动机名称 目的 内在感受
权利 渴望影响他人（包括领导权、统治权） 效力
好奇心 渴望了解 好奇
独立性 渴望独立自主 自由
地位 获得社会地位（包括引起关注） 自我重要感
社会联系 获得同伴相陪（共同玩耍） 乐趣
复仇 报仇（包括对抗取胜） 自我证明
荣誉 渴望遵守传统道德准则 忠诚
理想主义 渴望改变社会（包括大公无私） 怜悯
锻炼身体 渴望锻炼肌肉 活力
浪漫 性需求（包括求偶） 欲望
家人 渴望养育孩子 爱
整理 组织需求（包括宗教仪式） 稳定
饮食 食物需求 饱腹感（免受饥饿）
接受 获得认同 自信心
宁静 避免紧张、恐慌 安全、放松
存储 收集、节约需求 拥有权
（此表根据《Multifaceted Nature of Intrinsic Motivation: The Theory of 16 Basic Desires》制定）
然而，《Farmvile》核心机制利用的动机在本质上并不比其它动机“拙劣”，比如收集道具（Reiss理论上的“存储”），以及希望回报赠送Blue Doohickey的玩家一个Green Whatsit（即互利主义，也就是Reiss理论上的“理想”动机），但由于缺少其它刺激性能，这款游戏就难以长期维持这些动机。许多出色的MMO游戏都很擅长充分利用这两种以及其它额外动机。
Jesse Schell在《The Art of Game Design》一书中将它们当作“透镜”，该书包括100个发人深省的透镜，设计师可以借此观察和改进游戏。可能会有人设想开发出对应Reiss基本动机的透镜（游戏邦注：比如，独立透镜：我的游戏会让玩家获得独立感吗？玩家会获得控制感吗？他们能够自由选择有意义的选项吗？）事实上，Schell的列表中已囊括不少与上述动机相关的透镜（比如竞争透镜、合作透镜、需要透镜、控制透镜、社区透镜）。
本文内容是我们同Andrzej Marczewski以及（直接与）Richard Bartele进行的讨论所得结果，这两者都曾研究玩家类型学这一问题。除此之外，业内还出现了Nicole Lazzaro提出的趣味类型理论，“4个关键2种趣味”。
而对于制作手机游戏的开发者来说，情况也很明显，制作人人都想玩的游戏极为困难，而制作人人都愿意付费的游戏更是难上加难。现在每天都有超过13万款的游戏向苹果App Store提交审核，《CSR Racing》这类游戏首个月就可以创收1200万美元，但更多手机游戏并没有这么幸运，据称平均来看，每款手机游戏收益不足4000美元。虽然理论上看，仅用数千美元开发一款手机游戏仍是可行做法（游戏邦注：但即使没有实际的财政支出，开发者在无报酬的情况下开发游戏仍然需要付出一定的机会成本），但多数来自专业工作室的游戏仍需要准备5万至100万美元的开发预算。
手机游戏玩法不但要适合其运行的设备，还需要依据设备用户特点而设计。在此开发者通常会忽视一些情况。需注意的是，不要因为iPhone 4/iPad 2可传递堪比主机游戏的高质量图像，就误以为你真的可以照搬其他平台的做法。试想有多少玩家会在iPhone上投入20多个小时持续玩每个关卡至少需要20分钟才能玩的游戏？
篇目1，Intro to User Analytics
by Anders Drachen
The science of game analytics has gained a tremendous amount of attention in recent years. Introducing analytics into the game development cycle was driven by a need for better knowledge about the players, which benefits many divisions of a game company, including business, design, etc. Game analytics is, therefore, becoming an increasingly important area of business intelligence for the industry. Quantitative data obtained via telemetry, market reports, QA systems, benchmark tests, and numerous other sources all feed into business intelligence management, informing decision-making.
Two of the most important questions when integrating analytics into the development process are what to track, and how to analyze the data. The process of choosing what to collect is called feature selection. Feature selection is a challenge, perhaps especially when it comes to user behavior. There is no single right answer or standard model we can apply to decide what behaviors to track; there are instead several strategies that vary in goals: e.g., improve the user experience or increase monetization. In this article, we will attempt to outline some of the fundamental concerns in user-oriented game analytics, with feature selection as an overall theme. First, we’ll walk through the types of trackable user data, and then introduce the feature selection process, where you select how and what to measure. Importantly, this article is not focused on F2P and online games — analytics is useful for all games.
Data for Analytics
The three main sources of data for game analytics are:
Performance data: These are related to the performance of the technical- and software-based infrastructure behind a game, notably relevant for online or persistent games. Common performance metrics include the frame rate at which a game executes on a client hardware platform, or in the case of a game server, its stability.
Process data: These are related to the actual process of developing games. Game development is to a smaller or greater degree a creative process, but still requires monitoring, e.g., via task-size estimation and the use of burndown charts.
User data: By far the most common source of data, these are derived from the users who play our games. We view users either as customers (sources of revenue) or players, who behave in a particular way when interacting with games. The first perspective is used when calculating metrics related to revenue — average revenue per user (ARPU), daily active users (DAU) — or when performing analyses related to revenue (churn analysis, customer support performance analysis, or microtransaction analysis).
The second perspective is used for investigating how people interact with the actual game system and the components of it and with other players, by focusing on in-game behavior (average playtime, damage dealt per session, and so forth). This is the type of data we will focus on here. These three categories do not cover general business data, e.g., company value, company revenue, etc. We do not consider such data the specific domain of game analytics, but rather as falling within the general domain of business analytics.
Figure 1: Hierarchical diagram of sources of data for game analytics emphasizing user metrics.
Developing Metrics From User Data
Many people have proposed different methods of classifying user data over the past few years. From a top-down perspective, a development-oriented classification system is useful, as it serves to funnel user metrics in the direction of three different classes of stakeholders — for example, as follows.
Customer metrics: Covers all aspects of the user as a customer — for example, cost of customer acquisition and retention. These types of metrics are notably interesting to professionals working with marketing and management of games and game development.
Community metrics: Covers the movements of the user community at all levels of resolution, such as forum activity. These types of metrics are useful to community managers.
