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Mobile Gaming Summit 2012

I have recently been getting more into mobile design and development and so was excited to attend the Mobile Gaming Summit in New York today. It was a well attended event, with what seemed like dozens of presenters from top mobile studios sharing tips on everything from user acquisition to design, mobile analytics, cross-platform development, finance, and social. What I wanted to share here quickly were some of the resources that were mentioned at the summit because I think they would be useful to any mobile studio / developer who’s just starting out (noobs like me!). So, by topic, here are some services to check out:

  • Ad Platforms for user acquisition
  • Analytics
    • Flurry (free analytics platform to help you understand how users are using your app)
    • Bees and Pollen (analytics to help optimize the user experience based on the user)
    • Apsalar
  • Cross-Platform Technologies
    • Corona (uses a language called Lua that I’ve never heard of)
    • Marmelade (program in c++, deploy to iOS, Android, xbox, etc.)
    • Phone Gap (program in javascript, HTML, CSS)
    • Unity (geared toward 3D games)

In general I was impressed with the amount of data driven design going on in the mobile apps / games space and how the big studios are really optimizing for attention, retention, and monetization by constantly tweaking things.

Other tips that were shared included things like: use Canada as a test market to work out kinks in your apps before you launch in the larger U.S. market; concentrate marketing efforts / budget in a short period of time to attain the highest rank in the app store as this drives more organic growth; the industry is heavily moving towards a free-to-play model with monetization done with in-app purchases or advertising.

In the next few weeks I’ll be excited to try out some of these services with my new app, Many Faces, which launched a couple weeks ago. I think it’s all about the user-acquisition / marketing at this point …

computational journalism games newsgames

Newsgame Platforms

So this past weekend I had the opportunity (and pleasure) to attend a newsgames workshop at the University of Minnesota. The purpose of the gathering, which brought in academics, game designers, and journalists, was to brainstorm around the topic of newsgames. What are some of the questions that we need to address in order to make progress in this domain?

While there were discussions on everything from the business end of monetizing games, to organizational / cultural clashes, here I’m going to summarize some of the thinking we did on the medium of newsgames itself including issues of building platforms for newsgames. Platforms is, incidentally, one of the areas discussed in Newsgames: Journalism at Play by Bogost, Ferrari, and Schweizer.

At the top of our list was the question of how news organizations could repurpose their existing content (including text, video, audio, or data) into newsgames? There’s a huge investment in the content that’s already being produced by newsrooms. Can this form a platform for newsgames? Can we come up with new ways to take content that’s already produced and create compelling, playable experiences from the content? Once we figure out effective mappings, can we generate these content games automatically, or with minimal human involvement? Some examples of games already touching on this space are Hangman RSS, and Scoop, both of which use news headlines to produce word puzzle games. Some of my own work on Salubrious Nation has looked at how to take data sets from the likes of and turn them into playable experiences.

A recurring tension that we identified was the timeliness issue. What’s the scale and speed with which newsgames need to be developed? Certainly, there are many different types of stories that could be told with newsgames; do we need them for breaking news, or does it make more sense to make newsgames for ongoing issues and debates? Programming is simply time-consuming, and combined with editorial development, newsgames cab be pretty slow to develop. But, if we were to think of a platform or templates for newsgames that make use of recurring streams of information this could alleviate the time strain. We brainstormed some content streams that we thought would fit this model: sports data, budgets, economic indicators, natural disasters, weather, conflict / war, births / deaths, business / financial statements, movie releases, book/restaurant/other reviews, traffic, crime, comments and other user generated content, travel … and the list could go on. If we have cyclic data streams, why not create game templates that can be quickly generated based on the latest dump of that stream?

Running counter to the idea of developing a platform for newsgames was the tension between abstraction and specificity. If you build a framework (abstracting the process) what does this mean for the kinds of stories you can tell? Typically games are rich, semantically laden experiences, so if we platformatize the newsgame production process we might lose some of that nuance and richness. Let’s draw an analogy to Google maps as a platform for developing geo-stories. When those first came out they were relatively limited and you pretty much just had pink pins to indicate locations: certainly constraining to the types and richness of stories you could tell. But now you can do a lot more with Google maps: it’s more customizable, you can embed google charts, and the flexibility built into the framework allows for many different types of stories to be told. This makes me optimistic that we might yet find platforms for newsgames that vastly simplify the authoring process but still allow for a certain flexibility and nuance to the story.

