Mary Flanagan’s collaborator at Tiltfactor in the rather hush-hush Book of Jing manga project, Jonathan Jay Lee, has had a solo show in Hong Kong and been featured in Penninsula magazine as a “rising star” in the comics world.
Go Jonathan!
And… a sneak peak — Book One!
A recent article in the Washington Post discusses South Korea’s world-leading gaming culture. Considered the world’s most technologically integrated country, with high numbers of gamers and internet users, South Korea is the one to watch as far as gaming policies. Appx 95% of households have broadband access, and in July 2010 there were appx 420,00 concurrent users of the popular online game Maple Story — that is about one in every 115 South Koreans playing the same game at the same time. The e-sports movement has gained massive momentum in South Korea as well, with popular Starcraft competitions.
Meanwhile, the government is ineffectively trying to combat ‘internet addiction’ (or what game industry folks there call ‘overindulgence,’ as it is a type of behaviour, not a particular substance) and considering blocking underage gamers from logging in from midnight to 6am.
The gamers argue that there is no proof that internet use or game playing is dangerous.
What happens to game designers when they don’t know the “right” answers?
This is especially important in situations where designers need to somehow verify crowdsourcing data. What data can we obtain with the resources we have?
Well, what do we have?
1) In the case of our Metadata Games project for Archives, we have a huge collection of photographs.
2) Users who might want to interact with these photographs, and the user accounts they create.
3) The competitive relationships between players that might be fostered within our games.
4) The relationships between tags based upon how often they appear in images together.
5) Eventually, defined groups of associated images based on the tags that they share.
Sounds great, but what don’t we have?
We don’t know the right answers, the correct tags for any of the photos we’re asking people to tag. This is kind of a big deal, because games usually involve the user solving something the system already knows! We have no way of checking whether a user’s input is correct, so we’ll have to use the competitive relationships between users to prevent false entries.
How? By giving players a VETO button, allowing the crowd to moderate itself by disagreeing with the entries of their peers.
In single player situations we’ll be completely unable to check if entries are correct.
But it turns out our entries don’t even have to be correct. At least, not all of them. With the patterns that emerge from massive quantities of tags, we can quickly tell which entries are valid by keeping track of how many times they have been agreed upon and how many times they have haven’t been flagged. A picture tagged with the word “Dog” a thousand times and “cat” once is probably a dog. Ideally the tag “cat” will have been flagged as incorrect by someone…
In his work “Metadataing The Image”, Lev Manovich explains how automation helps humans manage an otherwise overwhelming amount of information:
“What is important in this paradigm –- and this applies for computer media in general – is that storage media became active. That is, the operations of searching, sorting, filtering, indexing and classifying which before were the strict domain of human intelligence, become automated. A human viewer no longer needs to go through hundreds of hours of video surveillance to locate the part where something happens – a software program can do this automatically, and much more quickly. Similarly, a human listener no longer needs to go through years of audio recordings to locate the important conversation with a particular person – software can do this quickly. It can also locate all other conversations with the same person, or other conversations where his name was mentioned, and so on.”
So this means that all we really need to do is to prompt users for semi-specific information and give players a way to flag or reinforce previously existing tags.
In order to avoid the stray “cat” tag, our users should be rewarded for entering tags that are approved of by others later on. How can we get people to 1) care about whether their tags are approved by people they know and 2) invest in contributing/editing our network of tags over a long period of time?
Well Facebook seems like a pretty solid option for us if we stay away from annoying tendencies of some FB games.
Approving / flagging tags on a given photo might be a slow process to build rewards for, but so are many popular facebook games! If we keep track of who enters what tag and reward them for user approvals of said tag later on, then we can grant experience points over time. Experience grants users levels and level progressions can be broadcast to friends on facebook as a form of social reward for playing our game over an extended period.
So we’ll need to mobilize human players to filter out their own bad tags over extended periods of time. The question now is how to promote the experience in such a way that users WANT to become a part of the information salvation process. Isn’t it more fun to be bad?
A recent article highlights Dartmouth College’s rather unusual approach to game design by basing the process in humanistic thinking. Influenced by Professor Mary Flanagan’s commitment to social change design and human values through the Values at Play project, the students enrolled in Dartmouth’s games courses bring their eclectic backgrounds to the design process to make unique games. Currently in the works at the lab: games for pressing healthcare issues, a game on sustainability and biodiversity, metadata games, and research with the Games for Learning Institute, focusing on the links between industry designers’ everyday knowledge and popular learning theories.
We at Tiltfactor send out a tribute to Canadian game inventor Chris Haney, one of the designers of Trivial Pursuit. We’d also like to those who have come before us to enrich our games, especially those at Dartmouth: Thomas Kurtz and John G. Kemeny, inventors of the BASIC programming language, invented at Dartmouth College in 1964; Richard Tait, Dartmouth Alum and inventor of Cranium; Steve Russell, one of the students behind the game Spacewar! at MIT, finished his undergraduate education at Dartmouth College in 1958; John Donahoe (a really nice guy), with Ebay and Skype; and Ernest Everett Just, a distinguished African American biologist who graduated from Dartmouth in 1907, Dr. George Stibitz, faculty and inventor of the first digital computer in 1940, and those who, in 1956, created the field of artificial intelligence research at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades!
Thanks!
(We’d love to learn about those we’re missing! email us at info@tiltfactor.org!)
catch it all tomorrow with a Tiltfactor open house 3-6pm hosted by Digital Humanities Professor Mary Flanagan and her student design team in 304 North Fairbanks, Dartmouth College;
followed by “Rebooting Our Democracy”
a public lecture by Prof. Lawrence Lessig
7:00 p.m. Thursday, May 27, 2010
Filene Auditorium, Moore Hall Dartmouth College
Lawrence Lessig is Director of the Edmond J. Safra Foundation Center for Ethics,
and Professor of Law at Harvard Law School. He is the author of Remix (2008),
Code v2 (2007), Free Culture (2004), and The Future of Ideas (2001). He has won umerous awards, including the Free Software Foundation’s Freedom Award, and was named one of Scientific American’s Top 50 Visionaries.
This event is free and open to the public.
At Tiltfactor we’re designing a suite of games that inspires users to tag photographs with “expert” data. Using their input, are building a searchable database of terms that users can explore to find the photographs they need. But how should such a database be constructed in order to be searchable? To answer this question we need to decide exactly what we will be sorting.
If you want to sort dates, design a timeline; if you want to sort names, just make an alphabetical list.
It turns out we’re sorting the entirety of human knowledge.
So how on earth do you arrange “everything” in a way that is searchable and avoid keyword-only piles of data? My initial idea was to build a “tree” which could be measured and molded and meticulously added to by expert players.
It’s not that unique of an idea:
Aristotle is said to have been the last person to know everything there was in his own time.
Knowing everything in the context of ancient Greece, Aristotle established a framework for that body of information. By doing so, he established an informational “ontology,” an answer to the question: how should one think about the world? In information science, an “ontology” is the way in which data is structured and referred to, the vocabulary with which information is discussed inside its own context. Yep.
But Aristotle’s context was 2300 years ago. Today it might seem strange to us that he divided the category for all animals into two main groups: those that have blood and those that do not. As the world’s first taxonomist, (a person who arranges meanings) Aristotle determined the nature of zoology for the next two thousand years simply by making a miniscule decision regarding where things should be divided in the animal kingdom within his particular project.
So what is our context, and how might we best design an ontology to organize it? Well, today the estimated information content of all human knowledge is 10 exobytes. That’s 1 billion gigabytes, 6000 times the storage capacity of the human brain. We have to deal with way more meanings to design a context for than Aristotle admirably dealt with. Information is also created at a much faster rate today, so we’d have to structure a database prepared to take in information in real time and instantly incorporate it.
Yet even if we were to “succeed” in representing human knowledge as it stands today, our Solomonic decisions regarding what-goes-where will seem equally as random to future generations as Aristotle’s animals-with-blood division seems to us today.
Human knowledge is a dynamic, changing thing. As time passes, our libraries and universities slowly shift from one paradigm to the next. As such, to try to design a perfect hierarchy would be to miss the point: knowledge and culture are developing systems of astounding complexity and they’re growing more complex every day. Even the way they change is changing!
So what do we do?
Well it might be interesting to try to build a taxonomy with some help from crowd sourcing. We could hand users a very basic outline (animals, plants, volcanoes etc) and let them have a go at filling it out. But this might be a tad chaotic. 
Hierarchical databases have been outdated since the 1970’s for a few reasons:
1) They only allow for entries to be listed under a single category. Thus “European History” would have to be listed under either “Europe” or “History”.
2) If you delete a single node, every node listed underneath it is deleted.
So if we we were to build relationships between all of the tags we receive, we’d have to come up with something “polyarchical,” with connections possible between every node in the structure. Thus “European History” would be connected both to “Europe” AND “History.” It might even be connected to a hundred other terms in varying ways. Why not?
Because moderating a structure like that would be unfeasible. As language and culture develop with new events happening every day, the pace of data entry would far outpace any possible moderation. The network would become unavoidably riddled with bugs, mistags and misconceptions. Wikipedia survives because a small handful of moderators volunteer to painstakingly preserve it; they believe in the cause. Because our database is hidden behind the façade of a game, the humanitarian goals of our project will not be as openly advertised and thus publicly upheld.
So what is the tree now? Maybe it was just a good idea. An ideal. We’d like to think that everything in the world makes sense in a way that is somehow navigable. Things do, but only in a given context.
There are 8 billion contexts walking around the planet every day, each changing completely with everything they do. It is time for us to scale this project back: Rauner (the Archive we are working with) needs their photos tagged so that people can use them. Any information, albeit temporary and contextually biased information, is useful.
So lets design a system we can make, moderate, and use rather than lament unattainable ideals. (This, from a humanist!)