Gameplay metrics: Any variable related to the actual behavior of the user as a player inside the game (object interaction, object trade, and navigation in the environment, for example).
Gameplay metrics are the most important for evaluating game design and user experience, but are furthest from the traditional perspective of the revenue chain in game development, and hence are generally underprioritized. These metrics are useful to professionals working with design, user research, quality assurance, or any other position where the actual behavior of the users is of interest.
Customer metrics: As a customer, users can download and install a game, purchase any number of virtual items from in-game or out-of-game stores and shops, spending real or virtual currency,over shorter or longer timespans. At the same time, customers interact with customer service, submitting bug reports, requests for help, complaints, and so on. Users can also interact with forums, official or not, or other social-interaction platforms, from which information about these users, their play behavior, and their satisfaction with the game can be mined and analyzed. We can also collect information on customers’ countries, IP addresses, and sometimes even age, gender, and email addresses. Combining this kind of demographic information with behavioral data can provide powerful insights into a game’s customer base.
Community metrics: Users interact with each other if they have the opportunity. This interaction can be related to gameplay (combat or collaboration through game mechanics) or social (in- game chat). Player-player interaction can occur in-game or out-of-game, or some combination thereof — for example, sending messages bragging about a new piece of equipment using a post-to-Facebook function. In-game, users can interact with each other via chat functions, out-of-game via live conversation (TeamSpeak or Skype), or via game forums.
These kinds of interactions between players form an important source of information, applicable in an array of contexts. For example, a social-network analysis of the user community in a F2P game can reveal players with strong social networks — who are the players likely to help retain a big number of other players in the game by creating a good social environment (think guild leaders in MMORPGs). Likewise, mining chat logs and forum posts can provide information about problems in a game’s design. For example, data-mining datasets derived from chat logs in an online game can reveal bugs or other problems. Monitoring and analyzing player-player interaction is important in all situations where there are multiple players, but especially in games that attempt to create and support a persistent player community, and which have adopted an online business model, which includes many social online games and F2P games. These examples are just the tip of a very deep iceberg, and the collection, analysis, and reporting on game metrics derived from player-player interaction is a topic that could easily take up several volumes.
Gameplay metrics: This subcategory of the user metrics is perhaps the most widely logged and utilized type of game telemetry currently in use. Gameplay metrics are measures of player behavior: navigation, item and ability use, jumping, trading, running, and whatever else players actually do inside the virtual environment of a game (whether 2D or 3D). Four types of information can be logged whenever a player does something or something happens to a player in a game: What is happening? Where is it happening? At what time is it happening? And: Who is involved?
Gameplay metrics are particularly useful for informing game design. They provide the opportunity to address key questions, including whether any game world areas are over- or underused, if players utilize game features as intended, and whether there are any barriers hindering player progression. These kind of game metrics can be recorded during all phases of game development,as well as following launch.
Players can generate thousands of behavioral measures over the course of a single game session — every time a player inputs something to the game system, it has to react and respond.
Accurate measures of player activity can include dozens of actions being measured per second. Consider, for example, players in a typical fantasy MMORPG like World of Warcraft: Measuring user behavior could involve logging the position of the player’s character, its current health, mana, stamina, the time of any buffs affecting it, the active action (running, swinging an axe), the mode (in combat, trading, traveling), the attitude of any NPC enemies toward the player, the player character name, race, level, equipment, currency, and so on — all these bits of information simply flow from the installed game client to the collection servers.
From a practical perspective, you may want to further subdivide gameplay metrics into the following three categories (in order to make your metrics more searchable, for instance):
In-game: Covers all in-game actions and behaviors of players, including navigation, economic behavior, as well as interaction with game assets such as objects and entities. This category will in most cases form the bulk of collected user telemetry.
Interface: Includes all interactions the player performs with the game interface and menus. This includes setting game variables, such as mouse sensitivity and monitor brightness.
System: System metrics cover the actions game engines and their subsystems (AI system, automated events, MOB/NPC actions, and so on) initiate to respond to player actions. For example, a MOB attacking a player character if it moves within aggro range, or progressing the player to the next level upon satisfaction of a predefined set of conditions.
To sum up, the array of potential measures from the users of a game (or game service) can be staggering, and generally we should aim for logging and analyzing the most essential information. This selection process imposes a bias, but is often necessary to avoid data overload and to ensure a functional workflow in analytics.
Bias is introduced in the dataset both by the selection of the features to be monitored and also by the measuring strategies adopted, and that happens to a large degree when analysts work in a vacuum. If those responsible for analytics cannot communicate with all relevant stakeholders, critical information will invariably end up missing and the full value of analytics will not be realized.
Analytics groups are placed differently across companies due to analytics arriving to the industry from different directions, notably user research, marketing, and monetization, and this can lead to a situation where the analytics team only services or prioritizes their parent department. Having a strong lateral integration — making sure that the analytics team communicates with all the teams, for example — helps to avoid this issue. This also helps alleviate the common problem that the analytics teams, without having sufficient access to design teams, are forced to self-select features to track and analyze, without having the proper grounding in the design of the game and its monetization model.
Even for a small developer with a part-time analyst this can be a problem. Another typical problem is that the decision about which behaviors to track is made without involving the analytics team. This can lead to a lot of extra time spent later on trying to work with data that are not exactly what is needed, or needing to record additional datasets. Good communication between teams also helps alleviate friction between analytics and design.
Importantly, analytics should be integrated from the onset of a production — all the way back in the early design phases. Early on it should be planned what kinds of behavior that should be tracked and with what types of frequencies. This allows for optimal planning of how to ensure value from analytics to design, monetization, marketing, etc. Analytics should never be slapped on sometime after the beta. In this way analytics is similar to other tools like user research, in that it ideally is embedded throughout the development processes, and after launch.
Knowing that there is an array of things we can measure about user behavior, how do we then select among them? And do we really have to make choices here? Sadly, yes. In real life, we rarely have the resources to track and analyze all possible user behaviors, which means we have to develop an approach to analytics that considers cost-benefit relationships between the resources required for tracking, storing, and analyzing user telemetry/metrics on one hand, and the value of the insights obtained on the other. It is also important to be aware that the analyses needed during different stages of production and post-launch varies. For example, during the latter phases of development, tuning design is vital, but many metrics related to monetization cannot be calculated because purchases have not been made by the target audience yet.
