Confessions of an Interdisciplinary Researcher: The Case of High Performance Economics
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Confessions of an Interdisciplinary Researcher: The Case of High Performance Economics

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Scaling up economics models to run on large input sizes, complex market and agent model settings, and on big computational resource pools is a demanding feat. ...

Scaling up economics models to run on large input sizes, complex market and agent model settings, and on big computational resource pools is a demanding feat.
This presentation tells you what it takes to work as a computational economist.

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    Confessions of an Interdisciplinary Researcher: The Case of High Performance Economics Confessions of an Interdisciplinary Researcher: The Case of High Performance Economics Presentation Transcript

    • Confessions
of
an
inter‐ disciplinary
researcher
 The
case
of
high‐performance
 economics
 by
 Tibi
Stef‐Praun
 :berius@ci.uchicago.edu
 Nov.
2009


    • The
evolu:on
of
science:

 Specialized
Modeling

 •  physical
and
biological
sciences
 have
proven
successes
with
 increasingly
complex
models
 •  economics
lags
because
it
is
 imprac:cal
to
conduct
experiments
 to
validate
theories,
and
data
fiIng
 and
simula:ons
remain
the
only
 tools
available

 •  current
state
of
the
art
in
 economics
is
in
models
(ref.
any
 graduate
textbook),
which
s:ll
need
 valida:on
with
mul:ple
data
sets
 •  economics
Nirvana
means
 integra:ng
all
models/theories
and
 simula:ng
and
predic:ng
realis:c
 outcomes.


    • The
seIng
for
economic
modeling
 •  modern
growth
theory
 –  model
individual
agents
(households,
firms,
govt.)
 –  market
with
asymmetric
informa:on
 –  forward
looking
agents
 –  stochas:c
shocks
 •  computa:onal
limita:ons
 –  above
model
features
introduce
alterna:ve
paths,
each
of
which
has
 to
be
evaluated
and
considered
in
the
final
choice
 –  curse
of
dimensionality
 •  advanced
tools
 –  standard
constructs:
dynamic
programming,
max
likelihood
 –  standard
tools:
op:miza:on,
solvers,
sta:s:cs
 –  agent’s
decision
is
an
op:miza:on
problem
 –  exploit
model
structure,
introduce
approxima:ons

    • Must
work
in
all
three
areas
 •  economic
theory
 –  need
to
understand
the
constraints
of
the
model
(agents
 decision
model,
:melines,
resources
involved)
 –  be
able
to
generalize
model
to
solve
related
data
and
seIngs
 (add
degrees
of
freedom)


 •  computa:onal
resources
 –  iden:fy
execu:on
paOers
(agent’s
decision
code,
market
setup
 and
clearing,
structural
calibra:ons,
etc.)
and
dependencies
 –  exploit
parallel/distributed
resources
(Grid/Clouds,
SwiQ/WS)
 •  mathema:cal
tools
 –  familiarity
with
solving
the
mathema:cal
formula:ons
 (op:miza:on
theory,
solver
libraries)

 –  understand
implica:ons
of
the
tools
used

    • Current
involvement:

 Dynamic
Mechanism
Design
Theory
 •  AOack
the
problem
from
the
economic
modeling
side,
provide
(scalability)
 improvements
to
exis:ng
models
(ini:a:ve
of
Rob
Townsend)
 •  Evalua:ng
choices
of
group
organiza:on
for
risk
sharing
purposes,
 by
Madeira
and
Townsend.
Paper:
Accelera'ng
solu'on
of
a
moral
hazard
problem
 with
Swi9,
eScience
conference
2007.
Contributed
modest
speedup
(20x).
 •  Linking
growth
to
financial
deepening
and
inequality,
by
Ueda
and
 Townsend.
Poster
with
Victor
at
the
Uncertainty
workshop
(2008).
Contribu:on
in
 parallelizing
Matlab
code
(stochas:c
shocks).
 •  Borrowing
choices,
work
in
progress
by
Esteban
Puentes
(with
 Townsend).
Contributed
70x
speedup
for
remote
expensive
func:on
evalua:on
 (2009):
 hOp://www.mathworks.com/matlabcentral/fileexchange/24982‐parallelizing‐matlab‐on‐large‐distributed‐compu:ng‐clusters
 •  Incomplete
financial
markets,
by
Karaivanov
and
Townsend.
Work
in
Progress,
 Contribu:ng
code
reengineering
for
user
defined
regime
evalua:on
and
parallel
 implementa:on
and
speedup.

 •  Wealth‐constrained
occupa:onal
choice
(LEB).
Contributed
prototype
of
web‐ based
user‐driven
input
data
genera:on
and
model
execu:on
(for
interac:ve
 model
evalua:on).



