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https://archive.devcon.org/archive/watch/6/cost-of-feudalism-towards-a-theory-of-mev/
Maximal Extractable Value (MEV) is excess value captured by miners/validator. This excess value often comes from reordering, censoring, or inserting new transactions that allow a miner to front-run users' transactions. Is MEV *always* bad? Can it sometimes lead to good equilibria for users? We modify tools from algorithmic game theory and probability to prove some surprising paradoxes — *some* MEV improves trading efficiency in networks of automated market makers.
Speaker(s): Tarun Chitra, Guillermo Angeris
Skill level: Advanced
Track: Cryptoeconomics
Keywords: MEV,algorithmic game theory
Follow us: https://twitter.com/efdevcon, https://twitter.com/ethereum
Learn more about devcon: https://www.devcon.org/
Learn more about ethereum: https://ethereum.org/
Devcon is the Ethereum conference for developers, researchers, thinkers, and makers.
Devcon 6 was held in Bogotá, Colombia on Oct 11 - 14, 2022.
Devcon is organized and presented by the Ethereum Foundation, with the support of our sponsors. To find out more, please visit https://ethereum.foundation/
A
A
You
know
I
probably
don't
need
to
talk
about
what
Mev
is
as
much
given
that
there
were
if
I,
remember,
right,
17
or
18
talks
about
Mev,
but
you
know
I
think
in
the
spirit
of
trying
to
understand
the
incentives
hidden
in
a
lot
of
these
these
systems.
One
question
you
might
ask
is:
does
there
exist
sort
of
some
type
of
formal
theory
for
Mev?
Does
there
exist
some
type
of
mathematical
structure?
A
To
this
thing
that
seems
to
be,
you
know,
Captivate
people's
imaginations,
yet
no
one
can
describe
it
and
so
I'm
going
to
talk
a
little
bit
about
different
ways
of
addressing
this
problem,
including
some
work.
We've
done
and
I
I
like
to
call
any
analysis
of
Mev
sort
of
measuring
the
cost
of
feudalism.
So
that's
that's
sort
of
where
the
title
comes
from.
A
Let's
start
with
the
operative
definition
of
Mev,
you
know
when
you
see
it
like
the
of
course,
the
famous
Supreme
Court
ruling
on
porn-
and
you
know,
Mev-
comes
in
many
shapes
and
sizes.
You
know,
of
course,
we
have
everything
from
Sandwich
attacks,
liquidations,
Arbitrage,
nft,
front
running
and
cross
chain
and,
as
you
can
see,
there's
it's
sort
of
quite
a
variable
form
of
revenue
for
for
validators
and
Searchers,
and
somehow
we
because
we
know
there
are
many
examples
and
we
feel
like
we
can't
enumerate
all
of
them.
A
It
might
feel
like
the
space
of
all
mevs
super
big.
So
how
do
you
analyze
it?
You
know
I
know.
There
were
two
talks
yesterday
about
what
is
and
isn't
Mev
but
I
think
we're
going
to
focus
on
kind
of
an
operative
definition
that
can
be
used
and
we
could,
we
could
sort
of
say
Mev
is
any
excess
value
that
can
be
captured
by
validators
by
reordering
transactions,
strategically,
adding
or
removing
transactions.
A
Part
of
the
reason
is
the
blockchain
was
just
getting
spammed
by
people
doing
liquidations
and
back
running
oracles
and
instead
of
normal
users
being
able
to
get
their
transactions
in
you'd.
Have
the
you
know
entire
blocks
filled
with
people's.
You
know
spam
background
transactions.
On
the
other
hand,
of
course,
as
the
previous
talk
was
focused
on,
there's
a
lot
of
centralization
risk
with
off-chain
auction.
So,
let's
you
know
try
to
maybe
schematically
look
at.
You
know
what
how
Mev
works.
We
have
users
who
submit
transactions
which
are
the
Deltas.
A
We
have
a
mempool,
the
Searcher
has
sort
of
their
own
view
of
the
mempool,
and
maybe
they
have
some
transactions
that
are
directly
given
to
them,
which
you
know
people
would
call
Private
order
flow
and
then
the
Searcher
sends
their
bundle
which
they're
willing
to
pay
for
to
the
validator
and
the
validator
added
to
the
block.
