This document discusses different techniques for running experiments, known as multi-armed bandits, in order to maximize the success of experiments while minimizing costs. It introduces various bandit algorithms like epsilon first, greedy bandits, Thompson bandits, and contextual bandits that can help with the exploration vs exploitation tradeoff in experimentation. The goal is to get the most value from experiments through controlled exploration while reducing regret from suboptimal choices.

1. More
Bandits
@lukasvermeer
The
World
Needs

2. we
are
all
in
the
business
of
running
experiments
we
run
experiments,
because
we
need
to
know
that
what
we
do
is
good
for
our
business
our
most
important
KPI
is
how
many
successful
experiments
we
run

3. MulC-­‐Armed
Bandits
geFng
the
most
out
of
your
experiments

4. A/B
Test
(Epsilon
First)
the
gold
standard
for
(business)
experiments

5. ExploraCon/ExploitaCon
experiments
cost
money

6. Regret
minimizing
the
cost
of
your
experiments

7. Greedy
Bandit
controlled
exploraCon

8. Thompson
Bandit
exploit
as
much
as
you
dare

9. Contextual
Bandits
far
beyond
basic
bandit
experiments

10. Start
Now
maximize
experimentaCon
–
minimize
costs

11. we
are
all
in
the
business
of
running
experiments
we
run
experiments,
because
we
need
to
know
that
what
we
do
is
good
for
our
business
our
most
important
KPI
is
how
many
successful
experiments
we
do

12. More
Bandits
@lukasvermeer
The
World
Needs