    • Current
involvement
(cont)
 Dynamic
Programming
 •  aOacks
the
problem
from
the
other
end:
provide
high
performance,
 scalable
tools
to
economists
(ini:a:ve
of
Ken
Judd)
 •  dynamic
programming
is
the
current
(rediscovered)
wunderkind,
as
it
 allows
realis:c,
forward‐looking,
stochas:c
decision
modeling
 •  contribu:on
is
in
designing
a
general
plaborm
(for
many
classes
of
DP
 problems)
that
is
both
scalable
(in
computa:onal
resources)
and
easy
to
 use

 –  DP
engine
takes
as
parameters
the
problem
descrip:on
(state
space
grids
and
 produc:on,
u:lity,
stochas:c
transi:on
callbacks)
 –  the
parallelizable
DP
computa:on
steps
are
mapped
transparently
(from
the
 user’s
perspec:ve)
onto
the
resources
 •  address
curse
of
dimensionality
by
brute‐force:
throw
resources
at
the
 problem
 •  it
is
only
a
temporary
solu:on
(offsets
the
real
problem
with
the
size
of
the
 compu:ng
resources).
It
needs
to
be
combined
with
intelligent
 dimensionality
reduc:on
techniques
(state
space
approx.,
mul:‐grid,
etc)
 •  speedup
advantage
is
a
combina:on
of
resources
and
algorithmic
 approxima:on

    • Technical
adventures
(I)
 •  large‐scale
(Grid)
execu:on
implies

 –  using
open‐source
and
redistributable
soQware.
A
lot
goes
into
 replacing
commercial
alterna:ves
or
building
fresh
solu:ons

 •  open‐source
is
less
reputable/efficient/precise/available
 •  verifica:on
against
commercial
results
is
essen:al
(huge
debugging
:me)
 •  e.g.
replace
Matlab
model+CPLEX
with
alterna:ves
 •  choosing
the
right
“framework”
language
so
that
economists
will
adopt
it

 –  replica:ng
a
proper
model
solving
environment
on
those
resources.
 •  install
model
components,
dependency
libraries
 •  e.g.
install
python
adapters
to
hdf5
library
on
BlueGene,
OR
compile
open‐ source
solvers
(CLP,
LP_SOLVE)

with
Matlab
MEX
adapters
on
various
Grid
 sites.
Deal
with
32
vs
64
bit
or
Windows
vs
Linux
plaborm
issues.



 –  acquiring
the
compu:ng
resources.

 •  In
a
shared
academic
environment
the
tragedy
of
commons
kicks
in
 •  tools
exist
to
assist
with
this:
reserva:ons,
glide‐ins,
etc.
 •  alterna:vely,
go
commercial
(cloud
compu:ng)



    • Technical
Adventures
(II)
 •  parallel/distributed
model
execu:on
implies
 –  integra:on
of
diverse
soQware
(Matlab
executables,
op:miza:on
 libraries,
wrapper
scripts,
remote
invoca:on
facili:es)
 •  complex
management/lifecycle
of
the
code
base
 •  we
use
tools
such
as
SwiQ
or
web
services
to
choreograph
model
components
 –  a
proper
decomposi:on
of
the
model
that
op:mizes
execu:on
:me
 (given
the
resources)
 •  must
understand
model’s
logical
blocks,
inter‐dependencies,
and
their
 significance
in
the
economic
problem
(need
a
LOT
of
domain
knowledge
OR
a
 economist
to
collaborate
with)
 •  profiling
the
execu:on
involves
repeated
measurements
and
code
 reorganiza:on
(spent
20k+
CPU
hrs.
on
BlueGene
on
dynamic
programing)

 –  transparent
execu:on
for
the
user
 •  economists
do
not
(should
not)
have
to
know
technical
details:
provide
an
 opera:ng‐system‐like
abstrac:on:
execute
(op:mally)
this
piece
of
code
 •  several
op:ons
exist,
all
imply
lifecycle
management
of
the
model
library/ service
for
the
life:me
of
the
applica:ons
using
it.
Service‐oriented‐science
?



    • Technical
Adventures
(III)
 •  Data
is
essen:al
 –  Data
enables
model
parameter
es:ma:on/calibra:on
 –  Data
cleaning
is
a
pain
 –  We
need
good/clean/validated
data
:
survey
designing,
execu:on,
and
delivery
 can
cause
lots
of
pain.
See
Open‐Data‐Tool
mobile
collec:on
 •  Data
access
is
essen:al
 –  Fast
explora:on
/
Visualiza:on
/Web
hOp://age3.uchicago.edu:8080/thailand

 –  Model‐dependent
input
genera:on
(automated
?)
 –  Database
storage,
organiza:on,
access

 –  Con:nuous
data
collec:on,
schema
expansion
 –  User
data
access:
select
and
extract
into
favorite
tools
(Stata,
Excel)
 •  Data
has
many
dimensions
 –  cross‐sec:onal/panel/spa:al
(GIS)
 –  iden:fiers
for
connec:ng
fragmented
record
collec:ons
 •  Data
described
and
available
at
 –  hOp://cier.uchicago.edu
 –  hOp://dvn.iq.harvard.edu/dvn/dv/rtownsend