We will discuss this in more detail below, but in short, by following this line of reasoning, the minimum set of user attributes that should be tracked, stored, and analyzed should include considerations as to the following:
1) General attributes: The attributes that are shared for users (as customers and players) across all games. These form the core metrics that can always be collected, for any computer game– for example, the time at which a user starts or stops playing, a user ID, user IP, entry point, and so on. These form the core of any game analytics dataset.
2) Core mechanics/design attributes: The essential attributes related to the core of the gameplay and mechanics of the game. (For example, attributes related to time spent playing, virtual
currency spent, number of opponents killed, and so on.) Defining the core design attributes should be based directly on the key gameplay mechanics of the game, and should provide information that lets designers make inferences about the user experience (whether players are progressing as planned, if flow is sustained, death ratios, level completions, point scores).
3) Core business attributes: The essential attributes related to the core of the business model of the company, for example, logging every time a user purchases a virtual item (and what that item is), establishes a friend connection in-game, or recommends the game to a Facebook friend — or any other attributes related to revenue, retention, virality, and churn. For a mobile game, geolocation data can be very interesting to assist target marketing. In a traditional retail situation, none of these are of interest, of course.
4) Stakeholder requirements: In addition, there can be an assortment of stakeholder requirements that need to be considered. For example, management or marketing may place a high value on knowing the number of Daily Active Users (DAU). Such requirements may or may not align with the categories mentioned above.
5) QA and user research: Finally, if there is any interest in using telemetry data for user research/user testing and quality assurance (recording crashes and crash causes, hardware configuration of client systems, and notable game settings), it may be necessary to augment to attributes on the list of features accordingly.
When building the initial attribute set and planning the metrics that can be derived from them, you need to make sure that the selection process is as well informed as possible, and includes all the involved stakeholders. This minimizes the need to go back to the code and embed additional hooks at a later time — which is a waste that can be eliminated with careful planning.
That being said, as the game evolves during production as well as following launch (whether a persistent game or through DLCs/patches), it will typically be necessary to some degree to embed new hooks in the code in order to track new attributes and thus sustain an evolving analytics practice. Sampling is another key consideration. It may not be necessary to track every time someone fires a gun, but only 1 percent of these. Sampling is a big issue in its own right, and we will therefore not delve further on this subject here, apart from noting that sampling can be an efficient way to cut resource requirements for game analytics.
Figure 2: The drivers of attribute selection for user behavior attributes. Given the broad scope of application of game analytics, a number of sources of requirements exist.
One important factor to consider during the feature selection process is the extent to which your attribute set selection can be driven by pre-planning, by defining the game metrics and analysis results (and thereby the actionable insights) we wish to obtain from user telemetry and select attributes accordingly.
Reducing complexity is necessary, but as you restrict the scope of the data-gathering process, you run the risk of missing important patterns in user behavior that cannot be detected using the preselected attributes. This problem is exasperated in situations where the game metrics and analyses are also predefined — for example, relying on a set of Key Performance Indicators (such as DAU, MAU, ARPU, LTV, etc.) can eliminate your chance of finding any patterns in the behavioral data not detectable via the predefined metrics and analyses. In general, striking a balance between the two situations is the best solution, depending on available analytics resource. For example, focusing exclusively on KPIs will not tell you about in-game behavior, e.g., why 35 percent of the players drop out on level 8 — for that we need to look at metrics related to design and performance.
It is worth noting that when it comes to user analytics, we are working with human behavior, which is notoriously unpredictable. This means that predicting user analytics requirements can be challenging. This emphasizes the need for the use of both explorative (we look at the user data to see what patterns they contain) and hypothesis-driven methods (we know what we want to measure and know the possible results, not just which one is correct).
Strategies Driven by Designers’ Knowledge
During gameplay, a user creates a continual loop of actions and responses that keep the game state changing. This means that at any given moment, there can be many features of user behavior that change value. A first step toward isolating which features to employ during the analytical process could be a comprehensive and detailed list of all possible interactions between the game and its players. Designers are extremely knowledgeable about all possible interactions between the game and players; it’s beneficial to harness that knowledge and involve designers from the beginning by asking them to compile such lists.
Secondly, considering the sheer number of variables involved even in the simplest game, it is necessary to reduce the complexity through a knowledge-driven factor reduction: Designers can easily identify isomorphic interactions. These are groups of similar interactions, behaviors, and state changes that are essentially similar even if formally slightly different. For example “restoring 5 HP with a bandage” or “healing 50 HP with a potion” are formally different but essentially similar behaviors. The isomorphic interactions are then grouped into larger domains. Lastly, it’s required to identify measures that capture all isomorphic interactions belonging to each domain. For example, for the domain “healing,” it’s not necessary to track the number of potions and bandages used, but just record every state change to the variable “health.”
These domains have not been derived through objective factor reduction; there is a clear interpretive bias any time humans are asked to group elements in categories, even if designers have exhaustive expert knowledge. These larger domains can potentially contain all the possible behaviors that players can express in a game and at the same time help select which game variables should be monitored, and how.
Strategies Driven by Machine Learning
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. More than an alternative to designer-driven strategies, automated feature selection is a complementary approach to reducing the complexity of the hundreds of state changes generated by player-game interactions. Traditionally, automated approaches are applied to existing datasets, relational databases, or data warehouses, meaning that the process of analyzing game systems, defining variables, and establishing measures for such variables, falls outside of the scope of automated strategies; humans already have defined which variables to track and how. Therefore, automated approaches individuate only the most relevant and the most discriminating features out of all the variables monitored.
Automated feature selection relies on algorithms to search the attribute space and drop features that are highly correlated to others; algorithms can range from simple to complex. Methods include approaches such as clustering, classification, prediction, and sequence mining. These can be applied to find the most relevant features, since the presence of features that are not relevant for the definition of types affects the similarity measure, degrading the quality of the clusters found by the algorithm.
In a situation with infinite resources, it is possible to track, store, and analyze every user-initiated action — all the server-side system information, every fraction of a move of an avatar, every purchase, every chat message, every button press, even every keystroke. Doing so will likely cause bandwidth issues, and will require substantial resources to add the message hooks into the game code, but in theory, this brute-force approach to game analytics is possible.
However, it leads to very large datasets, which in turn leads to huge resource requirements in order to transform and analyze them. For example, tracking weapon type, weapon modifications, range, damage, target, kills, player and target positions, bullet trajectory, and so on, will enable a very in-depth analysis of weapon use in an FPS. However, the key metrics to evaluate weapon balancing could just be range, damage done, and the frequency of use of each weapon. Adding a number of additional variables/features may not add any new relevant insights, or may even add noise or confusion to the analysis. Similarly, it may not be necessary to log behavioral telemetry from all players of a game, but only a percentage (this is of course not the case when it comes to sales records, because you will need to track all revenue).
In general, if selected correctly, the first variables/features that are tracked, collected, and analyzed will provide a lot of insight into user behavior. As more and more detailed aspects of user behavior are tracked, costs of storage, processing, and analysis increase, but the rate of added value from the information contained in the telemetry data diminishes.
What this means is that there is a cost-benefit relationship in game telemetry, which basically describes a simplified theory of diminishing returns: Increasing the amount of one source of data in an analysis process will yield a lower per-unit return.
A classic example in economic literature is adding fertilizer to a field. In an unbalanced system (underfertilized), adding fertilizer will increase the crop size, but after a certain point this increase diminishes, stops, and may even reduce the crop size. Adding fertilizer to an already-balanced system does not increase crop size, or may reduce it.
Fundamentally, game analytics follow a similar principle. An analysis can be optimized up to a specific point given a particular set of input features/variables, before additional (new) features are necessary. Additionally, increasing the amount of data into an analysis process may reduce the return, or in extreme cases lead to a situation of negative return due to noise and confusion added by the additional data. There can of course be exceptions — for example, the cause of a problematic behavioral pattern, which decreases retention in a social online game, can rest in a single small design flaw, which can be hard to identify if the specific behavioral variables related to the flaw are not tracked.
Goals of User-Oriented Analytics
User-oriented game analytics typically have a variety of purposes, but we can broadly divide them into the following:
Strategic analytics, which target the global view on how a game should evolve based on analysis of user behavior and the business model.
Tactical analytics, which aim to inform game design at the short-term, for example an A/B test of a new game feature.
Operational analytics, which target analysis and evaluation of the immediate, current situation in the game. For example, informing what changes you should make to a persistent game to match user behavior in real-time.
To an extent, operational and tactical analytics inform technical and infrastructure issues, whereas strategic analytics focuses on merging user telemetry data with other user data and/or market research.
When you’re plotting a strategy for approaching your user telemetry, the first factors you should concern yourself with are the existence of these three types of user-oriented game analytics, the kinds of input data they require, and what you need to do to ensure that all three are performed, and the resulting data reported to the relevant stakeholder.
The second factor to consider is to clarify how to satisfy both the needs of the company and the needs of the users. The fundamental goal of game design is to create games that provide a good user experience. However, the fundamental goal of running a game development company is to make money (at least from the perspective of the investors). Ensuring that the analytics process generates output supporting decision-making toward both of these goals is vital. Essentially, the underlying drivers for game analytics are twofold: 1) ensuring a quality user experience, in order to acquire and retain customers; 2) ensuring that the monetization cycle generates revenue — irrespective of the business model in question. User-oriented game analytics should inform both design and monetization at the same time. This approach is exemplified by companies that have been successful in the F2P marketplace who use analysis methods like A/B testing to evaluate whether a specific design change increases both user experience (retention is sometimes used as a proxy) and monetization.
Up to this point, the discussion about feature selection has been at a somewhat abstract level, attempting to generate categories guiding selection, ensuring comprehensiveness in coverage rather than generating lists of concrete metrics (shots fired/minute per weapon, kill/death ratio, jump success ratio). This because it is nigh-on impossible to develop generic guidelines for metrics across all types of games and usage situations. This not just because games do not fall within neat design classes (games share a vast design space and do not cluster at specific areas of it), but also because the rate of innovation in design is high, which would rapidly render recommendations invalid. Therefore, the best advice we can give on user analytics is to develop models from the top down, so you can ensure comprehensive coverage in data collection, and from the core out, starting from the main mechanics driving the user experience (for helping designers) and monetization (for helping making sure designers get paid). Additional detail can be added as resources permit. Finally, try to keep your decisions and process fluent and adaptable; it’s necessary in an industry as competitive and exciting as ours.
篇目2，Marczewski’s Gamification User Types 2.0
by Andrzej Marczewski
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.
User Types 2.0
I was trying to simplify and improve my gamification user types. Version 2 is just that and a little more. After more research and the results of mine and others surveys on the matter, I have realised a few things.
The four basic types; Achiever, Socialiser, Philanthropist and Free Spirit are all fine. They work and can be left exactly as they are. I am also happy that the extrinsic types (Consumer, Networker, Self Seeker and Exploiter) are ok, however – they have caused a lot of confusion with people. I made everything a little too black and white – it was as if people saw my intrinsic types and extrinsic types as good and evil!
As such, I offer this new version. It is not a replacement, more an addition. If you are using the current four or even eight types – keep using them, they work just fine! However, this is where my thinking and research has led me, so I wanted to present it here properly.
The image below shows the basics.
Six User Types
As you can see, there are now six names on the board. Philanthropist, Achiever, Socialiser and Free Spirit are still there and still represent the four intrinsic motivations of RAMP, however we now have Disruptor and Player. Neither of these is new, Player was first introduced in my original work on user types as a name for the extrinsically motivated users. Disruptor was introduced recently as my “negative” user type.
There is still a split between action and interacting on users or systems, though this time Disruptor and Player straddle more than one segment. Disruptor is seen here as Acting on users and systems, where Player interacts with users and systems.
Socialisers are motivated by Relatedness. They want to interact with others and create social connections.
Free Spirits are motivated by Autonomy. They want to create and explore.
Achievers are motivated by Mastery. They are looking to learn new things and improve themselves. They want challenges to overcome.
Philanthropists are motivated by Purpose. This group are altruistic, wanting to give back to other people and enrich the lives of others in some way.
Players are motivated by Rewards. They will do what is needed of them to collect rewards from a system.
Disruptors are motivated by various things, but in general they want to disrupt your system, either by directly or through other users.
Players are happy to “play” your game, where points and rewards are up for grabs. Disruptors want nothing to do with it and the others need a bit more to keep them interested.
This looks a bit like this
willing to play
It took me a while to realise this, but black and white is actually not all that much use when talking about how people behave. Grey is a much more usable area for this. So, I have created a little grey with the new user types. Whilst Players and Disrupters can be seen as distinct user types in their own right, they can also be viewed as modifiers for the other four types.
If you have seen the original user type descriptions, that is how I created the extrinsic groups.
So the Player characteristics of being interested in the rewards a system can give them can be seen as modifying the motivations of the intrinsic types.
The same can be said of the Disruptor. Their interest is in disrupting the gamified system. The reason for this can be varied. It may be considered purpose. They feel that disrupting the system has a greater meaning, be it educating the creators of flaws or proving that the system is somehow wrong. It could be autonomy. In the intrinsic types, autonomy is seen as a positive motivation, exploration and creativity. However, this can just as easily be seen as wanting to break free from the confines of the system – how can you have true autonomy when there are rules in place that you don’t like. Mastery can be achieved as they learn how to disrupt the system and Relatedness can be seen in the status that such acts can give them.
All of these things relate to the positive motivations I talk about, but they would be considered by most as the polar opposite. Rather than helping, destroying. However, at this point it is worth considering the more modern meaning of Disruptive. These days disruptive refers to improving the system by breaking down the norms and showing new and improved ways.
As I say, this creates a lot of grey areas. Disruptors should for the most part be discouraged from being in a stable system. If they are hell bent on breaking the rules for no reason other than because they can, they need to be removed. However, they may well be the key to unlocking better levels of engagement by showing you what is wrong with a system and how to improve it!
There you have it. My current thoughts on the gamification user types. It may seem like I am making the waters muddy, but if you choose to use this version of the user types, you will see that it gives you much more flexibility and a better understanding of the grey areas of user motivations!
篇目3，Sixteen ways to motivate – is your game tapping into them?
by Gabriel Recchia
The following blog was, unless otherwise noted, independently written by a member of Gamasutra’s game development community. The thoughts and opinions expressed here are not necessarily those of Gamasutra or its parent company.
7 Habits of Highly Effective People. The 8 Essential Steps to Conflict Resolution. I’ll be the first to agree that including an arbitrary number in a headline makes an article sound like something that you’d find in the bargain bin of your local bookstore, but in this case there’s a rationale.
In a series of studies from 1995 to 1998 that investigated fundamental human drives/motives for action (status, hunger, sex, etc.), Dr. Steven Reiss and colleagues started with a list of “every motive they could imagine,” including hundreds of possibilities drawn from psychological studies, psychiatric classification manuals, and other sources.
They whittled this down to a mere 384, and distributed a survey designed to measure the importance that survey-takers assigned to each motive to over 2,500 people.
Plugging the results into a factor analysis to find out how many distinct underlying dimensions were necessary to account for the majority of variance yielded 15 distinct clusters of motives that people rated as of particularly high importance. (They added one more in 1998). In no particular order, they are:
Based on Multifaceted Nature of Intrinsic Motivation: The Theory of 16 Basic Desires, Table 1.
This is at odds with the reigning approach of dividing motivations up into extrinsic vs. intrinsic, and is much messier from a theoretical perspective. But as the psychologists who conducted the studies argue, there’s no reason to expect that an adequate theory of something as complex as human motivation should be anything but messy.
We have over 50 distinct cortical regions, over 100 different neurotransmitters, and thousands of proteins. Why not at least a handful of innate motivational categories?
Certainly, the theory has its flaws. There is ample evidence that people don’t have a good grasp of what really motivates them (which puts limits on what we can learn from surveys), and the theory doesn’t do justice to fact that our reactions to “things we want” vs. “things we want to avoid” are subserved by different neural systems. But it certainly provides an interesting perspective.
Many designers were astounded at the popularity of Farmville, whose key mechanics flew in the face of received game design wisdom, and Zynga’s continuing demise has been heralded by some as proof that the intrinsic motivation provided by a good game ultimately trumps the extrinsic motivation of praise and badges. Maybe so.
But it’s also possible that the motives that Farmville’s core mechanics tap into—accumulating items (Reiss’ “saving” motive) and the desire to give a Green Whatsit to someone who gave you a Blue Doohickey (reciprocal altruism, which falls under Reiss’ “idealism” motive)—are not inherently ‘worse’ than other motives, just hard to sustain in the long term in the absence of other motivating features. Arguably, many good MMOs take ample advantage of both of these motives and many more besides.
My previous post highlighted some of the difficulties of designing intrinsic motivators into a game. Even if the intrinsic/extrinsic distinction is a meaningful and important one to make, the difficulties of navigating this space in a real-world game may make multi-factor theories more useful to game designers in practical terms.
In particular, they can be used as “lenses” in the sense of Jesse Schell in The Art of Game Design, which contains 100 thought-provoking lenses through which one’s game can be viewed and improved. One can imagine developing corresponding lenses for each of Reiss’ fundamental motives (e.g. “The Lens of Independence: Does my game make people feel autonomous? Do players have a sense of control over their actions? Do they feel free to select from meaningful choices?”)—and in fact, Schell’s list already includes several that are relevant to some of the motives above (The Lens of Competition, The Lens of Cooperation, The Lens of Needs, The Lens of Control, The Lens of Community).
(Drawing up lens cards for Reiss’ remaining motives, and designing a game that satisfies the motives of “desire to eat,” “desire for sex,” and “desire to raise own children” is left as an exercise to the reader.)
Although most designers already have a sense of what motivates their audience, focusing one’s attention on the sixteen dimensions that have emerged as particularly important in large-scale studies of human motivation may be a worthy endeavor, if for no other reason than to identify which motives one’s game already addresses best, and to evaluate whether ramping those up even more would improve it further.
In addition to features that conventional wisdom suggests are motivating to players (rewards for skill development, compelling narrative, gradually increasing difficulty, etc.), ’16 Basic Desires’ theory may inspire further ideas for underappreciated features worthy of consideration.
篇目4，4 Temperaments – Some Remarks on Gamer Typology
by Alfons Liebermann
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The following thoughts are an offspring of a discussion we had with Andrzej Marczewski and – indirectly – Richard Bartle, both of whom have dealt with the problem of a gamer typology. In addition – once again mediated by Andrzej Marczewski – there are traces of Nicole Lazzaro’s typology of fun, “4 keys 2 Fun”. Although one cannot find her marks anymore, it is crucial for the underlying reward system.
Presenting our model we do not claim originality but just an extension of some already elaborated gamer typologies. What we can add though is our experience as well as certain theoretical assumptions that guide us.
1. Why there is a need for a gamer typology
The biggest advantage is that using such a typology for a reaction system (an AI). On this basis you allow a system to individually respond to different gamer psychologies. Although a common request, this kind of customization is somewhat rare, not because it implies some sophisticated programming but because one has to elaborate a highly flexible model of gamer temperaments, a model that does not cover basic and known prototypes but also the multitude of nuances. Therefore – as Richard Bartle rightfully pointed out – the crucial question of such a classification system does not lie in the listing of possible gamer psychologies but in a sound theoretical foundation.
Here a remark of the famous French psychoanalyst Jacques Lacan was inspiring. Lacan once noted that the other side of the university discourse is hysteria, and that signification arises where two presumably unlinked signifying chains glide past each other.
One may put it more bluntly: motivation is fed exactly by the things people fight with – and it is this very conflict that becomes the core of the game.
If one reads the model against this background one realizes that the respective gamer psychology is not derived from a certain prototype, but that it points to a conflict which may have many forms (and in which the psychological axes describe the dominant forces).
2. The axes of desire
We have two axes: From the right to the left we have an axis that signifies the polarity between the individual and the collective. It reflects the question as to what extent a gamer entrusts himself to a collective order or whether he feels obliged to act on his own account. One could be tempted to derive certain gamer affinities towards multi-player vs single-player games – but this is an approach that could easily lead to misinterpretations.
The conclusion at least that the POLITICIAN could be identified with the leader of a World of Warcraft cohort would be a gross misunderstanding. Whether or not somebody refers to a collectivist mindset has nothing to do with the respective social practice. It is essential though that the POLITICIAN conceives of himself as a representative of a collective order, a corporate identity so to speak. This reveals him as a strategy gamer who – from his god’s perspective – is supervising his realm.
Here the second axis gains importance. It is oscillating between strictly ordered and ad-libbing gameplay, between rules and breaking the rules, or if you prefer: between tradition and innovation.
It is evident that the player of a strategy game opts for the rule (i.e. for law and order) and that he abhors the irregular: randomness, chance, the intrusion of hostile units.
Nevertheless this conflict describes his interior map as well as it profiles his preferred means. He relies on repetition and accumulation, the perpetual repetition of the law, and at the same time on the necessity of steady growth. The phantasm that drives the gamer is omnipotence – and his reward the resulting status.
Given this short psychology the diagonal points to the POLITICIAN’s perfect antipode, hence the reality that he actually tries to ban. This is the appearance of the FREE SPIRIT – and the double break of the axes. Disrespecting the social order and neglecting the rules reveal him as the politician’s true antagonist.
The FREE SPIRIT’s kick is the adventure. He does not care for repetition but is striving for the unique moment. Ignoring the rules he tries to outdo them instead. That is his thrill: the rush of adrenaline, instant karma, paradise now.
His desire of freedom leaves no room for social arrangements. In case of doubt he opts for the shortcut. Enthusiast that he is, he constitutes an aesthetic and sophisticated avantgarde – without followers though.
From his point of view the system constitutes a natural, even personalized adversary. The system however (that you can depict as a dark imago, a punishing father) acts as a magnet, attracting him magically. To betray the system is a big motivation – and in case of success, a respective satisfaction.
Now let us focus on the upper left square. Here we have the ACHIEVER, him who takes the mastery of the machine as his very objective. If we put him on the side of the FREE SPIRIT, we could take them for relatives – and rightfully so, since both of them lean towards the pole of individualism.
What differentiates the ACHIEVER from the FREE SPIRIT is that he prefers to play within the rules. He is not interested in betraying the system. On the contrary: he is determined to dominate it. The highscore, his skillfullness, shows him at the height of the system, and HIS awesomeness is actually what he is looking for.
While the strategy gamer is indulging in fantasies of omnipotence, the ACHIEVER is obsessed by the phantasm of the machine: he yearns for the individually sensed absolute power. In the terminology of the computer games we can identify him as the ego-shooter that stops the surging horde: the last independent, the dweller of an apocalyptic world that made warfare his home.
What he has in common with the FREE SPIRIT is the free roaming attitude, but his favored strategems correspond to the POLITICIAN’s behaviour. Like him he is obsessed with repetition and accumulation. The continuous repetition helps him to improve, the accumulation serves his as an imprint of boosted competence. Whereas the objective of the games resides in the perfect control of the environment, his imago depicts him as grandiose lone fighter (an appraisal he might not able able to enjoy outside the game).
Taking again the diagonale into focus his anatgonist becomes visible: It is the SOCIAL GAMER gamer that does no care about mastery (ruling the sytem) nor struggles for a considerable excellence in playing the game. In fact this conflict can easily be discerned as the gap between high-tech shooter games and their poor equivalents à la Zynga.
The SOCIAL GAMER is casual by heart. Gaming is just a way of killing time: a dialogue without dialogue. Once again we face a paradox: Whereas the strategy gamer, the POLITICIAN, evokes a perfect social order, the SOCIAL GAMER invokes the human contact he is actually missing. In this invocation of society the disparate areas overlap – and that’s why the POLITICIAN and the SOCIAL GAMER are located on the same side.
Here we can see the difference to the FREE SPIRIT. Whereas the former is looking for the state of emergency, the SOCIAL GAMER is just interested in social standards, simple, easy-to-learn, predictable constellations.
Nevertheless the SOCIAL GAMER’s approach is not inspired by the need of social exchange, but by his will to excel. The farmville gamer that buys himself a tractor and augments his capacities and position thereby, demonstrates that his currency is not cooperation, bus competition instead. Once again we can see the repercussion of the antipode: While the ego-shooter stuggles with his NPC-adversaries, the social gamer degrades his co-gamers to NPCs.
It is evident that these 4 prototypes may seldomly be found in their cristalline form. Instead we encounter psychological nuances and a variety of behaviour instead.
The axiality permits to understand the psychological field as a map, where distance may be translated as a gradient for kinship. More important than this spatial alignment though (which is ideal for implementation) is the fact that each gamer prototype cannot be explained by itself but only through his antagonist.
In this sense the absent part of the field is ever-present – and should be understood as a key for the gamer psychology.
Hence the game reveals the logics that cinematographic narration has taught us: You will not understand a character unless you know about its inner conflicts. It’ all about conflict, stupid!
篇目5，The opportunities in mobile gaming are in asynchronous social multiplayer games
by Kevin Corti
It should be clear to anyone that is interested in computer games that the mobile gaming market is growing very fast and, with smartphone penetration still accounting for only 40% in even major markets, that there is room for a lot more growth and for several years still.
It is also clear, to anyone who is actually making mobile games, that creating a game that people want to play en masse, let alone pay for (or in) en masse, is extremely hard. There are already over 130,000 games already submitted to the Apple App Store. Games like CSR Racing may be pulling in US$12million in their first month, but there is a very long tail in action here and the average revenue for a mobile game is reportedly less than US$4,000. Whilst it is still theoretically feasible to develop a mobile game for a few thousand dollars (working unpaid still has an opportunity cost even if there is not an actual monetary expenditure) most games from professional studios will have development budgets ranging from US50,000 to as much as US$1million.
The costs do not stop at simply making a game; far from it, next comes the marketing cost. Developers that base their plans/hopes/dreams around some form of free, natural virality are most likely going to fail. This is especially true of iOS games where (a) getting discovered requires being at the top of the charts, and (b) getting to and staying at the top of the charts costs lots of money. Putting that even more succinctly; getting visibility for your app WILL cost money….and no small amount of it.
Developers frequently drop a pot of money into user acquisition services such as Tapjoy (players are incentivised to download your game) or FreeAppADay (where players go to find normally paid-for apps being offered for free temporarily). These, and other methods, invariably cost from $10,000 and upwards on ‘day 1’. For a game to be profitable it needs to:
(1) generate revenue per user (ARPU) at a rate that exceeds the average cost per user (ACPU)
(2) reach a critical mass of users to ensure that the ‘net’ profit covers the initial development cost.
We must also consider that ‘net’ revenue is the gross sales revenue minus a whole host of direct costs starting with Apple (30%) but possibly also including any sales taxes, licensing costs, publisher’s cut, partner revenue share and on-going infrastructure (e.g. server) costs.
It is also very rare for a game to be created then launched then left unattended. We are in a ‘games as a service’ era and games are usually hooked up to some form of user behaviour data collection and analytics tool nowadays, meaning that developers can see what is working and what is not. That means not just technical bug fixes but user interface improvements, tutorial re-working, revisiting game variable to achieve better balancing, editing narrative, creating new content and new features. A game that does at all well will invariably be ported to other platforms (Android, Windows mobile/8, Amazon Kindle) and/or be localised for different territories. That’s more cost folks.
So, making games, marketing them and maintaining them costs a lot of money. It is a crowded market and one where customer loyalty is low and where new/different games are foisted at players from all angles. If, therefore you want to make games for the mobile phone and tablet market, you had better be clear about what kind of games you are going to make if you want to have a chance of achieving breakeven let alone amassing huge profits. What are the options? I boil these down into four (broad but distinctly different) game types. These are:
 Casual games (that work on mobile devices) – ‘play by yourself on the move’
Conceivably this can includes games that involve more than one player – e.g. two players, one finger each on same screen – but is invariably about single player games. If done right then the games are designed for the specific hardware capabilities (some might say ‘limitations’) of mobile devices but many are copies of web, PC or console games which are simply ported to mobile because it is feasible to do so not because it is sensible to do so. Cut The Rope, Plants vz Zombies and Fruit Ninja are exemplars of this category of game but for each of these there are a hundred (make that ten thousand) Tic Tac Toe clones and shoddy platformers. If you make this class of game then you need to be highly aware that the only benefit you have over console, PC and browser games is that your game can be played on the move. Design for that modality of use not for what is technically achievable.
 Casual social games – games that have a (vaguely) social layer where you ‘play by yourself….then see if your friends can beat your score’. Put another way; ‘games that are given another dimension because your friends are involved to some degree’.
These games are usually characterised by being a fundamentally single player experience on top of which is bolted a ‘challenge friends’ and/or leader-board functionality. This is rapidly becoming the de facto design pattern for mobile games. I regard this as a somewhat lazy and possibly an commercially finite approach. It is often achieved with basic functionality provided by third party services such as OpenFeint or GameCentre that very much looks and feels ‘bolted on’ rather than having been crafted to enhance the player experience. This also leads to several frequent interruptions to the playing experience in the form of registration, login and pop-up leader-board or achievement screens that look completely different to the game art and UI. If this is done well, e.g. where the playing experience is genuinely enhanced by the ability to try to perform better than people you know, then there is quantifiable end user value. This doesn’t disguise the fact, however, that the product is essentially still a single player game. These services also all exit to ultimately build a user-base for the service itself (e.g. to engage the user with advertising or cross-promotion interstitial ads) and that commercial goal conflicts with the game developer’s goal of engaging and retaining their player as long as is possible.
There is a secondary type of game in this class that closely resembles the Facebook/browser-based ‘social game’ type. Numerous social games have made their way to mobile devices (e.g. Farmville,
CityVille and Ravenwood Fair) however the game-play remains fundamentally of a single player nature which is augmented with the social mechanics of, for example, gifting, sharing and visiting and where such behaviour is rewarded with free virtual goods, in-game currency or other utility value. Despite seemingly interacting with friend’s in-game on a frequent basis, the nature of those interactions exist solely to bring about free user acquisition for the developer rather than to deliver intrinsic fun from playing. You interact with your friends because you have to not because it makes the game more fun in of itself.
 Synchronous multiplayer games – ‘play with or against other (probably quite hard-core) players in real time….on a mobile device’.
These kinds of games are rare and for two good reasons: firstly, they require a level of technical infrastructure and service provision that is typically very expensive to put in place and to maintain, and, secondly, because it is statistically unlikely that any one player has many friends that likes (an downs) the same game they do and whom are able to play that game at exactly the same time on a regular basis as they do. There is also the factor that in order to do so they may also require the same device/platform as you. ‘Android on a Samsung? Sorry you need an iPhone 4 or higher to play this game”.
Synchronous collaborative or competitive play is major aspect of the PC and console gaming experience where play sessions are much longer, happen at more regular (often coordinated) times and in environments conducive to that activity e.g. where you can strap on a headset and swear a lot. The very nature of synchronous gameplay tends to lend itself to more traditional, or ‘hard-core’, games genres which is not mass market (when expressed as a subset of the mobile phone gaming market overall). Mobile game play typically happens at unplanned opportunistic times, for very much shorter sessions spread throughout the day at a wide variety of locations many of which do not offer a reliable cellular or wifi network connectivity. I see synchronous (‘real time’) multiplayer gaming as a small niche that offers creatively interesting but commercial limited opportunities.
 Asynchronous multiplayer games – games where ‘the entirety of the fun is derived because you are playing with (or against) friends but which do not require an immediate data exchange’.
This is the class of mobile game that I think truly fit the ‘social mobile game’ definition. Whilst a real time (type 3) game is clearly about a genuine interaction with other (real) people and fundamental to gameplay, the very fact that this will be practical to only a very minor subset of mobile gamers make it, IMHO, by definition ‘antisocial’. Asynchronous mobile games, when done well, deliver playing experiences that are very much enhanced by the involvement of others but which do not fail to cater for the very real modality of mobile device usage (‘anytime, anywhere’).
Indeed, these games deliver an experience that is intrinsically fun because they are using a device that exists to enable communication and interaction between people who are not physically together in the same location and which does not require cumbersome peripherals or – at least not all of the time – power supply or data connectivity. Asynchronous games can be somewhat ‘lossy’ in that the exchange of data isn’t overly time-sensitive.
My archetypal example of this kind of game is Draw Something (OMGPOP/Zynga). It’s success may have been over a fairly short time frame (approx. 6 months) but it reached 90million downloads and delivered outstanding revenues (reportedly $50-75million).
The title of this ’blog is about where I believe the (greatest) opportunities lie for mobile gaming. Given that commercial success is highly dependent upon successfully acquiring users and at a cost that is less than the revenue that they generate, how then do the different types of game (as defined above) contribute, or not, towards this goal?
Casual mobile games – no direct user acquisition benefit. These games lack both the instruments for users to spread the word to other users and the intrinsic motivation for them to do so. You are playing a single player game on your mobile device. Your progress in game and enjoyment of it are totally unrelated to whether or not your friends may be playing it. Score 0/10
Social casual mobile games – some benefit if the developer owns the user data, however that is rarely the case when using third party APIs such as OpenFeint. Zynga have a whole raft of ‘X with friends’ games in this category and have built an eco-system aimed at capturing that user data and then cross-promoting their games (thus avoiding the $2/user cost of acquiring users through other channels). Most developers are unlikely to be able to afford to replicate that ecosystem too any degree. Equally, as these games can be played as a single player experience, the user’s motivation to connect social network accounts and to enable ‘sharing’ etc is not necessarily high. Visibility of the game name and link on Facebook is a positive factor but one that is limited by the fact that the game isn’t immediately playable on that platform if you are not using Facebook on the same mobile device. Score 5/10
Synchronous multiplayer mobile games – whilst there is the logical argument that players must have other players with whom to interact with in this case, (a) the potential user reach is fairly insignificant, and (b) the likelihood is that you will be paired with/against strangers by the system (in order to ensure there are enough people to take part) rather than being required/motivated to bring new players that you actually know into the game. Score 2/10.
Asynchronous multiplayer mobile games – these are the very definition of what makes the foundation for a genuine virally-promoted game as you have to have friends to play with or against or you can’t play yourself. There is not alternative state. These games – such as OMGPOPs Draw Something – invariable involve a very early screen asking you to connect Facebook or Twitter accounts or to send out email invitations. There is certainly a trust barrier here and having a genuinely stellar game offering is unquestionably of fundamental importance, but get that right and your entire user-base is acting to expand itself. Make a great game that is unquestionably fun and which delivers that fun over a sustained time period (e.g. has longevity to the play experience) and you have a hit on your hands that should only need seeding with an initial paid-for user-base. Score 10/10.
So, asynchronous multiplayer games it is then…..but what makes for a good asynchronous game?
Mobile gameplay needs to be designed not simply just to work on mobile devices but also to be designed for the mobile device user. These are quite different things that are often overlooked. Just because the iPhone 4/iPad2 could deliver highly impressive raw computational and graphical power capable of delivering ‘near console’ game experiences doesn’t make it appropriate to do so. Who has 20+ hours to play a game on their iPhone where each level takes 20minutes or more?
An inelegant but essentially accurate term to describe the prevalent modality of use is ‘dip in and dip out’ gameplay. Contextual scenarios involving stops at traffic lights or being in the queue in Starbucks typically get used to illustrate this and these resonate with casual geeks and professional analysts alike. They also ignore the fact that something like 50% of mobile game play time actually happens in bed or on the sofa where the user sessions are not measured in seconds but dozens of minutes. ‘Dip in and dip out’ gaming is certainly very important but it is not the only factor.
We are only just beginning to understand the specialist craft of effective mobile game design but a crude rule of thumb of revaluating any game concept’s appropriateness for mobile deployment (versus PC, Facebook etc) could simply be:
 Is this game fundamentally fun because I can play it anytime and anywhere?
 Can I start playing, stop playing and re-start playing with minimal ease?
To those questions we can then assess the level of genuine organic user acquisition by asking:
 Is this game made fun because people being able to play with or against their friends is central to it’s design?
If you can answer ‘yes, yes and yes’ then go build that game!