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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.

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Presentation on theme: "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."— Presentation transcript:

1 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

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

3 Samer Hassan SS@IJCAI 2009 3 Research Aim

4 Samer Hassan SS@IJCAI 2009 4 Research Aim Theoretical KISS Structural Validation Abstract General

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

6 Samer Hassan SS@IJCAI 2009 6 Classical Logic of Simulation

7 Samer Hassan SS@IJCAI 2009 7 Data-Driven Logic

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

9 Samer Hassan SS@IJCAI 2009 9 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

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

11 Samer Hassan SS@IJCAI 2009 11 Methodology for DM-assisted ABM

12 Samer Hassan 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

13 Samer Hassan 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

14 Samer Hassan 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

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

16 Samer Hassan 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

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

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

19 Samer Hassan 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

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

21 Samer Hassan 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

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

23 Samer Hassan 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

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

25 Samer Hassan 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

26 Samer Hassan SS@IJCAI 2009 26 Analysis in Mentat Selection of relevant data EVS-1980,1990,1999 Options: 1.Algorithm for the best subset of variables 2.Rely on domain expert Tested domain knowledge (2) chosen Variables adaptation Normalisation NameTypeRange gendercategorical agenumeric≥18 studiesnumeric≥5 civil statecategorical economynumericreal ideologyordinal1-10 conf. churchordinal1-4 church att.Ordinal1-7 relig. personcategorical

27 Samer Hassan 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

28 Samer Hassan 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

29 Samer Hassan SS@IJCAI 2009 29 Analysis in Mentat

30 Samer Hassan 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

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

32 Samer Hassan 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

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

34 Samer Hassan 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

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