1 / 34

Re-thinking Modelling: a Call for the Use of Data Mining in Data-driven Social Simulation

Re-thinking Modelling: a Call for the Use of Data Mining in Data-driven Social Simulation. Samer Hassan Javier Arroyo Celia Guti é rrez. Universidad Complutense de Madrid. Contents. Data-driven ABM DM-assisted Methodology Case Study: Mentat Application Conclusions. Research Aim.

dorao
Télécharger la présentation

Re-thinking Modelling: a Call for the Use of Data Mining in Data-driven Social Simulation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Re-thinking Modelling: a Call for the Use of Data Mining in Data-driven Social Simulation Samer Hassan Javier Arroyo Celia Gutiérrez Universidad Complutense de Madrid

  2. Contents • Data-driven ABM • DM-assisted Methodology • Case Study: Mentat • Application • Conclusions SS@IJCAI 2009

  3. Research Aim SS@IJCAI 2009

  4. Research Aim • Theoretical • KISS • Structural Validation • Abstract • General SS@IJCAI 2009

  5. Research Aim • Data-driven • Non-KISS • Empirical Validation • Specific (case study) • Expressive • Theoretical • KISS • Structural Validation • Abstract • General SS@IJCAI 2009

  6. Classical Logic of Simulation SS@IJCAI 2009

  7. Data-Driven Logic SS@IJCAI 2009

  8. Data-driven Approach • Complexity • Large amounts of Data • Auxiliary AI: • Fuzzy Logic • Ontologies • Evolutionary Computation • Data Mining SS@IJCAI 2009

  9. Data Mining • Data Mining • Extracting patterns and relevant information from large amounts of data • Pre-processing of empirical data • Cluster finding • Discovery of hidden patterns • Locates redundancies • Post-processing of simulation output • Clustering: • Discovery of hidden patterns • Validation of clusters • Locates inconsistencies • Classification • Cluster matching SS@IJCAI 2009

  10. Contents • Data-driven ABM • DM-assisted Methodology • Case Study: Mentat • Application • Conclusions SS@IJCAI 2009

  11. Methodology for DM-assisted ABM SS@IJCAI 2009

  12. Methodology for DM-assisted ABM • Data Collection • Initial point • Validation points • Necessarily ≠ initial • Type • Explicit • Externalised • Empirical distributions • Secondary sources • Methods • Quantitative • E.g. surveys • Qualitative • E.g. interviews SS@IJCAI 2009

  13. Methodology for DM-assisted ABM • Analysis • Preprocessing of empirical data • Roles • Domain expert • Guide DM exploration • Interpretation • DM expert • Confirm or refine theories SS@IJCAI 2009

  14. Methodology for DM-assisted ABM • Selection of Relevant Data • Filtering • Adaptation of data • Normalisation • Discretisation • Domain Expert • Theory • DM • Redundancies • Overlooked independent variables SS@IJCAI 2009

  15. Methodology for DM-assisted ABM • Types • Cluster analysis • Principal Component Analysis • Time series methods • Association rules • Data Analysis • Large data collections • Guided by theory SS@IJCAI 2009

  16. Methodology for DM-assisted ABM • Interpretation of results • Theory expert • Relate results to theory • New findings are added to the findings base SS@IJCAI 2009

  17. Methodology for DM-assisted ABM • ABM Building • Based on Findings • Modeller • Steps • Formalisation • Data-driven Design • Implementation • Initialisation SS@IJCAI 2009

  18. Methodology for DM-assisted ABM • Simulation • Fine tuning the ABM • Sensitivity analysis • Intensive testing • Output • Record agent trace SS@IJCAI 2009

  19. Methodology for DM-assisted ABM • Validation • Analysis of the results • Empirical validation • Theoretical consistency • Roles • DM expert • Analyse the data • Domain expert • Extract conclusions • Iterative cycle SS@IJCAI 2009

  20. Contents • Data-driven ABM • DM-assisted Methodology • Case Study: Mentat • Application • Conclusions SS@IJCAI 2009

  21. The Problem • Aim: simulate the process of change in social values • in a period • in a society • Plenty of factors involved • Inertia of generational change: • To which extent the demographic dynamics explain the mental change? • Inter-generational: • Agent characteristics remain constant • Macro aggregation evolves SS@IJCAI 2009

  22. Mentat: architecture • Agent: • Mental State attributes • Life cycle patterns • Demographic micro-evolution: • Couples • Reproduction • Inheritance SS@IJCAI 2009

  23. Mentat: architecture • World: • 3000 agents • Grid 100x100 • Demographic model • 8 indep. parameters • Social Network: • Communication with Moore Neighbourhood • Friends network • Family network SS@IJCAI 2009

  24. Contents • Data-driven ABM • DM-assisted Methodology • Case Study: Mentat • Application • Conclusions SS@IJCAI 2009

  25. Data Collection in Mentat • Initial data: • EVS-1980 • Representative sample of Spain • Qualitative info • Empirically-grounded demographic equations • Validation data: • EVS-1990 • EVS-1999 SS@IJCAI 2009

  26. Analysis in Mentat • Selection of relevant data • EVS-1980,1990,1999 • Options: • Algorithm for the best subset of variables • Rely on domain expert • Tested domain knowledge • (2) chosen • Variables adaptation • Normalisation SS@IJCAI 2009

  27. Analysis in Mentat • Data Analysis • Algorithm selection • Wrapped k-means • Explore different k (# of clusters) • Discarded variables • Gender & Age provokes appearance of irrelevant clusters • E.g. widowed women • Economy is redundant • High correlation with Education SS@IJCAI 2009

  28. Analysis in Mentat • Interpretation • Sociological research • Religious typology (RLGTYPE) • Based on 3 variables • Ecclesiastical, low-intensity, alternatives & non-religious • Clusters found (1980, 1999) • Based on the 9-3=6 variables • 5 clusters with sociological meaning • Consistent with RLGTYPE • Theoretical observations of the pattern evolution: • Religiosity strength falls • Ideological spectrum twists to the left • education & economy • Newest type of religiosity, “alternatives” rise • youngsters SS@IJCAI 2009

  29. Analysis in Mentat SS@IJCAI 2009

  30. Validation in Mentat • Mentat re-building & simulation explored • Mentat output clusterised • Same 5 clusters found • Similar evolution trends • 3 theoretical observations shown • Inconsistencies detected • Liberal cluster % do not match • although aggregated they do • Graphics show less youngsters • Liberal clusters deeply affected • Guide to re-design SS@IJCAI 2009

  31. Contents • Data-driven ABM • DM-assisted Methodology • Case Study: Mentat • Application • Conclusions SS@IJCAI 2009

  32. Conclusions • DM-assisted ABM methodology • Suitable for DDABM • Complexity • Large amounts of data • Limitations • KISS • Qualitative sources • Uses • Build new ABM • Re-thinking existing DDABM • Revealing hidden facts • Detect inconsistencies SS@IJCAI 2009

  33. Thanks for your attention! Samer Hassan samer@fdi.ucm.es Universidad Complutense de Madrid SS@IJCAI 2009

  34. 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 SS@IJCAI 2009

More Related