    • Philosophical
Musings

 •  The
dimensionality
of
problem
space
hurts
 –  Structural
es:ma:on
(MLE,
GMM)
are
the
most
expensive
procedures,
they
 re‐run
the
whole
models
with
different
structural
parameters
to
find
the
best
 fit
 –  Op:miza:on
rou:nes
that
drive
these
(non‐linear
with
finite
difference
 gradient
evalua:ons)
are
dependent
on
the
star:ng
point
and
on
the
user’s
 mastery
of
the
search
algorithm’s
knobs
 –  number
of
free
parameters
determine
exponen:ally
the
computa:onal
 requirements
 –  discre:zing
the
problem
variable
space
affects
computa:on
requirements,
 results




 •  Knowledge
of
the
economic
problem
and
understanding
of
the
tools
that
 solve
it
can
oQen
lead
to
improvements
that
trump
computa:onal
brute‐ force
methods.
 •  Economists
avoid
integra:ng
models
or
building
complex
systems
because
 it
becomes
difficult
to
explain
the
results
of
such
simula:ons
(ceteris
 paribus
assump:on
starts
geIng
weak)



    • What
kind
of
research
this
is
 •  Paraphrasing
from
Office
Space:
“I
deal
with
the
resources,
so
that
the
 economists
don’t
have
to”
 •  For
CS
types,
it
is
a
combina:on
of
soQware
engineering,
 parallel
programming,
systems
integra:on,
mainly
applied
 to
mathema:cal
models.
 •  For
the
science
addicts,
it
combines
linear
algebra,
 op:miza:on
theory,
sta:s:cs,
game
theory,
and
behavioral
 theories
into
a
big
numerical
model.

 •  For
economists,
it
enables
asking
and
answering
big
(in
 input
size)
ques:ons
and
tackle
complex
models.
 •  Where
is
the
fun
in
that?
Applied
Scalable
Science
 •  At
this
stage,
it’s
an
art


    • What
this
kind
of
research
this
is
not
 •  it
is
not
a
quick
and
easy
way
to
publish
an
econ
paper.
Quite
the
 opposite
!
 •  it
does
not
apply
to
the
mainstream,
reduced‐form,
analy:cal
 economics
research,
it
is
mainly
cuIng‐edge,
micro‐founda:ons,
 numerical
simula:on

 •  it
is
not
about
valida:ng
parallel
execu:on
plaborms,
integra:on
 schemes,
etc.
 •  it
is
not
about
showing
high
throughput/high
performance
 capabili:es
of
the
models
on
massive
resources
(BlueGene,
etc)
 •  it
should
not
forget
about
the
primary
beneficiary:
the
researcher
 who
needs
to
run
his
models
with
confidence
and
in
manageable
 :me
 •  it
should
not
be
a
way
to
add
buzzwords
to
your
grant
proposal
 (Grid/Economics)

    • Support
 •  The
BAD:
 –  Generic
(economic)
domain
tools
are
rarely
funded
by
govt.
agencies
(NSF,
etc)
 –  Since
this
is
not
pure
economic
research,
and
as
it’s
heavily
slanted
towards
 computa:onal
resource,
liOle
chance
of
publishing
in
economic
journals
 (Econometrica,
etc)
 –  Since
this
is
not
a
generic
computa:onal
plaborm,
or
a
resource
alloca:on
 mechanism,
or
a
new
Science
2.0,
it
receives
liOle
interest
from
the
computer
 science
community
(HPDC,
etc)
 •  The
GOOD:
 –  a
few
ini:a:ves
support
this
kind
of
work
(Townsend,
Judd)
 –  lots
of
interest
with
the
students
(ICE
@
UChicago)
 –  big
ins:tu:ons,
government
should
be
interested
in
result,
such
work
should
 be
the
policy
evalua:on
tool
they
always
needed.
 •  The
OFFER:
 –  Join
forces
with
the
AGE3
group
(Applied
general
equilibrium
for
Entreprise
 Economics)
and
be
involved
in
exci:ng
science.
We
cater
to
the
needs
of
big
 economists
!
 –  hOp://age3.uchicago.edu


    • About
me

 (example
personal
journey)
 •  Started
as
a
CS
(focus
on
systems),
ended
up
with
PhD
thesis
on
market‐ based
decentralized
(in
space
and
ownership)
resource

(web‐service)
 alloca:on
 •  Moved
to
Grid
technologies,
worked
on
scaling
up
(parallelizing)
 applica:ons
for
various
clustered
resources.
Used
SwiQ
parallel
workflow
 descrip:on
and
execu:on
engine.

 •  Specialized
in
Economics
applica:ons
(Growth
Theory,
Mechanism
Design,
 DSGE,
micro‐founda:on‐based
modeling)
and
their
applica:on
to
 emerging
economies
(with
incomplete
financial
markets,
entrepreneurial
 growth
poten:al,
etc).
2+
years
experience
 •  Close
collabora:on
with
the
Enterprise
Ini:a:ve
hOp://enterpriseini:a:ve.org

 •  A
related
(earlier)
presenta:on:
hOp://www.youtube.com/watch?v=Uaw7VMZw7tQ


 •  Interested
in
joint
grant
proposals
on
the
topics
above.
 •  Interested
in
collabora:ons
with
large
economics
ini:a:ves
 :berius@ci.uchicago.edu