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EURACE and SPACE
Christophe Deissenberg
Université de la Méditerranée
and GREQAM
Bruxelles, Oct. 1st 2010
EURACE and SPACE
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EURACE
An agent--based software platform for European economic
policy design with heterogeneous interacting agents: new
insights from a bottom up approach to economic modeling
and simulation
IST-FET proactive initiative “Simulating
Emergent Properties in Complex Systems”
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EURACE Goals
1.
Technological: Construct a software platform to
perform and analyze very-large-scale agent-based
simulations on parallel computing systems.
2.
Scientific: Develop a very large, closed agent-based
model of a multi-national economy (EU-27).
3.
Economic: Conduct and analyze macroeconomic
policy experiments.
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EURACE Structure
ROW: Energy
Regulatory
bodies
Central
bank
regulations
Firms
financing
labour
reserves
revenues
taxes
interests
interests
Governments
Labour
markets
Cons.
Goods
markets
Assets
markets
Banks
taxes
savings
wages
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public
spending
goods
Households
EURACE and SPACE
dividends,
interests
interests
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Stock-flow consistency
• Interlinked balance sheets:
– Consistency of all stocks and flows
– Monetary and physical flows
– Time-evolution of the balance sheets
• System of national accounts:
– SAM: Social accounting matrix
• Consistency rules:
– Accounting identities (intra agent)
– Economic time-invariants (across sectors)
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Time and space
• Decisions are taken daily, monthly, quarterly, or annually
• Likewise, beliefs are updated and data are processed
daily, monthly, quarterly, and annually
• Space is divided into heterogeneous regions (countries)
• Production, sales, and housing occur at specific
geographical locations
• Transportation of goods and commuting are costly
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EURACE Agents
•
•
•
•
•
•
•
•
Households (∼10⁶)
Investment good producers (∼ 10²)
Consumption good producers (∼ 10³)
Local outlet malls (∼ 10)
Banks (∼ 10²)
Governments (∼ 27)
Central Bank (1)
Others: Market research entity, Statistical office
(Eurostat), Asset Management Companies.
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Decision making
• Behavioral rules:
 strong micro-foundations, empirically based :
 Firms: standard decision-making rules from the Operations
Management/MBA/Finance literature:
 Markup pricing
 Standard Inventory and Production Planing
 etc.
 Households: saving and consumption decisions a la Deaton
(1991).
 beliefs formation:
 History
 Local feedback: economic partners
 Global/regional feedback: Eurostat
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Details: Consumption Goods
• Homogenous consumption goods are produced using (vertically
differentiated) capital and labor.
• Complementarity between quality of investment goods and level of
specific skills of workers
– Productivity of a given technology level is only fully exploited if
workers in the firm have sufficiently high specific skills.
• Differentiated skill structure: general (formal training), specific
– General skills heterogeneous within and across regions.
– Specific skills attained during employment in firms.
– Higher general skills improve speed of attaining specific skills.
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Details: CGs Market
•
Consumption goods producers offer (and store) goods at
selected geographically distributed local market outlets
(‚malls‘). They post (different) prices of their goods in all
malls they serve.
•
When visiting a mall a consumer collects randomly
selected information about local prices and inventories.
Purchasing decision is modelled using a logit approach.
•
Suppliers on the consumption goods market act
globally (without spatial frictions) whereas
consumers buy locally.
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Details: Technological Change
• The quality of supplied investment goods increases
stochastically over time.
• The average quality of the capital stock of a CGP is
updated as old capital is replaced by new investments.
• The profits of investment goods producers are uniformly
distributed to households.
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Details: Labor market
• Firms post job vacancies based on planned output.
• Searching workers send applications based on posted
salaries.
• Firms rank applications based on general skills and
make offers.
• Workers rank offers (wage - commuting costs),
compare best offer to their reservation wage and
accept/reject.
• => Firms may be rationed on the labor market; there is
frictional unemployment.
• => Labor Market is global with spatial frictions
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Using EURACE: Philosophy

Calibrate the parameters, using empirical data wherever
possible.
• Some parameters are chosen in order to stabilize the
simulations and yield plausible outcomes:
– Examples:
• Parameters in demand forecasting rules
• Parameter governing wage offer increases
triggered by rationing on the labor market
• Speed of adjustment of firm’s planned capital stock
• Parameters in the consumer’s logit model
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Using EURACE: Philosophy

Check that EURACE model is able to reproduce the
main ‘stylized facts’: time series and distributions.

