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Injecting Data into Simulation: Can Agent-Based Modelling Learn from Microsimulation?

This presentation discusses the challenges of randomness in agent-based modelling (ABM) and proposes a data-driven approach using microsimulation techniques. A case study on the simulation of moral value changes in a society is presented to demonstrate the effectiveness of this approach. Suggestions for future research and the importance of comparing different data sources are also provided.

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Injecting Data into Simulation: Can Agent-Based Modelling Learn from Microsimulation?

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  1. Injecting Data into Simulation: Can Agent-Based Modelling Learn from Microsimulation? Samer Hassan Juan Pavón Nigel Gilbert Universidad Complutense de Madrid University of Surrey

  2. Contents • Problems in Randomness • A Method for Data-Driven ABM • A Case Study: Mentat • Concluding suggestions WCSS 2008

  3. Problems in Randomness • Uniform Random Distribution • Common for initialisation • But also in • Distribution of objects in space • Determining unmeasured exogenous factors • Controlling agent behaviour WCSS 2008

  4. Random Initialisation Random Initialisation Random Initialisation Mean Run Run Run Target Problems in Randomness ··· ··· WCSS 2008

  5. Problems in Randomness • There is always a chance of non-matching • What if the target behaviour is an outlier? WCSS 2008

  6. Problems in Randomness • Basing initial conditions on empirical data • Moving ABM in the direction of the Target • Even if the phenomena behaviour is an outlier WCSS 2008

  7. An example: Eurovision song contest • Hypothesis: “over a sufficiently long period of time the results of Eurovision would approximate to random” • Random initial conditions & random voting schema should approach the real situation... • …but they don’t WCSS 2008

  8. An example: Eurovision song contest • Introducing empirical data approaches the real scenario: • Distance between countries • Measuring similarity of cultures WCSS 2008

  9. Contents • Problems in Randomness • A Method for Data-Driven ABM • A Case Study: Mentat • Concluding suggestions WCSS 2008

  10. Microsimulation • Approaching to Microsimulation • Surveys / Census  initialisation • Equations / Probability rules  behaviour • Difficulties of Microsimulation • Requires plenty of quantitative data • Unable to model interactions WCSS 2008

  11. A Method for Data-Driven ABM • Learning from Microsimulation: • Minimizing random initialisation • Basing the simulation in representative survey samples • Explicit rules need plenty of data • Using probability equations to determine changes in the values of agent parameters • Injecting more data into ABM • From other sources (e.g. qualitative) • In other stages (e.g. design) WCSS 2008

  12. Classical Logic of Simulation WCSS 2008

  13. Proposal for Data-Driven ABM WCSS 2008

  14. A Method for Data-Driven ABM • Difficulties • When the ABM is too abstract • Empirical data cannot be obtained • Requires detailed data from individuals • Suitable surveys? Unobservable? • Need of individual history? (panel studies) • Requires dynamic information • Difficult to obtain: networks, micro-interaction • Complicating not always implies benefits • Loss of generality? Discussed WCSS 2008

  15. Contents • Problems in Randomness • A Method for Data-Driven ABM • A Case Study: Mentat • Concluding suggestions WCSS 2008

  16. A Case Study: Mentat • Aim: simulate the process of change in moral values • in a period • in a society • Plenty of factors involved • Now focusing on demography WCSS 2008

  17. Mentat: architecture • Agent: • Mental State attributes • Life cycle patterns • Demographic micro-evolution: • Couples • Reproduction • Inheritance WCSS 2008

  18. Mentat: architecture • World: • 3000 agents • Grid 100x100 • Demographic model • 8 indep. parameters • Network: • Communication with Moore Neighbourhood • Friends network • Family network WCSS 2008

  19. A Case Study: Mentat • Does the empirical initialisation substantially change the output in a pre-designed ABM? • Random approach • No effort for additional data • Average behaviour • Data-driven approach • Newly collected data is useful • Empirically based evolution WCSS 2008

  20. A Case Study: Mentat • Two ABM: • Same design and micro-behaviour • Different initialisation • Mentat-RND: Random age • Mentat-DAT: Empirically based age • Same validation • Against newly collected data, not used in initialisation WCSS 2008

  21. A Case Study: Mentat Comparison of outputs WCSS 2008

  22. Contents • Problems in Randomness • A Method for Data-Driven ABM • A Case Study: Mentat • Concluding suggestions WCSS 2008

  23. Concluding suggestions • Explore the problem background: availability of data? • Compare different sources of data to give a stronger foundation to the model • The most valuable data are those that provide repeated measurements • Design ABM with an output directly comparable with empirical data • Simulate the past and validate with the present WCSS 2008

  24. Thanks for your attention! Samer Hassan samer@fdi.ucm.es University of Surrey Universidad Complutense de Madrid WCSS 2008

  25. Contents License • This presentation is licensed under a Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/ • You are free to copy, modify and distribute it as long as the original work and author are cited WCSS 2008

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