The
philosophical
question
here,
though,
is:
how
do
we
know
value
captured
by
Mev
is
excessive
or
in
a
notion
of
excess.
You
know
in
this
sort
of
first
definition,
I
said
any
excess
value.
A
Well,
how
excessive
is
excess
describing
value?
Flows,
In
Mev
is
actually
quite
difficult,
so
in
in
algorithmic
Game
Theory,
you
usually
have
two
main
ways
of
thinking
about
the
world.
One
is
optimizing.
Social
welfare,
social
welfare
means.
If
everyone
has
some
notion
of
utility
some
notion
of
how
happy
they
are
with
the
world.
You
want
to
maximize
the
sum
of
everyone's
utilities.
On
the
other
hand,
revenue
is
you
know
the
the
the
person
selling
the
good
you
know
maximizing
how
much
they
earn
and
and
then
in
cryptocurrency.
A
It's
actually
interesting
that
you
have
to
optimize
both
jointly.
So
obviously,
user
welfare
is
important
for
the
success
of
any
network.
I.
Don't
need
to
really
convince
anyone
of
that,
but
validator
Revenue,
being
optimized
is
actually
important
for
Economic
Security.
If
validators
are
not
making
sufficient
income
from
it,
then
they're
unable
to
to
Really
secure
the
network
and
in
decentralized
networks
versus
centralized
networks.
A
These
are
two
competing
goals
and
I
think
the
one
reason
there's
a
broad
view
of
how
Theory
you
know:
algorithmic
Game,
Theory,
it's
different
for
crypto-
is
that
you
have
to
sort
of
balance
these
two
goals.
Instead
of
trying
to
optimize
one
of
them,
so
what
pieces
are
sort
of
missing?
So
how
would
we
kind
of
describe
welfare
as
how
would
we
describe
Revenue?
A
A
The
user's
utility
for
minting
in
nft
is
extremely
different
for
a
user's
utility
for
borrowing
against
some
collateral,
and
inevitably
that
means
that
the
allocation
mechanism
should
somehow
be
tied
to
that.
Okay.
So,
let's,
let's
try
to
do
kind
of
like
the
eli5.
Simple
version
of
those
which
is
you
know,
we
have
a
notion
of
block
space
which
is
sort
of
a
notion
of
Max
number
of
transactions
or
slots
or
size
or
gas.
A
You
can
choose
a
unit,
but
for
Simplicity,
just
assume
they're
sort
of
fixed
number
of
units,
there's
sort
of
a
notion
of
a
binary
allocation
to
a
user
of
whether
a
particular
user
gets
a
particular
slot.
The
users
have
the
utility
functions,
I'm
unspecifying
the
domain
and
range
because,
of
course,
it's
actually
quite
complicated
in
a
world
of
cross-chain
bridging,
and
we
see
here
that
social
welfare
is
defined
as
sort
of
the
expected
total
utility
of
all
the
users
given
an
allocation.
A
So
these
X
variables
are
effectively
whether
that
slot
is
given
to
a
certain
person.
Then
this
is
sort
of
measuring
their
sort
of
net
happiness.
Let's
say:
Revenue,
however,
is
just
the
expected
value
of
the
payments
and
notice.
These
are
quite
different
in,
in
particular,
the
social
welfare
includes
the
utility
of
the
auctioneer,
which
means
that
all
of
the
payments
that
the
the
users
send
are
subtracted
or
sorry
our
negative
terms
for
the
bidders
and
it's
a
positive
term
for
the
auctioneer.
So
the
payments
cancel
out
in
social
welfare.
A
So
you
have
no
dependence
on
payments
explicitly,
whereas
in
revenue
of
course,
you
you
depend
on
payments,
so
you
know
if
we
wanted
to
try
to
formulate
what
is
an
economic
equilibria
for
the
system.
You
might
say:
okay.
Well,
let's
try
to
find
joint
up.
You
know
Maxima
of
the
social
welfare
and
revenue
functions.
A
But,
like
you
know,
the
auctioneer
is
pretty
pissed
aka,
the
validator
slash
minors,
and
this
happens
all
the
time
in
mud,
the
the
idea
that
the
optimal
revenue
equilibria
and
the
optimal
social
welfare
equilibria
are
sort
of
disjoint.
