I have also successfully moved into a new house and purchased a new car with my wife for her to use.  She also got a job as an assistant professor at Northwestern University in the Communication Studies department.

Now that we are approaching the new school year, and I’ve got all this craziness behind me, I hope to be blogging more regularly about things I’m thinking about.  And I’m sure the new academic year will bring lots to think about: I’m teaching a class on the intersection of Computing and Journalism, and I’m running a seminar series on Social Media.  In the meantime, I’ve been traveling a lot, and spending that much time in my car has caused me to think about traffic more than I should.  So in the next couple of posts, I’m going to take a break from talking about social media and talk about how some of the ideas I work with can be applied to improve traffic.

Threatening to exclude users can serve as a strong motivation for users to contribute information.  The simplest way to do this is with a minimum threshold: any user who does not contribute the minimum amount of information to the system will be excluded from accessing the system.  Technically, this is a very simple mechanism to implement; however, it is not clear how users will react to such a threat.

Jeff MacKie-Mason and I constructed a mathematical model of behavior to try to predict what will happen when a system uses a minimum threshold to try to induce greater contributions.  It turns out that the behavior is not straightforward; some users will increase their contributions to the minimum in order to continue receiving access.  Other users will decide that its not worth it anymore and stop using the system.  It is actually the users with the lowest marginal net benefit that will stop using the system.  These are the users who get the least benefit out of the first piece of information they contribute.    Users with more moderate marginal net benefits are the ones that will increase their contributions to meet the threshold.

Overall, what the minimum threshold does is produce greater equality of contributions.  Without the threshold, there is a wide variance in how much information each person contributes.  The threshold pushes everyone to contribute a similar amount as everyone else, reducing the variance in contributions.

We also use this model to provide concrete design advice.   When does it make sense to use a mechanism like this?  This mechanism works best when there are a lot of users in the system.  This is because the minimum threshold acts kind of like a coordinating device; many users all coordinate and contribute that minimum.  More users in the system create a larger multiplier on the minimum threshold, leading to a much larger amount of information for everyone.   It also works best when, without the threshold, very little information is voluntarily contributed.  A system where lots of information is already contributed doesn’t need more users contributing, and runs the risk of unnecessarily hurting people by forcing them to contribute more information than they would prefer to.

Rick Wash and Jeffrey MacKie-Mason. “Using a Minimum Threshold to Motivate Contributions to Social Computing” 2009

However, making this work isn’t quite as easy as it sounds.  You need to make sure that the information users contribute is actually useful for other people. Take del.icio.us, again, as an example.  Users contribute “tags” or keywords by associating them with specific bookmarks.  I bookmark the NYTimes website, and then I can associate tags like “news” and “newspaper” and “reporting” with it.  But these words aren’t really that useful to other people.  If I search on “newspaper”, I get almost 150,000 hits, and the whole first page is news stories about newspapers and not newspapers themselves.   The tag “news” is even worse, it has over 2.5 million hits, most of which are links to actual news stories. These tags are a really bad way to find news sites.

The reason these tags don’t work can be explained by looking at how these tags are contributed in the first place.  Users choose tags to help themselves organize their own bookmarks.   When I am bookmarking a website, I try to come up with words that help me organize my bookmarks; but I don’t care about anyone else’s bookmarks or tags.  This means that when I chose to use the word “newspaper” for the NYTimes, I didn’t care if I was using it in the same way as other people, or if my using it added clutter for other people who are using that tag.  I just wanted to be able to re-find my favoriate newspaper in the future.  In short, I have no reason to choose tags in a way that helps other people.

Bookmarks, on the other hand, are different.  When I choose to create a bookmark on del.icio.us, I do think about clutter problems.   I try not to bookmark too many websites because each new bookmark makes it harder for me to my old ones.   If I bookmark too much, things get cluttered and I can’t find anything.   Tags help, but only if I use them consistently and sparingly.  But this means that if you share an interest with me, you can go to my bookmarks and they will be useful to you.  Right now, most of my bookmarks recently have been about social computing research, so if you are interested in that, head over to my bookmarks and you’ll find a relatively clutter-free set of links.

Bookmarks on del.icio.us are an example of incentive alignment.  Del.icio.us induces me to contribute information (bookmarks) in a way that the resulting contributes are actually useful to others.  Looking at any single person’s bookmarks is usually a good way of finding links to websites on topics related to that person’s interest. Each user tries to limit the clutter in his or her own bookmarks, and that helps other users make use of those bookmarks. Tags on del.icio.us represent an incentive mis-alignment.  The structure of del.icio.us gets me to contribute tags, but the tags that I contribute are almost useless to other users because I don’t take into account the additional clutter my use causes for other users.

When designing a social computing system, giving users a private, individual reason to contribute can be a powerful motivator.   And making those contributions public as a side effect can provide lots of value to the other users on the system.  But, when doing this, be careful to make sure that the incentives are aligned; that users contribute information in a form that is actually useful to others.  Make sure that concerns like clutter that are important to the users of the information are considered by the contributors when making their contributions.

Rick Wash and Emilee Rader. “Public Bookmarks and Private Benefits: An Analysis of Incentives in Social Computing.” In American Society for Information Science and Technology (ASIS&T) Annual Meeting (2007).

Del.icio.us has a great way to deal with this problem.  They sidestep the whole “strategic” problem by giving people a mostly personal reason to bookmark.  Users on del.icio.us choose their bookmarks primarily because they think that they might go back and visit the website sometime in the future.   And tags are chosen mainly to support this potential re-finding activity; users tags are chosen to help themselves organize their collection of bookmarks.   Users create these bookmarks for personal reasons, and would continue to do so even if no one else ever saw them.

However, del.icio.us makes all bookmarks public by default.  Anyone can go and view my bookmarks or anyone else’s bookmarks.   This public benefit is a side effect of the private incentives that users have to create bookmarks.  The main purpose of creating bookmarks isn’t to benefit others, but this benefit happens because of the default-to-public nature of the site.

This suggests a more general mechanism for inducing user contributions.   Social computing systems can provide a strictly private benefit from contributions — potentially by emulating something that users already do on their computers like store bookmarks — and then make these contributions public by default.  Making things publicly available as a side-effect of normal, personal use is a great way encourage contribution to a social computing system.

Rick Wash and Emilee Rader. “Public Bookmarks and Private Benefits: An Analysis of Incentives in Social Computing.” In American Society for Information Science and Technology (ASIS&T) Annual Meeting (2007).