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Issues with Economic and Social systems modelling

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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  1. Issues with Economic and Social systems modelling Mariam Kiran University of Sheffield Future Research Directions in Agent Based Modelling June 2010

  2. 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

  3. 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

  4. 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)

  5. EURACE markets and their interactions

  6. Eurace dot file

  7. 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.

  8. Libmboard – FLAME message board library

  9. Uses distributed memory model Single Program Multiple Data (SPMD). Synchronisation helps prevent deadlocks. Uses Message Passing Interface to communicate messages

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

  11. Simulation time results

  12. Comparing Economic policies for EU The effect of Fiscal tightening (FT) and Quantitative Easing (QE) on price and wage levels

  13. 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. Germany Poland

  14. Energy shocks to the markets The effect on GDP growth with and without energy crisis

  15. 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.

  16. 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

  17. -2.5 outdegree 1.00 reciprocity 0.55 transitivity 0.45 similarity Initial Structure = In-Star

  18. 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

  19. Centralised versus Decentralised Models Time increases as number of nodes are increased Cournot Model

  20. Time is unchanged with nodes Sugarscape + IPD Model

  21. Conclusions 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.

  22. More information: • FLAME Website: www.flame.ac.uk • Documentation can be found: www.eurace.org www.eurace.groups.shef.ac.uk • Other current models our group is working on: Ant Phermone trails Social Networks Sperm behaviour E-Coli behaviour Epithelium Tissue

  23. Move to reality using ABMs Companies Others Others Others Others Banks Shops • 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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