So
you
know
the
the
name
cost
of
fee
or
cost
of
feudalism
is
really
a
pun
on
the
concept
of
the
price
of
Anarchy,
which
is
sort
of
a
common
Concept
in
algorithmic
gain
Theory.
Roughly
speaking,
you
can
think
of
the
price
of
Anarchy,
as
if
I
take.
If
I
have
a
set
of
many
equilibria.
A
I
want
to
look
at
the
ratio
of
the
worst
case,
behavior
of
an
equilibria
to
the
best
case,
Behavior,
it's
an
approximation
ratio,
and
so
what
this,
for
instance,
this
formula
says,
is
what's
the
worst
case
that
the
equilibria
the
payments
earned
by
the
validator
deviate
from
the
payments,
earned
at
optimal
welfare
and
what's
the
best
case,
and
what's
the
ratio
of
those-
and
you
could
basically
think
of
this
as
if
this
is
really
large
or
depends
on
the
number
of
transactions
or
users.
That's
really
bad,
and
so
you
know,
tongue-in-cheek.
A
You
know,
of
course,
nothing
in
real
life
is
asymptotic,
but
you
know
I
I,
Feel,
Like
These
are
good
heuristics,
which
is
constant.
Price
of
Anarchy
is
really
good.
It
means
that,
even
if
you
get
to
a
shitty
equilibria,
it's
not
that
much
worse.
It's
not
that
much
worse
than
the
best
equilibria
if
the
price
of
Anarchy
is
is
weekly
growing,
so
little
o
of
n.
So,
like
maybe
it's
logarithmic
in
the
number
of
users
or
slots,
maybe
it's
square
root
of
the
number
of
user
slots.
That's
okay!
A
It's
you
can
deal
with
that
and
if
the
price
of
Anarchy
is
linear
in
the
number
of
users,
that's
horrible,
that
that
basically
means
that
the
more
users
join
the
system.
The
worse
the
equilibria
is
for
everyone,
which
is
like
the
opposite
of
a
network
effect.
It's
like
the
anti-network
effect
right.
A
You
know
both
meme
ability
and
inability
for
anyone
to
write
down
equations,
for
it
is
because
it's
so
dependent
on
the
application,
which
is
something
I
think
you
know
the
early
designers
of
cryptocurrency
networks
did
not.
You
know
Divine
at
that
time.
So
let's
go
through
an
example,
a
stylized
example
of
kind
of
what
this
looks
like.
How
would
you
analyze
this
for
amms
there's,
a
very
famous
Paradox
in
algorithmic,
Game,
Theory,
called
braces,
Paradox
and
braces
Paradox
is
really
about
how
traffic
flows
on
a
network.
A
So
we
have
this
network
on
the
left
and
we
have
a
source
City
s.
We
have
a
destination
city
d
and
imagine
that
there's
a
bunch
of
traffic
that
needs
to
go
from
source
to
destination.
Each
Edge
that
it
takes
is
a
latency,
and
so,
when
you
see
a
latency
of
one,
that's
sort
of
like
a
a
link
that
can
take
arbitrary
capacity.
It
takes
the
same
amount
of
time
for
everyone
to
to
cross
that
link.
A
A
If
100
cars
all
go
down
the
route
X,
then
it's
proportional
to
the
number
of
cars
that
are
going
on
there,
and
so
what
you
can
do
is
you
can
say
Hey
what,
if
everyone
is
selfish
or
greedy
they're,
not
cooperating,
they
don't
have
Google
Maps,
so
Google
Maps
effectively
gets
around
this
because
it
it
sort
of
implicitly
coordinates
people,
whether
you,
you
know
you're
thinking
about
it
or
not,
but
assume
everyone's
selfish,
and
they
they
know
this
map.
How
would
they?
How
would
they
choose?
A
Which
path
to
take
and
the
the
thing
you
can
show
is
basically
it
they
would
split
themselves
up.
50
50.
the
traffic
would
go
half
on
the
upper
path,
half
on
the
lower
path,
and
so
the
the
time
it
would
take
is
three
half
you
know.
We
choose
some
units
such
that
X's
from
zero
to
one.
On
the
other
hand,
you
might
think
that
adding
more
roads,
always
you
know,
decreases
traffic,
and
this
is
not
true.
