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.

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

2. 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.

3. 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

4. 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

5. 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).

6. 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

7. 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)

8. 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 ?

9. 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

10. 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)

11. 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

12. 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)

13. 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

14. 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