These are really just a sampling of the issues and questions that were discussed at the workshop, but some that I personally thought were the most interesting. There’s a lot of work to do in this space, both designing and studying what works and what doesn’t. It’s great to have participated in the brainstorming; now it’s time to get to work.

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Balance and Challenge in Playable Data

Note: A version of this will appear at the CHI2011 Gamification Workshop.


Work published this year at CHI has introduced the notion of game-y information graphics which take raw datasets from sources such as and create playable visualizations by adding elements of goals, rules, rewards, and mechanics of play. One example is Salubrious Nation, which uses geographically tagged public health data such as smoking and obesity rates, to create a guessing game. The goal of the game is to accurately guess the magnitude of the given health parameter for a randomly selected target county. A player’s guess can be informed by looking at the map (See screenshot below) for visual clues as a slider is changed, or by using hover-over information on correlated variables (e.g. poverty rate or elderly population rate).

In addition to allowing players to use the map-based graphic to arrive at insights about the data and to redistributing players attention to different aspects of the data, such an approach also offers the promise of reducing the amount of effort needed to repurpose that data into new playable experiences. Interested readers can see the paper for all of the details.

In the remainder of this post, however, I would like to expound on and explore the design difficulty associated with creating a challenging and balanced game experience when drawing on raw datasets as input for the construction of a game. Ordinarily when designing games, substantial effort is directed to level design. In fact, many games employ dedicated level designers who work with the game designer in order to provide the right amount of challenge, reward, and balance to the game experience (See Game Design Workshop for more details).

In contrast to such heavily authored experiences, gamified data experiences (whether they be based on infographics as in Salubrious Nation, or not), may draw on data that is incomplete, inconsistent, or dynamic. For instance, if a dataset is missing values, such missing values must be taken into account so that this does not completely break the game, or at least does not substantially reduce the engagement of the experience. Salubrious Nation relies on correlations between health variables to demographic variable such as poverty rate, to help users predict the public health variable (e.g. smoking rate). If the data were updated in such a way that relationships (i.e. such as a correlation) was diminished or removed, this would affect the playability of the game.

Dealing with data that is updated, refreshed, or otherwise dynamic represents a design challenge. Another example, the California Stimulus Map Game was a game-y infographic created for the Sacramento Bee newspaper website. In this trivia game players had to answer a series of trivia questions about stimulus funds by interacting with a visual map of the state of California. Two weeks after the initial publication the data for the map in the game had already been updated by the government. Not only did this affect the visual representation of the map, but it also impacted the answers to some of the trivia questions, thus forcing the designers to update the game in order to accommodate the new data. One approach to dealing with this issue would be to devise better automatic authoring routines so that trivia answers could be extracted directly from the data without human intervention (e.g. “What is the county with the largest (or smallest) amount of stimulus money”). More research needs to be done to determine the best way for dealing with changes to data which can impact a play experience. Methods developed should be robust to incomplete, inconsistent, or dynamic data and should provide for a playable experience regardless of reasonable changes to such data.

A more general issue with raw data is that the challenge or difficulty of the experience produced in the game is hard to control. With one set of data as an input a game may be too easy but with another it could become too hard. For instance, in Salubrious Nation there were 8 levels, each using a different public health parameter. For each of the levels we measured the average accuracy of the guesses that were produced by the 41 players in our experiment. This is shown in the figure below (with error bars showing the standard deviation of accuracy). As can be seen in the graph, some levels were more difficult than others, even considering some potential learning and improvement by players in the latter levels. This is in contrast to the typical game design pattern of increasing difficulty of levels. Indeed, based on the collected data it may be advisable to re-order the levels in Salubrious Nation so that easier levels are first and more difficult ones later.