A
Well
because
they're
not
coordinating,
so
you
might
say,
okay
great,
what
does
traffic
have
to
do
with
crypto?
Well,
you
know
first
before
we
even
get
to
that,
you
may
think.
Med
is
always
bad.
You
know
there's
always
these
articles,
that
people
are
writing
that
are
like
front
running.
Is
theft,
it's
bribery.
It's
you
know
whatever
you
will
pick
your
favorite
negatively,
connotated
weasel
weasel
word
and
you
might
just
say:
okay
like
yeah
it
we
we
need
to
get
rid
of
it
at
all
costs
with
that.
A
Instead
of
thinking
hey,
maybe
maybe
it's
just
endemic
to
such
systems.
We
we
saw
abrasive
Paradox
and
what
we're
going
to
see
is
we're
going
to
see
that
for
some
examples
of
applications
in
crypto,
you
can
get
around
braces
Paradox
by
having
nav,
which
sort
of
says
Mev
has
some
positive
externality
in
some
situations,
which
is
you
know,
the
opposite
of
front
running?
Is
theft
as
a
service
or
whatever
and
again
not
all
applications
are
the
same
so
that
that's
where
the
the
conundrum
in
defining
your
sort
of
optimization
problem
shows
up.
A
So
if
I
want
to
trade
token,
a
for
token
B,
but
there's
no
pool
directly
from
A
to
B
or
there's
not
enough
liquidity,
you
know
I
go
to
one
inch,
matcha
gem
I'm,
including
gem
from
the
you
know
you
could
argue:
nfts
have
the
same
properties
and
you
know
routing
is
actually
quite
complicated.
So
you
know
algorithmic
game
theory
is
studied.
Routing.
In
fact,
braces
Paradox
is
a
great
example
of
of
how
how
to
study
routing-
and
one
question
you
might
ask-
is
hey:
does
this
apply
to
amms?
A
Does
this
apply
to
one
inch
to
supply
the
mantra
and
the
interesting
thing
that
you'll
see
is
now
imagine
in
this
prior
example.
Instead
of
teleporting
in
the
middle
link,
we
actually
add
a
small
amount
of
congestion
Epsilon
times
the
traffic.
Now
you
can
show
that
equilibria,
basically
approaches
something
as
Epsilon
gets
really
large.
You
go
back
to
the
condition
where
everyone
routes
themselves
across
the
two
paths
and
when
Epsilon
is
really
small.
You
get
back
to
the
the
purely
selfish
thing
and
the
idea
is
the
Mev.
A
Searchers
are
actually
adding
this
extra
Epsilon
so
that
people
route
themselves
across
the
graph
more
efficiently
and
that's
something.
That's
a
weird
positive
externality
that
if
you
write
out
the
math,
you
know
I
I
try
to
not
add
a
lot
of
the
equations
to
this
talk.
But
if
you
write
out
the
math,
it's
actually
quite
compelling
that
the
social
welfare
somehow
has
improved
by
Mev
in
some
scenarios
right.
That's
not
the
narrative.
You
hear
right.
The
narrative
you
hear
is
like
oh
I'm,
just
grannies
are
getting
robbed
in
metamask
and
that's
just
not
true.
A
Always
so
I
like
I,
said
I'm
not
gonna,
go
through
how
you
show
such
a
thing,
but
I'll
at
least
give
you
an
idea
of
like
what
such
the
theorem
statement
looks
like
for
such
thing,
and
the
idea
is,
is
we
we
showed
that
even
when
they're
sandwichers,
the
price
of
Anarchy
is
constant,
remember
price
of
Anarchy,
constant,
that's
good!
That's
very
surprising!
You
would
think
that
hey
as
more
users
come
to
network
sandwich,
attackers
make
more
of
a
profit.
A
They
cause
more
of
a
loss,
but
in
some
liquidity
conditions
they
do
not,
and
this
this
sort
of
bottom
chart
shows
you
the
expected
profit
on
the
y-axis,
and
it
shows
you
sort
of
the
amount
of
slippage
in
your
by
the
the
user
and
you'll
see
that
it
has
a
Maxima.
It's
not
like
it
keeps
growing,
and
that
that's
the
thing
that's
actually
quite
interesting
and
nice
about
this
example.
A
So
you've
listened
to
me.
Try
to
convince
you
that
mev's
not
bad.
