I see the drive to acquire as explicitly not to do with “acquiring” knowledge or friendship, since they are covered by separate drivers. So I took this one to mean material things. And recommendations are useful in that process.

Anyway, to comment on this posting I need to comment on two things – (i)how well does the “Four Drives” model apply to syndication and (ii)your conclusions from applying that model. Now, any model is a simplification of reality to allow the user to make predictions and decisions. According to Einstein’s maxim, a model should be “as simple as possible, but no simpler”. As simple as possible, because that makes the model easy to understand and use. No simpler, because if the model is too simple it ceases to reflect reality and so becomes useless. The simplifications inherent in a model apply to the subset of reality that it is used to model.

So does the “Four drives” model apply to syndication? My gut feeling is that the model is a little too simple. It does not recognise avoidance drives (for example the drive to avoid danger (fear) and the drive to avoid boredom). It also does not recognise the drive for status. The drive for status should not be underestimated – it is significant in social networking sites and feeds, where there is kudos associated with the number of friends/links/followers you have. There is even status associated with being an early adopter of something that proved popular. (Even your blog has a “Nominate me for a CIO/IT Director award” link.)

(Later) I’ve done some further thinking on the “Four Drives” model and I’ve decided that I don’t believe there is a “Drive to acquire”. From an evolutionary perspective it’s difficult to see what advantage could have been gained by evolving such a drive. I don’t deny that there is a “Desire to acquire”, but I think this is a manifestation of other needs not being satisfied, in particular the needs for bonding and status.

But that fact that a model is too simple does not mean it cannot be used, it just means that care should be taken in interpreting conclusions from applying the model.

Now to my thoughts on using the model to classify subscriptions. My first thought is that it omits (what is too me) an important category – that is alerts to do with an immediate task at hand (that is “my plane is late” etc).

“Drive to defend”. What I called “Class A alerts” in a previous posting to this blog. Among the most useful of alerts, and normally infrequent.

“Drive to bond”. I don’t find web-based communications very useful for “bonding”. I don’t find them a very good way of getting to know people better, or getting closer to people. They have a purpose in maintaining existing bonds though.

“Drive to learn”. Well, “your mileage may vary”, but the signal to noise ratio, in my opinion, means alerts are at best an in efficient means of finding new avenues to explore.

“Drive to acquire”. I don’t understand your thinking here. For example, you seem to classify a recommendation as an acquisition. Can you further elucidate?

Also, you write ” What happens when they share more than just the event, when they share their views on this thing they’re doing. They like the book, they hate the restaurant, they’re intrigued by the film. That sort of thing.
So that’s where I’m at. The point I’m making is that this information cannot be aggregated.”

I think the problem lies on the fact that aggregators/filters are not sufficiently powerful.

I agree that a zero to five star rank on a book at Amazon is not a true reflection of reality and looses important context information. But that’s only because Amazon hasn’t been able (or I think bothered?!?) to make a better ranking system public.
Collective intelligence applied to social data is already out there. And I am sure it’ll soon become better and more widespread, with software capable of extracting more structured information tailored to the user (using supervised algorithms, maybe?), so that you’ll still be able to follow Paul’s twitters, but – if you wish – you’ll be able to subscribe to alerts containing accurate (to you) information extracted from the twits sent during the last hour by all Pauls in Twitter, so to speak.

I can say “these are my top n”….provided the other (100-n) are available as well. There are many ways to show the top n. Top n according to me. Top n according to comments. Top n according to visits. But in a long tail world, what is important is that we don’t revert to a hit culture and only show the top anything according to some narrow definition of top.