

Bayesian Learning in Social Networks - wallflower
http://pages.stern.nyu.edu/~ilobel/socialnetworks_revised.pdf

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samd
Can anyone translate the jargon into English? I tried to figure out what they
were talking about, but their jargon is so impenetrable even Google struggles
to provide meaning. It seemed to be something about how a Bayesian agent will
inevitably reach the "right" decision given some type of social network...

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snikolov
I took a class on networks with Ozdaglar and Acemoglu and tried
(unsuccessfully) to apply this paper to a more special case of networks whose
interconnectivity is influenced by geometry (actual physical positions).

The short story is that this is a model of "herd" behavior, where observing
enough (wrong) decisions by other before will make you choose the wrong thing
as well, even if you know better. The next person after you will do the same,
resulting in an "information cascade" of wrong decisions, even though everyone
"knows better." The paper studies theoretical conditions for whether such
information cascades happen or not.

The slightly longer story about these types of models is that you have a bunch
of agents that decide A or B one after another. It is assumed that either A or
B is "better." Each agent gets to see the decisions of some subset of the
previous agents. Each agent also has their own belief. The agent will then
make a decision based on observing the decisions of others (assuming something
about their decision making process to try to estimate what might be the true
answer).

The punchline is that even if your personal belief is biased toward the better
answer, if enough people make the wrong decision by chance, everyone after
them also will, despite knowing better, because it turns out to be optimal.

~~~
akshayubhat
So many upvotes, I find information cascade happening here.

~~~
rickmode
Perhaps due to Bayesian Magic Pixie Dust?

