Yet more EQ2 data

 Posted by (Visited 9445 times)  Game talk  Tagged with: ,
Feb 162009
 

I have referenced the EQ2 data dump to Dmitri Williams & team before, something that I helped kick off way back when and which has been supported by SOE in an ongoing fashion. Now there’s an article at Ars Technica which describes yet more findings, apparently from a session at the American Association for the Advancement of Science.

Jaideep Srivastava is a computer scientist doing work on machine learning and data mining—in the past, he has studied shopping cart abandonment at Amazon.com, a virtual event without a real-world parallel. He spent a little time talking about the challenges of working with the Everquest II dataset, which on its own doesn’t lend itself to processing by common algorithms. For some studies, he has imported the data into a specialized database, one with a large and complex structure. Regardless of format, many one-pass, exhaustive algorithms simply choke on a dataset this large, which is forcing his group to use some incremental analysis methods or to work with subsets of the data.

They got the first data dump around when I left SOE, so that should give you an idea of how big the dataset is, that it took this long to analyze!

Some bullet points:

  • “Gender turned out to be a negative influence on interactions: even after their low numbers were taken into account, female players avoided interacting with each other.”
  • “Time zones had some influence; players in the same time zone were 1.25 times more likely to partner than players even one time zone apart.”
  • “players within 10 kilometers of each other were five times more likely to interact. Contractor concluded that, for the typical player, the game simply offered a way of continuing their real-world social interactions in a virtual setting.”
  • “The average age of players turned out to be 31.”
  • “their body mass index was better than the US average and, although they were slightly more depressed than average, they were also less anxious.”
  • “a small subset of the population—about five percent—who used the game for serious role playing and, according to Williams, “They are psychologically much worse off than the regular players.” They belong to marginalized groups, like ethnic and religious minorities and non-heterosexuals, and tended to use the game as a coping mechanism.”
  • “Older women turned out to be some of the most committed players but significantly under-reported the amount of time they spent in the game by three hours per week (men under-reported as well, but only by one hour).”

EU says games good for kids

 Posted by (Visited 9128 times)  Game talk  Tagged with: ,
Feb 122009
 

A report from the European parliament concluded yesterday that computer games are good for children and teach them essential life skills.

via Video games are good for children – EU report | Technology | The Guardian.

Saw it via a Tweet from Steven Johnson this morning. I asked him, “Do you think our books were read as part of the debate?” Or those of Jim Gee, Marc Prensky, etc… The article does say experts in games were brought in from numerous countries, so maybe.

There is discussion of the issue of stimulating violence, but the conclusion was that legislation was not warranted.

More interesting in terms of online, which is poorly regulated right now, was the notion of a mandatory way for users to report online games to PEGI:

The growing market for online games needed to be better controlled, the MEPs said, and online games should include a red button on the screen which children or parents could click to disable the game.

Manders said the button could also be linked to the administrators of the Pan-European Game Information age rating system, so that when a game player presses it, PEGI is informed and can investigate potentially disturbing games that are available through the internet.

Video game conditioning spills over into real life

 Posted by (Visited 6390 times)  Game talk  Tagged with:
Jan 282009
 

It’s not my headline — it’s from the New Scientist, which reports something that seems obvious — if you condition users to associate certain movements, colors, actions, etc, with particular emotional stimuli, all in a game, the users will react to those things that way even when seeing them in different contexts.

Volunteers who played a simple cycling game learned to favour one team’s jersey and avoid another’s. Days later, most subjects subconsciously avoided the same jersey in a real-world test.

It’s the same logic used as when people use videogames to treat post-traumatic stress. Really, I think the researcher is a little disingenuous when he says

But no-one has shown that video games can train the kind of conditioned responses that underlie much of our behaviour, Fletcher notes.

I think it most certainly had been, and on many levels. I think here of stuff like the Stanford research on how we treat short avatars, for example. But whatever. More studies is good. 🙂

Of course, this will also go into the pot with the studies associated with raised levels of aggression, and someone will try to link the two… sigh.

Researchers work on procedural fun

 Posted by (Visited 10229 times)  Game talk  Tagged with: , ,
Jan 122009
 

Developing behaviors via genetic algorithms of various sorts has been around a long time now. You come up with a basic environment and ruleset, then you let loose millions of generations of simple AIs to keep trying to surivive. You then have the AIs tweak themselves based on what survived well, attempting to evolve the best survivor.

This can be used for lots of purposes — and now it’s being applied to game design. Starting with a simple Pac-Man like environment, researchers are generating zillions of procedural games, and then testing to see which is most fun. But how to measure the fun?

It should be pretty straightforward to see how game rules can be represented to be evolved: just encode them as e.g. an array of integers, and define some sensible mutation and possibly recombination operators. (In this particular case, we use a simple generational EA without crossover.) For other rule spaces, some rules might be more like parameters, and could be represented as real numbers.

What’s the much trickier question is the fitness function. How do you evaluate the fitness of a particular set of game rules? …

Our solution is to use learnability as a predictor of fun. A good game is one that is not winnable by a novice player, but which the player can learn to play better and better over time, and eventually win; it has a smooth learning curve.

via Togelius: Automatic Game Design.

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