete it. By giving rooms priority, you can fill the highest priority rooms first and kill off the low priority rooms. The result is that almost all rooms are constantly full and only the remainder are left alone.

We used this when creating world shards in Realm of the Mad God. The world generally felt full even when the concurrent population fluctuated dramatically.

Default to single player gameplay for rooms with one player

Room-based games have the ‘remainder’ issue. A given maximum room size rarely divides evenly into the concurrent population. If the room size is 2 and there are 3 players online, there will be 1 player placed in a new room by themselves.

To deal with this scenario, it helps to have a game that is playable as a single player game until the next player joins the room.

A retail game like Dark Souls assume very low concurrency and plays almost entirely as a single player game (with light async ghost interactions) The concurrent matchmaking is a silent parallel interaction that happens without interrupting the single player adventuring. Since having a second player in the right place at the right time is uncommon, the game instead treats it as a special occurrence. (Note that since Dark Souls promises a single player game, they make the concurrent multiplayer experience opt-in through the use of soapstones. The soapstones signal that a successful match has occurred and the player must accept it. Respect your initial promise when you mix single player and multiplayer interactions.)

Tool: Asynchronous techniques

Play-by-mail

A player complete an interaction and then the game signals to them that they have a very long period of time before the other player responds. The next day or so, the other player sees the first player’s action and composes their response. This can take place over days.

Words with Friends is a modern example of this technique, but the practice goes back decades if not centuries (if you include play-by-mail board games). It is an intimate method of play that works well with text communication much like instant messages or email. Play-by-mail is very amenable to play between friends.

A downside is that players are deeply impatient. A single turn may not be all that satisfying and then having to wait multiple days for a response has a major drop off in retention. There are still matchmaking issues if fragmentation is too high but the explicitly long wait window ensures players don’t get too worried that the system is broken (they may just not like the system). The other downside is that in turn-based games, the non-response of one player may block another player.

High Capacity Play-by-mail

One solution is for a player to start a large number of play-by-mail games. Given a response time of T days and a desired average wait time of W days, then the optimal number of games going at once is T/W. (So if you want a game popping in every hour and it takes 24 hours to response, then you need 24 games going.)

One added benefit of all this is that player response times are semi-random. This acts as a random reinforcement schedule and can result in very long term retention.

The downside to the technique is that it requires players to start up a lot of games in order to reduce the wait window and motivating players to do so is tricky. Automated game matching may be an answer.

Inviting

You can leverage active players to invite new players to the game. These players often have strong relationships with the player and can potentially act as a source of new players into the game.

Match with friends

Since async forms of multiplayer rely heavily on players to come back later, their game designs often relies on social connections outside the game as a form of additional pressure. If you can get people to invite or match with friends (as in Farmville) a lack of reciprocation in interpreted as putting their existing relationships at risk. The threat of being rude or seeming like you don’t care to someone you like is often enough of an incentive to encourage returning to the game.

Systems that play off existing relationships run the risk of alienating players. Players not invested in the game tend to find mechanical interactions annoying. Authenticity and intentions matter when it comes to human relationships.

Visiting

In building games, you may create a persistent structure such as a town that other players can then visit independently of your presence.

Clash of Clans uses this when players attack your town. The town is a persistent structure that then acts as a level for the other player to conquer.

Visiting usually boils down to a simple resource exchange despite the trapping of being something more meaningful. The issue comes from questions of what happens when multiple people visit at once and the solution is to spin up different instances.

Jason Rohrer’s The Castle Doctrine uses the unique design of making visiting a blocking interaction. This opens the possibility for permanent changes being made to the visited location. One can imagine more complex versions of musical chairs as the foundation for some innovative designs.

Ghosts

Record players behaviors and then play them back alongside the player in a similar environment. This works particularly well with parallel interactions like you see in racing games. It can also work with the rare non-zero sum interactions like you see in multiple time track games like Cursor 10 or Super Time Force. Ghosts gives a sense of presence but removes the matchmaking time constraints.

The downside is that ghosts usually works poorly with blocking or zero-sum interactions. The other downside is that if the ghost data and the environment get out of sync, then the ghost data becomes invalid. These can be alleviated slightly by either skipping blocked actions or falling back on AI behaviors that manage exceptions

On a more abstract level, ghosts are just tracks of player data that can be replayed on any sort of trigger. They can be triggered at the start of a race, when the player comes onscreen or when the player uses the special amulet of Ally Summoning.

General practices

This essay has covered a lot of ground (and is still incomplete!), but I’ll leave you with a few quick recommendations.

Don’t fragment your matchmaking population. Be very wary of the point at which your concurrent game’s matchmaking fails due to high concurrency ratios.

Use room-base methods where possible, not match-based play.

Persistence is your friend since it enables asynchronous interactions.

Relationships are your friend since they increase retention. Try to build them where possible.

Prototype early and deal with low populations density issues during the prototyping phase.

Conclusion

I remain quite excited about new multiplayer games. When I look at the theoretical advances being made with game grammar via Joris Dormans internal economies and some of the multiplayer concepts in this essay, the unexplored space for new forms of game seems vast. If you want to make your mark on our modern world, make a great multiplayer game. Solve the logistical issues that prevent people from playing together and build a game that spread quickly and easily throughout communities.（source：gamasutra）