In the absence of carefully authored levels of a game, we can still collect log data from players in order to infer difficulty and challenge. While this is relatively straightforward for a puzzle where there is a correct answer and a relatively simple metric can be used to infer difficult, there remain open questions for research. How can log data be used to infer other measures of difficulty (frustration even)? How can playable data games be rapidly and perhaps automatically re-adjusted to assess difficulty so that in a short period when a game is first being played it is able to evolve and adjust itself to provide an appropriately balanced and challenging experience?

These questions apply generally to the gamification of any data-based resource. When gamifying a dynamic, perhaps arbitrarily defined data source, how can we arrive at estimates for the challenge, balance, and playability of those experiences? Properly instrumented such games could perhaps automatically adapt their levels and difficulty to compensate for differences in the input data. I believe that answering these questions will be essential to being able to more rapidly create compelling gamified data experiences in the future.

computational journalism games gamification visual analytics

Playing With Data

Recent years have brought a steadily growing international interest in openly publishing government and other civic datasets online. Government efforts such as the United States’, the United Kingdom’s, and other European efforts such as in Norway immediately come to mind. There are commercial interests in this space as well, such as the newly launched Data Market, which goes a step beyond data curation to provide visualization tools as well.

So now that we have droves of data, what do we do with it?

One novel approach toward facilitating engagement with such datasets is to create playable experiences from that data. In particularly I have been exploring the design space of data-driven information graphics which include familiar aspects of games, such as goals, rules, rewards, competition, and advancement. The promise is that engagement and learning will be enabled by connecting game activities and goals together with interactions which facilitate visual analysis of the data. For instance, if in order to achieve a high score in the game, the player has to visually compare the values for a data attribute, then in the course of playing they may have an additional opportunity to engage with the data in a meaningful analytic capacity.

At Rutgers University we recently created a game called Salubrious Nation, which explores this idea of playable data. Salubrious Nation takes U.S. public health data, such as smoking, obesity, or diabetes rates, and creates a playable map-based game. The goal of the game is to guess the public health data value (e.g. binge drinking rate) for a randomly selected target county. The player can inform their guess by using other correlated demographic data such as poverty rate, or by looking at how the graphic changes colors when the player manipulates the slider to enter their guess. The player earns points based on how close their guess was to the actual. Each level is a different public health issue and at the end the player can compare their score to others who have played and finished. A screenshot is shown below, and the reader is invited to try the game online at

We ran an online study of Salubrious Nation with dozens of people in order to better understand how such a game-y (i.e. game-like) presentation of information impacts exploration of the data, insights and learning, and fun. What we found is that, when compared to a standard information graphic presentation of the same data, the game-y version was able to redistribute player’s attention in interesting ways. For instance, people’s interaction with counties was more uniformly distributed across the country due to the random nature of selecting target counties in the game. Also, players used the slider more in the game-y presentation, likely because that was both necessary to enter a guess as well as helpful in informing the guess since regional patterns could be see by tweaking it.
Considering the selective attention issue, where people are more likely to pay attention to things that they already agree with, this result suggests an opportunity to get players to look at aspects of the data that they might not otherwise be inclined to look at. While the game-y presentation wasn’t any more fun or engaging than a standard infographic, Salubrious Nation does demonstrate that the goals embedded in a game can successfully motivate interactions and bias both the exploration and the nature of the insights for players.

While Salubrious Nation is an initial foray into the design space of playable data and game-y information graphics, there still remains a huge array of possibilities for exploring this design space. Future designs might incorporate different player interaction pattern (e.g. player vs. player), game resources (e.g. lives, currency, power-ups) and goals or mechanics (e.g. collecting, building). Moreover, there are many other possibilities for game-y presentations of data depending on different data types (e.g. network, tree, temporal, etc.) and different non-map-based visual representations (e.g. node-link, timeline, etc.). Some game mechanics may be more general and adaptable to different data types whereas others may be more specific. More research needs to be done to better understand the generalizeability of these methods.

