netsci08 blogging

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

internet.

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

presented.

The after-coffee section is focussing on social influencers, now this is

interesting.

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

interesting.

Oh my God, someone has an OLPC machine in the audience, how cool is

that!!

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.

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