Model Replication in the Context of Agent-based Simulation

Richárd O. Legéndi, László Gulyás, Yuri Mansury
Eötvös Loránd University, AITIA International, Inc., Cornell University
rlegendi@aitia.ai, lgulyas@aitia.ai, ysm3@cornell.edu

This work was partially supported by the Hungarian Government (KMOP-1.1.2-08/1-2008-0002), the European Union
Seventh Framework Programme FP7/2007-2013 under grant agreement CRISIS-ICT-2011-288501 (CRISIS –
Complexity Research Initiative for Systemic InstabilitieS) and mOSAIC 2011-256910 (Open-Source API and Platform
for Multiple Clouds). These supports are gratefully acknowledged.

1st European Conference on Political Attitude and Mentality
ECPAM 2012, Bucharest, September 3-5, 2012

Layout
 Motivation and Background
 Replication? Why care?
 Case Studies and Results
 ABM approach for the New Economic Geography
 Replication of the Bottom-up Adaptive Macroeconomics
 Summary

2012.09.03. ECPAM 2012 - Replication case studies 2

Replication? Why care?
 Replication of experiments, validation of results are
essential
 „Simulations as experiments”
 If cannot be reproduced, its scientific value is in
question
 Models never replicated - except a few classical ones
 Helps us get a deeper understanding
 Of relevant properties, key issues
 Deploy simulation as a research tool


Validation?
 Docking – alingment of different models
 Different computational models for the same
phenomenon
 Replication
 W/o being able to replicate results of an artificial model,
how to target real-world systems?
 Several problems, e.g. ambiguity
 Different approaches exist (AgentUML, ODD, etc.)
 But there’s no consensus on using them...


An Agent-Based Adaptation of the New Economic Geography


New Economic Geography
 Paul Krugman’s city
formation model
 Originally a numerical
model
 Applied agent-based
approach

Masahisa Fujita, Paul Krugman, Anthony J.
Venables: „The Spatial Economy.” MIT Press,
Cambridge, MA, 1999.


Model Structure


Zipf’s Law in City Formation
City Population Rank
(2010)
New York 8,175,133 1
Los Angeles 3,792,621 2
Chicago 2,695,598 3
Houston 2,099,451 4
Philadelphia 1,526,006 5

Phoenix 1,445,632 6
San Antonio 1,327,407 7

San Diego 1,307,402 8
Dallas 1,197,816 9
San Jose, CA 945,942 10


Motivation
 Previous works explains Zipf’s law successfully
 But lacks micro-foundations
 We extended th FKV model
 General-equilibrium model
 Excellent micro-foundations
 But cannot generate a hierarchical system of cities


Why the Agent-Based approach?
 Introduce heterogeneity
 Noise
 Agent-specific migration thresholds
 Enables migration to proceed in a non ad-hoc way
 Extensibility


Results
 We proposed a spatial AB version of FKV
 Applied an inherently different approach
 Retains the key features of the original model
 Including consumers’ love for varieties
 Increasing returns in production
 Tension between centripetal (agglomeration) and
centrifugal (dispersion) forces


Tomahawk-diagram
 Population migration (λ)
vs. „freeness” of trade (φ)
 Break and sustain point
 φB and φS
 Closed-form solution
and implicit function to
evaluate


Replication Results
 Simulations replicates
expected results
 t = 2000 / 5000 time
steps
 φB and φS verified


Replication of the Macroeconomics from the Bottom-up


Macroeconomics from the Bottom-up
 Agent-based macro model
 Empirical external
validation
 Using real-world data
 Replication of the same
model
 In a different environment

Gatti, Domenico Delli, Saul Desiderio, Edoardo
Gaffeo, Pasquale Cirillo, and Mauro Gallegati:
Macroeconomics from the Bottom-up. 1st ed.
Springer, 2011.


