article

Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling

Authors:

Celia Gutiérrez,

Juan PavónAuthors Info & Claims

Computational & Mathematical Organization Theory, Volume 16, Issue 4

Pages 416 - 435

https://doi.org/10.1007/s10588-010-9078-y

Published: 01 December 2010 Publication History

Abstract

Agent-based models informed by empirical data are growing in popularity. Many models make extensive use of collected data for the development, initialisation or validation. In parallel, models are growing in size and complexity, generating large amounts of output data. On the other hand, Data Mining is used to extract hidden patterns from large collections of data using different techniques. This work proposes the intense use of Data Mining techniques for the improvement and development of agent-based models. It presents a methodological approach explaining why and when to use Data Mining, with a formal description of each stage of the corresponding process. This is illustrated with a case study, showing the application of the proposed approach step by step.

References

[1]

Ahmad MA, Teredesai A (2006) Modeling spread of ideas in online social networks. In: Proceedings of the fifth Australasian conference on data mining and analystics, vol 61. Australian Computer Society, Inc, Sydney, pp 185-190.

[2]

Balci O (2004) Quality assessment, verification, and validation of modeling and simulation applications. In: WSC '04: proceedings of the 36th conference on winter simulation, pp 122-129. Winter simulation conference.

[3]

Boero R, Squazzoni F (2005). Does empirical embeddedness matter? Methodological issues on agent-based models for analytical social science. J Artif Soc Soc Simul 8(4):6.

[4]

Botía JA, Hernansaez JM, Gómez-Skarmeta AF (2004) Towards an approach for debugging mas through the analysis of acl messages. In: Multiagent system technologies, second German conference, MATES 2004. Lecture notes in computer science, vol 3187. Springer, Berlin, pp 301-312.

[5]

Collier N (2001) Repast: an extensible framework for agent simulation. In: Swarmfest 2000: proceedings of the 4th annual swarm user group meeting, March 11-13, 2000. Utah State University, Logan, Utah, p 17.

[6]

Dean JS, Gumerman GJ, Epstein JM, Axtell RL, Swedlund AC, Parker MT, McCarroll S (2000) Understanding anasazi culture change through agent-based modeling. In: Dynamics in human and primate societies: agent-based modeling of social and spatial processes. Oxford University Press, London, pp 179-205.

Digital Library

[7]

Deffuant G, Huet S, Bousset JP, Henriot J, Amon G, Weisbuch G (2002) Agent based simulation of organic farming conversion in allier departement. Complex Ecosyst Manag, pp 158-189.

[8]

Domingos P, Richardson M (2001) Mining the network value of customers. In: Proceedings of the seventh SIGKDD international conference on knowledge discovery and data mining. ACM, San Francisco, pp 57-66.

[9]

Edmonds B (2001) The use of models--making MABS actually work. In: Multi-agent-based simulation. Springer, Berlin, pp 15-32.

[10]

Galán JM, Izquierdo LR, Izquierdo SS, Santos JI, del Olmo R, López-Paredes A, Edmonds B (2009a) Errors and artefacts in agent-based modelling. J Artif Soc Soc Simul 12(1):1.

[11]

Galán JM, López-Paredes A, del Olmo R (2009b) An agent-based model for domestic water management in valladolid metropolitan area. Water Resour Res 45(5):W05401.

[12]

Gilbert N (2008) Agent-based models. Thousand Oaks, Sage.

[13]

Gilbert N, Troitzsch KG (1999) Simulation for the social scientist, 1st edn. Open University Press, Maidenhead.

[14]

Gostoli U (2008) A cognitively founded model of the social emergence of lexicon. J Artif Soc Soc Simul 11(1):2.

[15]

Hassan S, Antunes L, Arroyo M (2008) Deepening the demographic mechanisms in a data-driven social simulation of moral values evolution. In: David N, Sichman JS (eds) MABS. Lecture notes in artificial intelligence (from the Lecture notes in computer science), vol 5269. Springer, Estoril, pp 167-182.

[16]

Hassan S, Antunes L, Pavón J (2009a) A data-driven simulation of social values evolution. In AAMAS 2009 proceedings, Budapest.

[17]

Hassan S, Antunes L, Pavón J (2009b) Mentat: a data-driven agent-based simulation of social values evolution. In: MABS 2009 proceedings, Budapest. Springer (Springer LNAI. 978-3-642-13553-8_12).

[18]

Hassan S, Pavón J, Antunes L, Gilbert N (2010) Injecting data into agent-based simulation. In: Takadama, K, Deffuant, G, and Cioffi-Revilla, C (eds) The second world congress on social simulation (tentative), Springer Series on Agent Based Social Systems. Springer, Washington. 978-4-431-99781-8_13.