Opportunities for innovation abound here. The gamification of data does not have to be limited to simply presentation, but could be applied to all facets of a news information pipeline such as information gathering or dissemination. Imagine gamified mobile apps that motivate users to interact with the world in a news gathering capacity. This could enhance activities such as citizen science, or add to news organizations’ ability to gather more and diverse information.

Indeed there is a spectrum of artifacts that can be imagined within the realm of playable data. Some will look more like information graphics, others more like casual games, and still others like something we haven’t even conceived of. I hope that others will join in exploring this design space to make the continuing flood of online civic data more engaging, insightful, and fun for users to interact with.

For more details on Salubrious Nation and the results of the study readers are invited to see a pre-print of the paper, which will be published at the 2011 Conference on Human Factors in Computing Systems in Vancouver, CA.


Practice Your Salsa Ear!

Last year I volunteer taught some salsa classes with my girlfriend, Kat, at the Georgia Tech Salsa Club. What we found was that one of the hardest parts of picking up salsa for many beginners is hearing the beats in the music. Of course there are other hard things about learning salsa, but at least if you can hear the beats you can start moving in synch. So, I set out the build a little interactive app, called the Salsa Beat Trainer, to help beginners train their ears to the music. The idea is pretty simple: listen to music and tap the 1 and the 5 keys when those beats occur in the music. You get some feedback for how you’re doing, and if you really need help you can turn on hints to make it easier. Try it out here.

computational journalism games journalism

Games as Informal Sources

How are people interacting in news games? What kinds of decisions are they making? And what game elements and relationships are players most interested in? These are the types of questions that an observant journalist might answer, or at least pose, if they began to think of games as informal sources of information.

In their 2004 textbook, Behind the Message: Information Strategies for Communicators, Kathleen Hansen and Nora Paul write, “Informal sources include observations about audiences, messages, and the environment in which the communicator operates, as well as networks of supervisors, colleages, clients, neighbors, and friends the communicator deals with every day.”

We would posit that news games (or other forms of interactive multimedia) could function as a valuable addition to the journalist’s toolbelt of information gathering capabilities if they were properly intrumented to gather observations of user behavior. Case in point is American Public Media’s Budget Game, a complex simulation asking users to manage the US federal budget by selecting different policy options ranging from taxation to defense and the environment. In the process of play, the game elicits a set of policy priorities from the user, leading to an understanding of the depth, complexity, and tradeoffs of remaining true to your ideological beliefs while also maintaining a realistic air. After decisions are made, the budget is simulated out to 2028¬† and you get a sense for the impacts of your decisions over time. You can also see how many other people played the same sets of decisions as you did and if you input some basic demographic data you can even start comparing your decisions to others.

The implications of the APM Budget Game as a journalistic tool, an informal sources, are interesting. On the backend, we can imagine a journalist looking at the aggregate decision data taken from players of the game and looking for trends or correlations between sets of decisions. Do 80% of players decide to roll back Bush’s tax breaks? Are those same players in a middle income tax bracket? Also, what is the ordering of the decisions made in the game? Perhaps this could lead to some insights into how players view the importance of some of the issues at stake. Interesting trend or correlation? The journalist can capitalize on it and write a follow-up story.

It’s well known that online journalism operations (e.g. the NY Times) have analytics departments that do data-mining on pages to understand both demographics and how users flow through news pages. But what about an interactives-analytics group that data-mines on the logged behavioral response to games and other interactive graphics? This type of mentality could also lead to different types of game designs since the goal would be both the user experience as well as the “exhaust”, the data that could be collected, from that user experience. How to design such an interactive experience that also produces something interesting for the reseacher / journalist?

Clearly games as informal sources are not going to replace other forms of sources for journalists. Interviews of scholars or reliance on institutional reports produce different types of insights compared to the observation of online behavior. But this could be yet another way to probe at the audience and understand what is most relevant to them.

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Functional and Cultural Tensions and Opportunities for Games in Journalism

Games and Journalism both evoke their own cultural images; the Ramen and Dorito stained gamer on one hand and the hard nosed, gum shoe journalist on the other. It’s not immediately obvious that oil and water can mix, nor am I going to argue that they should. But there are some interesting opportunities here, both for games to fill functional gaps in journalism and for games to come closer to journalism by adapting the cultural values of¬† news institutions. How can games fit into the sociology of news and journalism?

I started by reviewing “The Sociology of News” written by Michael Schudon, a sociologist at UCSD. If you haven’t read this book I would recommend it, not only for its concise definitions of terms like “news” and “journalism,” but also for its in depth description of the American culture of journalism.

Schudson claims that one of the distortions in the news that arises out of its culture is that it is “event-centered, action centered, and person-centered.” Event emphasis, for better or worse, is a characteristic I think most of us would agree predominates the news. Here’s where games can provide something more: process-oriented journalism. For instance, how does the process of the electoral college work? The news industry has often failed to provided process-oriented reporting, but games are perfectly suited to process explication. At the same time, Schudson writes, “When things are going well there seems less of a reason for a news story. The news instinct is triggered by things going badly.” Is process just boring? And if so, how can games make process more engaging for consumers? Perhaps the unusual and the “bad” news needs to be incorporated into process games.

On the other hand, perhaps what people want to know from the news isn’t process and that’s why it’s not prevalent. The brutal truth is that a majority of the useful information in the news consists of things like movie listings, restaurant reviews, weather forecasts, and local sales advertisements. This extends to “news you can use,” like reports about your health and financial investments. These are the topics and types of information that are “important” to people on a daily basis, for which they need a guide.¬† There’s a whole interesting story to tell here about the history of news and its evolution. Politics didn’t really enter into the news equation until the rise of democracies. The News was appropriated by those seeking democracy in the 18th century, and in the course of time journalism has maintained its rhetoric as the machinery that makes democracy work. But perhaps the bias that “serious” journalism needs to be about politics or public policy is unfounded and was socially constructed in a distant time. All I’m saying here is that games have a chance to go back to basics and give people what they want: the information they (really) need presented in a compelling format.

But whether or not journalists will accept games into their repertoire for telling stories is questionable. Technical and literacy issues aside, there is cultural conflict between journalism and games: News has a “prestige” aura around it. The prestige of the news organization legitimates certain forms of knowledge and amplifies stories. It provides a certification of importance. Just think back to the time when you made “the news” and were in the local paper. Somehow that paper had conferred on you an air of importance. Games may lack this prestige value because of their association with frivolity, playfulness, and general unproductivity. To break this cultural standoff would take leading news organizations accepting games into their news culture and framing them with the same aura of prestige conferred on other media.

The final point I want to make here builds off of Schudon’s observation that oftentimes a journalist’s aim in telling a story is astonishment and moral outrage rather than any deep understanding; the so called “Holy Shit!” stories that make milk come out of your nose at the breakfast table. This may also be an area where games can excel. Sure, images and videos can shock you, but what about a game that puts the player in an uncomfortable situation where their own actions shock them. I’m reminded of the PETA parody of Cooking Mama, where the player had to do all sorts of inhumane things to a Tukey in order to put Thanksgiving dinner on the table. If attention getting is part of the news culture it seems like a no-brainer that games can do this every bit as well as other media, maybe even better, albeit with perhaps more of a time investment on the part of the player.

games games for change journalism transparency

The Transparency of Mechanics

aibo x-ray.png

In Ian’s prior post on transparency and games he mentions three types of transparency: transparency of influence, transparency of construction, and transparency of reference. Cutting across these facets of transparency I’d like to add the transparency of mechanics which is particularly applicable to any consumer-facing journalistic software, of which games are one instance. To get a better understanding of (1) what the transparency of mechanics involves in journalistic software and (2) how mechanics are currently communication in software I analyzed a number of examples of serious games and info graphics including: SimCity 4, Democracy 2, Oil God, The Garbage Game, Energyville, Stop Disasters Game, The Chevron Energy Generator, Better to Buy or Rent, and’s 2008 what ifs. In this post I will mainly address the definitions and in future posts I will consider how the model of the transparency of mechanics presented here has been and can be reified in interfaces.

What I mean when I say “mechanics” is essentially the internal and external state of elements and relationships between elements of a computer program, including the values or attributes and categorizations of elements in the software with respect to their circumstances (e.g. time, place, etc.). A state within a game is the instantaneous value of all elements and relationships between elements. For example, in Sim City the state of the game at any one time slice is the set of all values (e.g. low, med, high) of all attributes (e.g. pollution, education, fire protection etc.) for all games objects (e.g. power plants, residential areas etc.) including how those objects are interacting and influencing each other at that moment.

Transparency of mechanics can be broken out into different facets including:

  • State. What are the attributes and relationships of game elements?
    • The specific WHY of state: a precise explanation for an element’s attribute.
      • This gets at the notion of what the relationships are between elements and what their valence and effect on each other is. For instance, what attributes at the current time-slice contributed to the attribute of the object of interest?
    • The general WHY of state: a generic explanation for an element’s attribute
      • What are the general attributes which affect a given attribute of interest, i.e. what are the relationships and weights to other entities? How do you know the strength and directionality of those relationships?
  • Computation of State (How). How are changes of state computed? How does probability factor into the computation? What is the method of inference or equation governing state change?
  • Explanation for State Change (Why). What was the trigger, event, or decision that affected a state change?
  • Assumptions and Limitations of the Model. How is the model grounded and where does it fail to accurately portray the phenomenon of interest?

Being fully transparent about all mechanics in a game may turn out to be a daunting and in fact unproductive enterprise. This is because of the granularity of transparency that would need to be supported to show the attributes and relationships between all game elements at all time-slices. Do users really need to know about every little state change? The answer is clearly no, but the job is then up to the journalist / programmer to make decisions about which aspects of the model should be most saliently transparent in the final presentation. Another question to ponder here is whether too much transparency in games can ruin the fun of it? And if perhaps by explicating too much you undermine the medium’s abilities to get people to comprehend models via interaction?

games games for change

Badge of Honor?

I played The Gotham Gazette Garbage Game and sent 1,897,872 tons of refuse across 698,093 miles.

annotation games

Music Recommendation & HerdIt

This week I had the chance to attend a tutorial at the ACM Conference on Multimedia on Music Recommender Systems presented by Oscar Celma. It was a very informative talk, touching on some of the foundational issues in music recommenders: relevancy, serendipity, transparency, and context. There was also some discussion of the tradeoffs between content based recommendations versus those made based on human added metadata. For instance, content based recommendations have the cross-genre problem of potentially recommending songs from a different genre which share some similar musical features. The assumption in the presentation is that this is bad, though in some sense, serendipity may call for some cross genre pollination.

I wanted to pick at a point that bothered me a bit: the tension between relevancy and serendipity. Relevancy on the one hand calls for a user centric model which takes into account how interesting a particular recommendation is for a particular user. Relevancy means that the recommendations made are in fact meaningful and perhaps “useful” or at least appreciated by the user. On the other hand the virtue of serendipity is espoused as something to strive for. The value judgement is that people shouldn’t be constrained to things they already know or are familiar with, but should also be exposed to things outside of their comfort zone. And music aside (especially in an information domain like politics) I think serendipity IS something major to strive for. But doesn’t this compete with relevancy for attention? A personalized / recommended news page that includes “serendipitous” results risks presenting results to the user that are in fact not relevant at all. I would have appreciated a more earest discussion of the tradeoffs between these factors.

There are various methods that commercial systems are using to make music recommendations. The two big ones discussed in this tutorial were and pandora. Pandora relies on an “army” of paid specialists who listen to each indexed some and rate it based on 400 attributes on a 10 point scale. This clearly cannot scale as there is simply too much labor involved in the process, but the result is impressive. Another tack on the problem is to produce these annotations using non-experts, something I’ve thought about in my design of games like PhotoPlay and AudioPuzzler. Some folks at UCSD have designed a social game for Facebook called HerdIt to try to collect affective music data in the process of people playing the game. The hope is that this data can inform machine algorithms and eventually produce better recommendation systems.