Model Structure

Source: Domenico Delli Gatti, personal communications


Agents
 Households
 Supply labor
 Buy consumption goods
 Hold deposits
 Firms
 Demand labor
 Produce and sell consumption goods
 Bank
 Receive deposits from households
 Extend loans to firms


Market Processes I
1. Fims compute net worth, production/price and
labour demand
2. Credit market:
1. Bank decides credit conditions
2. Firms decide to whether take loan or not
3. Job market:
1. Firms redefine labour demand, publish vacancies:
1. Excess workforce: fire workers
2. Insufficient workforce: hire if possible


Market Processes II
4. Consumption goods market:
1. Workers get wages and compute consumption budget
2. Firms post their price
3. Consumers contact z firms randomly
 Ordered by price
4. Unspent money  Involuntary savings
5. Unsold goods  Sold at zero cost (non-durable)
5. Accounting
1. Firms calculate profits
2. Earnings are retained profits
 Used to update net worth.


Why to replicate? Parameter sweeps
„[...] suppose that in a model there are just 10 relevant
parameters, and that each parameter can assume 10
different values (a rather simplifying assumption). As a
result, one obtains that the constellation of the
parameter space is given by 10^10 vectors. If we perform
20 different runs for each one of them to take into
account the possible effects of changing the random
seeds, the total number of simulations would
amount to 2*10^11!”
Gatti, Domenico Delli, Saul Desiderio, Edoardo Gaffeo, Pasquale Cirillo, and Mauro Gallegati:
Macroeconomics from the Bottom-up. 1st ed. Springer, 2011 (p. 76., section 3.10.1)


Why to replicate?
 In a different environment?
 Matlab  Java/Mason

 Efficiency
 Reduce required time for a single simulation run
 Tool support: MEME
 Parameter sweep exploration
 Being Strong
 Exploiting Grid/Cloud systems
 Being Smart
 Design of Experiments


Background
“The CRISIS project addresses building a next generation
macroeconomic and financial system policymaking model: a
bottom-up agent-based simulation that fully accounts for the
heterogeneity of households, firms, and government actors. The
model will incorporate the latest evidence from behavioral
economics in portraying agent behavior, and the CRISIS team will
also collect new data on agent decision making using
experimental economics techniques. While any model must
make simplifying assumptions about human behavior, the CRISIS
model will be significantly more realistic in its portrayal of relevant
agent behavior than the current generation of policymaking
models.”

Crisis project description: https://www.crisis-economics.eu/


Replicated
Model
Modelling Economic Simulator
Framework (Cloud-Based
Parameter Sweep
Execution)

Models Web-based Game
(Participatory
Experiments)


Results I - Benchmarking


Result II – Verification
 Scaled agents (w/o changing overall ratio)
 Up to 7500 agents
 Avg’d 40 runs
 t = 1000 time steps
 Included initial state
 High oscillations
 Until spontaneous
order emerges
(„equilibrium”)


Summary: Case Study 1
 We created a replication of the FKV by using a
different approach
 Retains hallmark of the original model

 Introduced heterogeneity at several levels
 Allows further studies
 With different activation regimes
 N-cities model


Project Info http://emergingcities.aitia.ai


Summary: Case Study 2
 We created a replication of the MacroABM model in a
different environment
 Identic output
 Results are platform, environment-independent
 Opens up the window of standardized simulation tools
 Extensive parameter space explorations (MEME)

 Performance speedup
 By the factor 5x-10x
 On the other hand, code length is increased similarly:
 Matlab: ~300 LoC
 Java: 1500 + 1000 LoC


Download!
http://www.crisis-economics.eu/jmark-i-build-report


Richard O. Legendi
Mail: rlegendi@aitia.ai
Twitter: @legendi_ELTE
Blog: http://xcafebabe.blogspot.com
Web: http://people.inf.elte.hu/legendi/

September 3., 2012.

Emerging Cities Website: http://emergingcities.aitia.ai/
Crsisis Website: http://www.crisis-economics.eu/

Model Replication in the Context of Agent-based Simulation

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