[19]

Heath B, Hill R, Ciarallo F (2009) A survey of agent-based modeling practices (January 1998 to July 2008). J Artif Soc Soc Simul 12(4):9.

[20]

Janssen MA, Ostrom E (2006) Empirically based, agent-based models. Ecol Soc 11(2):37.

[21]

Kennedy C, Theodoropoulos G, Sorge V, Ferrari E, Lee P, Skelcher C (2007) Aimss: an architecture for data driven simulations in the social sciences. In: ICCS '07: proceedings of the 7th international conference on computational science, part I. Springer, Berlin, pp 1098-1105.

[22]

LeBaron B, Arthur WB, Palmer R (1999) Time series properties of an artificial stock market. J Econ Dynam Control 23(9-10):1487-1516.

[23]

Remondino M, Correndo G (2006) MABS validation through repeated executing and data mining analysis. Int J Simul Syst Sci Technol 7(6):10-21.

[24]

Richiardi M, Leombruni R, Saam N, Sonnessa M (2006) A common protocol for agent-based social simulation. J Artif Soc Soc Simul 9(1):15.

[25]

Ripley BD (1987) Stochastic simulation. John Wiley and Sons, New York.

[26]

Sargent RG (2007) Verification and validation of simulation models. In: WSC '07: proceedings of the 39th conference on winter simulation. IEEE Press, New York, pp 124-137.

[27]

Sen S, Sekaran M (1996) Multiagent coordination with learning classifier systems. Springer, Berlin, pp 218-233.

[28]

Steinley D, Brusco M (2008) Selection of variables in cluster analysis: an empirical comparison of eight procedures. Psychometrika 73:125-144.

[29]

Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Elsevier, Amsterdam.

[30]

Yang L, Gilbert N (2008) Getting away from numbers: using qualitative observation for agent-based modeling. Adv Complex Syst 11(2):175-186.

Cited By

Shapiro BCrooks A(2022)Drone strikes and radicalization: an exploration utilizing agent-based modeling and data applied to PakistanComputational & Mathematical Organization Theory10.1007/s10588-022-09364-129:3(415-433)Online publication date: 14-Jul-2022
https://dl.acm.org/doi/10.1007/s10588-022-09364-1
Greasley A(2018)Using analytics with discrete-event simulationProceedings of the 50th Computer Simulation Conference10.5555/3275382.3275395(1-12)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3275382.3275395
Saoud MBoubetra AAttia S(2017)A Multi-Agent Based Modeling and Simulation Data Management and Analysis System for the Hospital Emergency DepartmentInternational Journal of Healthcare Information Systems and Informatics10.4018/IJHISI.201707010212:3(21-36)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.4018/IJHISI.2017070102
Show More Cited By

Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling
1. Information systems
  1. Information systems applications

Recommendations

3DM: Domain-oriented Data-driven Data Mining
Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge ...
3DM: Domain-oriented Data-driven Data Mining
Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge ...
On efficiently summarizing categorical databases

Frequent itemset mining was initially proposed and has been studied extensively in the context of association rule mining. In recent years, several studies have also extended its application to transaction or document clustering. However, most of the ...

Comments

Information & Contributors

Information

Published In

cover image Computational & Mathematical Organization Theory

Computational & Mathematical Organization Theory Volume 16, Issue 4

December 2010

111 pages

ISSN:1381-298X

Issue’s Table of Contents

Copyright © Copyright © 2010 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2010

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Shapiro BCrooks A(2022)Drone strikes and radicalization: an exploration utilizing agent-based modeling and data applied to PakistanComputational & Mathematical Organization Theory10.1007/s10588-022-09364-129:3(415-433)Online publication date: 14-Jul-2022
https://dl.acm.org/doi/10.1007/s10588-022-09364-1
Greasley A(2018)Using analytics with discrete-event simulationProceedings of the 50th Computer Simulation Conference10.5555/3275382.3275395(1-12)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3275382.3275395
Saoud MBoubetra AAttia S(2017)A Multi-Agent Based Modeling and Simulation Data Management and Analysis System for the Hospital Emergency DepartmentInternational Journal of Healthcare Information Systems and Informatics10.4018/IJHISI.201707010212:3(21-36)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.4018/IJHISI.2017070102
Secchi DSeri R(2017)Controlling for false negatives in agent-based modelsComputational & Mathematical Organization Theory10.1007/s10588-016-9218-023:1(94-121)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10588-016-9218-0
Harb HSrour FYorke-Smith N(2011)A case study in model selection for policy engineeringProceedings of the 10th international conference on Advanced Agent Technology10.1007/978-3-642-27216-5_2(3-18)Online publication date: 2-May-2011
https://dl.acm.org/doi/10.1007/978-3-642-27216-5_2

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents