As I delve deeper into the issue of “reputation”, how to manage, monitor, verify, port it, etc. It seems like “reputation” very often is used interchangeable with “identity” Blogs that are supposedly about reputation almost invariably devolve into a discussion of identity and how can someone verify who they are online. But reputation and identity are two very different things and are used for very different purposes and are important to different people. I think identity is important to the “seller” in a transaction, while reputation is important to the “buyer”.

What is identity anyway? Forgoing the philosophical or psychological discussion as to what identity is for this discussion I’ll define identity in the simplest terms as “who you are” in the sense of a verifiable legal identity. You have the legal right to use the rights, privileges, resources, etc. which have been granted to that particular identity.

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The web is still the wild west and because of the unregulated, anonymous, nature of the Inter

this article in Wired about one of the countless companies spriniging up to game social media sites like Digg. One of the biggest challenges Digg will face is becoming absolutely irrelevant as it’s pages are increasingly filled with artificially inflated articles

Digg has come up with some ways to combat abuse such as trying to ferret out patterns that signal abuse or setting up honeypots in order to figure out which articles are being manipulated and then banning those users who are digging that article. That’s all well and good and I’m sure there are some damn smart people at Digg working on the issue. The problem is that 1) the solutions are all reactive instead of proactive and 2) there are far more people (with a very large financial incentive) trying to figure out ways to manipulate the site, than there are people trying to prevent the abuse. So how do you combat the problem? This may come as a surprise, but I think the best defense would be to take into account the reputation of the user who diggs each story.

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Wow, did I have the rug pulled out from under me today.

If you remember the Simpson’s episode: “Bart vs. Australia”, you might remember a scene where the toilet in the American Embassy is portrayed as requiring an elaborate machine to reverse the “Autralian flow” to the familiar “American flow” (Homer proudly and tearfully sings “My Country, ‘Tis of Thee” to the toilet).

Well, I found out today while reading an article about tropical storms/hurricanes/cyclones that the Coriolis Effect, in fact, does not have any bearing on which way the toilet water goes down the drain.

This is the kind of thing that rattles me to the core (OK, not really)… but it does make me question (more so than usual) many of the scientific ‘facts’ that people rattle off without having all of, or the proper, information.

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Companies have had varying degrees of success in implementing feedback systems that allow users to transact in a “trusted environment,” with eBay arguably leading the way. However, for a feedback system to be truly transportable and have any realistic kind of meaning, the basic rating mechanisms must be compatible… if not exactly the same.

This problem presented itself to me most notably while perusing the (now defunct) Yahoo! auctions site. As a embarrassing example of the expected behavior of their feedback system, my user rating is a “-1″. I guess in 1999 sometime (I honestly don’t remember doing this) I bought something on their auction site and never coughed up the cash… resulting in the seller rightfully leaving me negative feedback for the transaction. I had no other transactions through that site. So, the logic there is pretty clear: 0 positive feedback minus 1 negative feedback leaves me with a paltry -1 rating. Safe to say that, if I could and were to continue to transact the Yahoo! site, potential buyers and sellers should take notice of my rating and proceed with caution until I prove otherwise.

A clear example of how the Yahoo! system fails can be seen if you take a look at the following user rating here. In this example, while the user has exemplary feedback, the numbers simply make no sense. 38 positive feedback details from 27 unique users, no negative feedback, and an overall rating of 93209886?!? Now, anyone who has written low level code knows that this could be caused by a overrun of the integer containing the rating, but the math shouldn’t be that hard! 27 - 0 = 27!!! This is an extreme example, but I found these kind of inconsistencies all over the Yahoo! site. I found sellers with all negative feedback details and yet a miraculous positive overall rating. Something is certainly amiss and there is a proclivity for unscrupulous sellers to “game” the system.

The question becomes: how do we normalize these disparate rating systems so that the number makes sense both in and out of context? First, we need to have a fundamental understanding of what the underpinning algorithms for each site are, so obvious (eBay) others not so (Yahoo!). Second, there is a missing link in the chain that I think needs to be applied in order to “normalize” the reputation. A particular piece of feedback is only as valuable (information wise) as the person who left it is credible. If you have a stellar eBay rating minus one impossible to please buyer, then that one negative piece of feedback should have less of an impact on your reputation. Assuming there is truly a free market for reputation information, the impossible to please buyer should have a lower feedback rating based on their aggregate dealings with other seller. So, a true feedback score can only be generated in the context of who left each individual piece of feedback.

Over the top? Maybe. But it is your reputation at stake! Just as I breakdown and discount most of the information contained in the rantings of a loud and boisterous blogger such as Guy Kawasaki (no offense meant to Mr. Kawasaki), information of any kind can recognize its full value only in the face of complete transparency, which includes its source and their source(s), etc.. The 80-20 rule probably applies in this situation and helps us contain the work involved in generating the informational value of a piece of data, meaning that 80% of the time, one level of abstraction from the source should be enough to determine how credible the information is. It is the other 20% that present the real challenge.

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The upcoming release of NEPOMUK (Networked Environment for Personalized, Ontology-based Management of Unified Knowledge) for the KDE Linux desktop will help users organize the masses of information stored on their hard drives and perform intelligent, semantic searches. From the article:

“For example, a Semantic Desktop will store the relationship between a file saved on the computer and the e-mail it originates from.”

This is, in my opinion, a first and very important step in the right direction of the goal of an intelligent, semantic network. We are all getting overloaded with too much siloed data and it quickly loses context, and as such, much of its informational value. As desktop users everywhere (I imagine the boys in Redmond can’t be too far behind) get used to this kind of convenience, they will begin to demand it on a grander scale.

Transparency is the ultimate organizational tool. It gives us the ability to quickly make more accurate relationships and assess data in context allowing us to make more informed decisions. There are, of course, issues with assuring that the system can’t be “gamed”, where a spammer can somehow link a nice picture of your grandmother to an anarchist’s militant porn website… you get the idea.

This puts more responsibility upon the individual to manage their own data relationships. Or at least implement an intelligent system to do it on their behalf. In a way, these systems will be the next generation of virus scanners, but will actively accumulate knowledge as they go… hopefully with minimal human intervention.

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