It
can
have
these
positive
externalities.
Maybe
there's
something
really
interesting
about
it,
and
maybe
you
can
direct
it
into
being
used
for
some
particular
applications
that
are
just
net
net
beneficial
to
your
network.
So
one
question
is:
what
do
you
do
to
to
harness
it
for
good?
A
So
one
thing
I
think
that's
interesting,
and-
and
this
goes
a
little
bit
to
Alex's
talk
before
is
suppose
you
did
have
a
mechanism
where
it
was
just
extremely
expensive
for
people
to
bribe
a
validator
off
chain
and
in
fact
people
were
willing
to
basically
share
their
Mev
Revenue.
Imagine
like
a
mining
pool
for
Mev
Revenue
now
the
reason
that
that's
very
hard
to
design
is
it's
very
hard
to
make.
A
But
suppose
you
have
such
a
mechanism,
then
you
can
redistribute
Mev
to
all
the
validators
in
the
system
and
one
very
interesting
thing
is:
you
can
show
in
some
some
sort
of
different
types
of
for
different
types
of
Mev.
You
actually
can
lower
the
overall
inflation
rate
of
the
network
if
the
Mev
subsidy
is
large
enough.
So
the
x-axis
on
this
plot
is
the
percentage
of
Mev
Revenue
shared
and
the
y-axis
sort
of
is
a
measure
of
how
much
people
are
adjusting
their
stake.
A
So,
let's
say
there's
sort
of
opportunities
outside
of
staking
that
people
can
earn
yield
on
their
assets.
With
one
question
you
might
ask
is
like
what
what
are
the
incentives
that
keep
people
staking,
and
the
interesting
thing
is
that
Mev
actually
adds
this
feedback
loop,
such
that
you
can
lower
your
inflation
but
also
ensure
that
people
stay
staked?
A
And
so
you
know
the
y-axis
is
sort
of
a
notion
of
percentage
of
how
much
is
of
the
network
is
staked,
and
you
can
see
that
at
around
25
to
50
in
this
stylus
model
of
Revenue
sharing,
you
actually
get
people
to
continue
staking,
which
is
just
you
know.
It's
just
kind
of
surprising
right,
like
everyone's
always
like.
Oh
we've,
always
needed
kind
of
hyperinflationary
economics
and
there's
lots
of
reasons
for
that.
But
but
but
Mev
distribution
also
allows
you
to
avoid
having
to
do
that.
A
A
Another
interesting
thing
and
I
think
this
is
the
type
of
thing
that
we're
seeing
in
multiple
ecosystems,
whether
it's
ethereum,
especially
with
roll-up
auctions,
whether
it's
in
Cosmos
but
everyone
sort
of,
is
starting
to
realize
the
Mev
auction
of
having
a
single
Mev
auction
for
everyone
to
kind
of
compete
in
may
not
be
the
best
form
of
an
auction.
You
may
want
to
have
an
auction
per
roll
up.
You
may
want
to
have
an
auction
per
application,
and
so
that
you
can
tune
the
aux.
You
can
tune.
A
The
social
welfare
versus
Revenue
trade-off
right
now,
if
you
think
about
it,
everyone
who's,
making
a
flashbulance
bundle
for
an
nft
mint,
is
competing
with
everyone,
who's
sandwiching
for
the
same
sort
of
block
space,
but
they
may.
Actually,
if
you
segment
to
the
block
space
and
say
only
this
amount
goes
for
sandwiching
in
this
amount
goes
for
nfts.
You
might
have
a
very
different
equilibria
and
I
think
you
know.
A
lot
of
the
roll-up
developers
are
are
basically
kind
of
talking
with
us,
but
an
interesting
thing
is
and
some
some
current
work
is.
A
As
this
like
emergent
unstudied
phenomena,
we
sort
of
backed
our
way
into
some
mechanisms
for,
like
you
know,
doing
an
auction
coming
up
with
sort
of
rough
reputation,
scores,
which
you
know,
it's
not
totally
clear,
exactly
how
that
whole
system
can
be
analyzed
to
a
point
where
we
actually
are
really
able
to
start
thinking
about
okay.
How
do
we
optimize
for
things
like
redistribution?
How
do
we
optimize
for
social
welfare?
A
A
The
first
thing
is:
what's
a
notion
of
optimality
for
an
auction
right
right
now,
if
you
look
at
flashbots,
it's
sort
of
a
first
price
auction
with
some
filtering,
because
they
use
a
scoring
function
that
removes
some
public
mempool
transactions.
Is
that
optimal?
Does
that
work?
When
you
have
multiple
chains
or
multiple,
this
sort
of
hierarchical
system
also,
does
it
does
it
sort
of
deal
with
some
of
the
problems
of
hey?
How
do
I
trust
the
auctioneer
right
now?
A
On
the
other
hand,
there's
sort
of
some
notion
of
lower
bounds,
like
in
an
open
permissionless
system,
with
a
some
notion
of
value
there
sort
of
has
to
be
some
lower
bound
to
the
amount
that's
extracted
because
within
without
any
extraction,
it's
very
hard
to
imagine
validators
being
able
to
to
continue
to
sustain
the
network,
and
so
one
question
is:
how
do
you
construct
those
lower
bounds?
You
know
in
information
Theory
and
in
theoretical
computer
science.
The
lower
bounds
are
extremely
hard.
Upper
bounds
are
always
easy.
A
Lower
bounds
are
are
really
sort
of
some
of
the
hardest
kind
of
things
to
show
and
I
think
the
Mev
space
of
lower
balances
is
still
quite
open.
I
think
you
know
in
in
spite
of
kind
of
there
being
some
a
camp
of
some
people
who
believe
with
sort
of
fair
ordering
that
there
exists.
No
such
lower
bound,
I
I
would
I
would
argue,
I
would
argue
just
proving
that
the
lower
bound's,
not
zero,
is
already
a
quite
difficult
problem.
A
That
would,
you
know,
be
worth
solving,
and
the
last
thing
that's
a
very
big
open
problem.
Research-Wise
in
Mev
is
this
concept
of
aggregation
versus
disaggregation
effects
and
so
I
think
a
lot
of
people
in
in
the
Mev
parlance
we'll
focus
on
talking
about
things
like
private
order
flow
order,
flow
aggregation
like
what
happens
when
a
builder
has
all
of
a
certain
type
of
order
flow.
There
are
ways
to
to
talk
about
what
the
equilibria
of
the
system
are.
A
What's
the
distribution
of
Revenue
to
different
participants
based
on
these
kind
of
aggregation
versus
disaggregation
effects,
but
if
I
were
to
kind
of
give
you
the
stylized
version
of
what
this
question
is
asking
and
saying
what
is
the
coast
theorem
for
Mev
so
cos
theorem
for
for,
for
those
who
don't
know,
is
sort
of
a
a
very
famous
thing
that
I
think
basically
says.
Imagine
you
have
a
company
now
suppose
the
company
had
everyone
at
the
company
as
an
individual
contractor.
Why
is
it
more
inefficient?
A
Why
is
it
more
efficient
to
actually
organize
as
a
group
versus
individual
contractors
who
are
giving
payments
to
each
other
and
the
coast,
theorem
sort
of
says,
there's
some
equilibrium,
the
Middle,
where
there's
aggregation
effects
where
it's
cheaper
for
people
to
work
together
and
share
common
infrastructure
up
to
some
point?
And
then
after
that
point,
the
overhead
of
managing
that
many
people
starts
causing
you
to
to
lower
your
welfare,
and
this
notion
of
aggregation
versus
disaggregation
and
Mev
is
is
completely
unsolved.
Yet
people
are
always
talking
about
it.
A
B
C
So
my
question
is:
you
gave
an
example
of
you
gave
first
theoretical
example
of
how
removing
an
edge
from
a
network
can
make
traffic
more
efficient,
and
then
you
give
an
analogy
for
how
this
can
also
happen
with
amms,
and
you
gave
a
practical
example
of
how
it
could
happen
with
amms.
But
I
I
did
not
understand
how
this
practical
example
works.
So
could
could
you
explain
more
like
how
could
Mev
improve
the
efficiency
of
trading
on
this
scenario
with
three
two
pairs.
A
Yeah,
so
so
think
of
it,
this
way,
I.
Imagine
that
there's
people
who
want
to
trade
from
token
a
token
B
and
there's
an
order
flow
of
n
orders
from
A
to
B.