Run simulation batches for different policy scenarios.

Formulate hypotheses about the policy impact.


Use statistical tests to check the significance of the
hypothesized impact.
Gain a qualitative understanding of the relevant
economic mechanisms responsible for the observed
phenomena by examining the evolution of key variables.
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Using EURACE: Practice
• EURACE is implemented in FLAME
• EURACE can run on any machine, from a PC
(practically: up to about 10 000 agents) to massively
parallel machines
• A GUI allows straightforward modifications of the
behavioral rules, initialization, running of simulations,
storage and statistical analysis of the desired parts of the
output, production of graphics, etc.
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Some policy experiments
•
•
•
•
•
•
Open labor markets within the EU: Frictions
can be Pareto-improving.
Should a government invest in formal
education or subsidize the firms?
The effects of quantitative easing and fiscal
tightening in dealing with a credit crisis.
A comparative study of policies to deal with an
exogenous oil price shock.
The importance of financial management
rules.
Etc.
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EURACE: Some conclusions
•
The agent-based EURACE macro-model reproduces well major stylized
facts, some of which are not adequately captured by the standard models.
•
It has been used to explore several issues directly relevant to the Lisbon
strategy or to European policy-making in general.
•
It allows to capture important aspects of the heterogeneity between EU
members.
•
The results of policy experiments confirm sometimes the conventional
wisdom. In other instances, they are new, unexpected, but meaningful.
•
For example, EURACE shows that the link between demand effects and
diffusion of new technologies matters (although it is ignored in standard new
growth models)
•
Reasonable “micro-explanations“ were found for all the results.
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EURACE: Some conclusions
All this makes us confident that the concept
underlying EURACE is sound and can shed
new, policy-relevant light on the working of a
multinational economy
But much remains to do …
A next step: SPACE ?
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SPACE
• A FET-Open proposal, with main goals:
1. Develop a comprehensive, empirically
meaningful, modelling framework that allows to
study the complex interactions in a spatial multinational economy;
2. Develop the ICT technology that makes the
model practically useful for policy making
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SPACE
• Like EURACE, we shall develop a very large, empirically
meaningful, multi-scale agent-based model of a multi-national
economy
• But SPACE will be MUCH MORE advanced, among others w.r.t. to
the physical and non-physical evolving networks linking the agents
• It will include among others realistic financial networks (financial
institutions and firms), allowing more in-depth investigations of
financial (in)stability and of the link financial-real sphere
• It will allow to study country-city dynamics, the spatial aspects of
knowledge flow …
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SPACE
• The agents will interact on evolving networks (trade, communication,
etc.)
• They will be able to relocate on the landscape
• They will exhibit adaptive behavior based on past experience.
• Some other important features that the model will capture are
demographic aspects (population aging, births and deaths) as well
as sophisticated protocols for firm creation and bankruptcy
• SPACE will have the potential to be linked to major existing spatial
models capturing e.g. environmental issues, climate change,
epidemics, or other dynamic properties of the physical environment .
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SPACE
The needs of the decision-makers will be better taken into
account:
• Better interface
• Improved data management
• Innovative vizualisation techniques
• Computational steering
• Etc.
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SPACE
The development philosophy will be different:
• EURACE: Develop more or less independent modules
and integrate them ex-post
• SPACE: Define a complete skeleton and enrich it
organically
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SPACE
SPACE will make qualitative and
quantitative quantum leaps towards
offering a comprehensive, practicable
agent-based model for multi-national
policy-making
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Thanks for your attention!
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EURACE Agents
• Households
Buyers on Consumption Goods Market
Sellers on Labor Market
Deposits at banks
Buyers/Sellers of firm shares
• Consumption Goods Producers
Buyers on Labor and Investment Goods Markets
Sellers on Consumption Goods Markets
Loans from banks
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EURACE Agents
• Commercial Banks
 Collect deposits from Households
 Give loans to firms
 Access standing facilities of the central bank
• Government
 Collects taxes, distributes unemployment benefits,
subsidies
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EURACE Agents
• Eurostat
 Collects regional and sector data
 Constructs macrodata
• Central Bank
 Monetary policy
• Malls
 Local markets for consumers, global markets for firms
• Clearinghouse
 Implements the order matching market mechanism for
asset orders
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