Game Theory based Incentive Model for Online Sharing


When you try to have the human network
converge on desire results right? and our entire axiom is that whatever you are
seeking because we’re all interconnected in our human web you know up to six
degrees everyone’s connected and in smaller communities it’s much less so
the ability of the human network to converge to a target audience and
whenever you need a business result there’s always the target audience you
want to reach so the ability of the human network to converge is there and
it’s highly optimizable so we are connected if I’m a yoga teacher and I’m
looking for my next students they’re somewhere not so far away in the network
for my existing students but then how do I properly incentivize the web to
converge efficiently right? not to spread in vain because when you spread in vain
right you can burn yourself in your human network and of course respectively
your reputation gets harmed on the 2key network you can earn less and
it’s a human resource so if you get burned you get burned it’s not like a
machine you can replace so the way for us to maintain and optimize the network
is to put a lot of thought of how to develop the right game theory algorithms
and models to incentivize online sharing in a successful manner. So our entire
approach for the 2key network is to enable a new kind of sourcing for the
web called social sourcing which is basically the incentivized activation of
result-driven online virility and handle this right
incentive meaning that when someone reaches the campaign you know how much
you should offer them for their services and then backwards how to properly compensate whoever was relevant in
achieving a conversion it’s a very challenging task this is why we’ve joined to our ranks the leading professors from academia that have researched this kind
of campaigns there actually hasn’t been so much research on it just a few
because there’s not enough data sets that have been explored and our notion
is to put forward a framework for enabling
machine learning based optimization lists so what we started is that we
started from a thorough investigation of all the research that was done in
multi-step, multi-level referral campaigns and there has been some
academic work in algorithmic game theory and along with our advisors
from the Technion the Israel Institute of Technology which are one of
the leading contributors to this research worldwide we’ve developed an
advanced incentive model that basically builds on top of all the
latest research in the world and this is just the baseline for developing a more
robust machine learning based method that would enable actually to optimize
for each and every person dynamically for various campaign types. So this is where we’re at and this is where we’re going with it.

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