If
I
look
at
how
to
optimally
route
the
orders
from
A
to
B,
if
everyone
gets
routed
on
the
same
route,
the
price
that
everyone
pays
on
average
could
be
a
lot
higher
right.
If
everyone
gets
routed
on
different
routes,.
A
When
there's
a
bunch
of
traffic
going
on
the
same
route,
it
increases
the
Mev
profit,
but
as
the
Mev
profit
goes
up,
then
people
start
routing
away
from
that
and
you
could
effectively
think
of
it
as
a
way
of
sort
of
having
a
decentralized
coordinator
where,
where
it
comes
from
the
fact
that
the
more
traffic
going
on
one
route,
the
more
the
sandwich
profit.
But
then
once
people
start
realizing
the
sandwich,
profit
they
route
elsewhere,
and
so
this
routing
problem
is
actually
you
know
the
quintessential
example
of
these
cases.
A
Where
congestion
sort
of
you
can.
You
can
add
in
some
sort
of
like
incentives
to
avoid
congestion
and-
and
you
know,
a
lot
of
internet
routing
traffic
routing
on
the
internet.
A
lot
of
that
kind
of
work
is
like
where
this
initially
started,
and
this
is
sort
of
making
an
analog
about
here.
But
the
key
is
that
someone
is
getting
the
worst
price.
By
being
sandwiched,
there's
no
doubt,
but
everyone
going
across
the
graph
is
getting
a
better
price
on
average
and
that
that's
the
social
welfare
versus
Revenue
trade-off.
If
that
makes
sense,.
D
A
Yeah
I
mean
I
think
the
hard
part
with
any
of
these
sort
of
like
pseudo
centralized,
Federated
type
of
things
is
well
I,
I
mean
the
validator
should
can
just
always
do
the
Mev
themselves
right,
like
the
limit
of
of
the
centralized
thing,
is
like
well,
what
does
Facebook
what
are
Facebooking
Google
doing,
they're
just
doing
Mev
on
their
own
auctions
right
they're,
constructing
the
bidding
strategies
for
you,
they're
like
we're
using
the
highest
machine
learning
to
give
you
optimal
like
targeting.
It's
like.
A
It
actually
means
actually
we're
just
going
to
front
run
you
in
our
auction,
but
it's
too
complicated
to
explain
how
so
in
the
centralized
World.
These
types
of
things
are
not
that
interesting
to
analyze.
From
that
perspective,
in
the
sense
that
the
centralized
coordinator
will
just
like
choose
the
most
expensive
route
for
you
all
the
time
right
but
in
the
decentralized
world
is
actually
quite
interesting
because
the
user
has
to
choose
their
own
way
through
the
network,
so
yeah
that
that
sort
of
you
great.
B
E
Hi
I'm,
following
up
on
the
first
question
in
the
brass
example,
the
conditions
under
which
the
routing
would
actually
or
in
which
Mev
would
be
beneficial
is,
and
if
you
can
teleport
right,
these
conditions
are
usually
not
met.
What
are
the
you
said
under
certain
conditions?
This
applies
for
MMS.
What
are
those
conditions?
Does
it
mean
if
I
can
trade,
you
know
if
I
can
print
free
money
or
trade,
it
unlimited
or
zero
exchange
rate
or
what
are
the
conditions
in
which.
A
No
yeah,
actually
that's
a
good
point,
so
so
the
the
zero
is
in
the
traffic
example.
In
the
amm
example,
you
can
never
have
zero
effectively
in
that
Edge.
There's
always
some
amount
of
liquidity
price
impact.
Unless
it's
like
you
know,
a
linear,
amm,
I
I
think.
The
main
thing
we
showed
is
that
the
liquidity
difference
on
certain
edges
on
that
graph
relative
to
the
center
edge
has
to
be
large.
So
it's
not
necessarily
that
it's
saying
that
hey
you're
teleporting
or
it's
like
you're
you're.
A
You
have
some
sort
of
like
non-balanced
budget
Condition.
It's
actually
just
that
the
liquidity
relative
liquidity
differences
on
different
edges
has
to
be
high
and
that
those
are
the
conditions-
and
you
know
I'd,
say
our
proof-
is
pretty
pessimistic
in
our
conditions
and
there
definitely
were
ways
of
like
getting
much
better
bounds
on
it.