Issues with economic and social systems modelling
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Issues with Economic and Social systems modelling. Mariam Kiran University of Sheffield. Future Research Directions in Agent Based Modelling June 2010. Talk Agenda. Agent-based modelling for socio-economic systems as compared to the traditional methods. Case study: EURACE model

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Issues with Economic and Social systems modelling

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Issues with Economic and Social systems modelling

Mariam Kiran

University of Sheffield

Future Research Directions in Agent Based Modelling

June 2010

Talk Agenda

  • Agent-based modelling for socio-economic systems as compared to the traditional methods.

  • Case study: EURACE model

    • Useful results

    • Issues raised

  • Case study: Social Capital Model

  • Conclusions

Modelling of socio-economic systems

  • Traditional approaches involve using differential equations

  • Use game theory models, commonly with a maximum 5 number of players in the model

  • Large number of exaggerated assumptions

    • Rational people making rational decisions

    • Small populations

    • Complete knowledge

      ABMs can overcome some of these issues, like

      populations, heterogeneity , etc

Case Study: EURACE

FLAME Framework

The first attempt for economic modellers to merge more than one market together to represent a complete economy.

Each individual is considered as a agent like households, firms or more.

Various computer scientists and economists worked together to achieve the goals of this project (8 universities)

EURACE markets and their interactions

Eurace dot file

EURACE Modelling issues

Large complicated agents and large concentrations.

Too much of communication overhead for agents communicating with each other.

Economists had very little programming experience.

Libmboard – FLAME message board library

Uses distributed memory model Single Program Multiple Data (SPMD).

Synchronisation helps prevent deadlocks.

Uses Message Passing Interface to communicate messages

Using filters and added iterators have helped quicken message parsing for agents.

Simulation time results

Comparing Economic policies for EU

The effect of Fiscal tightening (FT) and Quantitative Easing (QE) on price and wage levels

Effects of technology, innovation and skill for old and new EU members

Specific skill levels of workers when the labour markets are open or closed.



Energy shocks to the markets

The effect on GDP growth with and without energy crisis

EURACE results

Predicts that not increasing taxes will allow UK to recover from the recession.

Opening borders across the EU benefits all countries for the labour market.

Energy shocks to the system. System came back to an equilibrium when this happened.

Case Study: Social Capital Modelling

  • -Replication of mathematical model

  • -Calculations of numbers of transitive relationships, reciprocated ties, incomplete transitive ties

  • Looping, Bottlenecks

  • Flame group is currently working on overcoming these issues

-2.5 outdegree




Initial Structure = In-Star

Comparing Geometric and Round robin partitioning

Geometric partitioning is when agents are distributed across processors based on their x and y coordinates

Round robin partitioning is when agents are distributed evenly across processions

Centralised versus Decentralised Models

Time increases as number of nodes are increased

Cournot Model

Time is unchanged with nodes

Sugarscape + IPD Model


Think about the kind of models.

Initial distribution of agents on processors.

Is the model correct? Run the model till we reach equilibrium.

Copying files across for data analysis. GB of data can take hours to copy across.

Communication problems between computer scientists and economists, sociologists.

Different time expectations between disciplines.

More information:

  • FLAME Website:

  • Documentation can be found:

  • Other current models our group is working on:

    Ant Phermone trails

    Social Networks

    Sperm behaviour

    E-Coli behaviour

    Epithelium Tissue

Move to reality using ABMs








  • Collection of unique individuals.

  • Experimenting with different populations.

  • Most assumptions are being overcome.

  • Each individual is different, represents heterogeneous collection.

    • Each has different properties, different functions, different memories.

  • There can be a million representative of the same individuals or a million others in the system.

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