Sun Dec 21, 2008
I'm in Norwich all this week attending Netsci08
http://www.ifr.ac.uk/netsci08/, the internatinal workshop and conference
on network science. It's a week long event, and broadly speaking it
looks like there are three types themes that are being discussed here:
biological networks, pure networks science and community detection in
networks, principlaly emergent networks of the kind we see in the
I'm twittering about the meeting using the tag #netsci08, but it seems
that I'm the only one out there in the twitterverse who is also at this
meeting. Not enough power in the lecture hall, and wifi is a little
ropey, but the conference is pretty good so far.
The talks on Monday were about some basics on network mathematics, and
on network science in the social sciences. I'll go back over my notes
and give a quick report on them when I get a chance to catchup, but the
discussion in the evening was pretty interesting, and the talk in the
morning touced on some very important topics.
The Tuesday morning tutorial is on economics and networks. The morning
model was very simple, and I think that's fair enough, but I got the
feeling that the level of the audience, at least on the side of the room
that I am sitting on, was high enough to have taken a bit more robust
model, so I got the feeling that there was some discomfot with the model
The after-coffee section is focussing on social influencers, now this is
How is it that information flow is highly assymetric in the world?
The model is a mmulti-state model with differeing outcomes. Individuals
don't know the true state of the system, but they have beliefs about the
states. Sounds like a hidden markov model.
The model is stationary, and we want to see how the choices we make
change the beliefs that we have. Could be a bayseian network? Let's see.
What I am hoping to see from this model is how reccomendations can
travel throgh a network. There is a network of communication between the
network. The model can integrate dynamics, the dynamics of belief.
There is also feedback between actions and beliefs. The main result is
that as time goes by new information has less effect, and so beliefs
converge in the network. This is a consequence of Martingale's theorm.
The big question is whether we get optimal actions, and the big result
is that the ability to explore the action space and find the best action
is depenant upon the structure of the network. That is really
Oh my God, someone has an OLPC machine in the audience, how cool is
Anyway, back to the talk. So this is indeed a Bayseian network. The
anti-intutive outcome from this model is that if you have to build a one
time only network that can't be changed later, then you have the best
chance of getting optimal behaviour if no one person has undue
influence, hoever I think that for online social networks there is a lot
of dunamics going on that can pull you out of local sub-optimal minima.