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Agents in Space: Validating ABM-GIS Models

  • Kristoffer Wikstrom
  • Hal Nelson
  • Zining YangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)

Abstract

The purpose of this paper is to spatially validate an agent-based predictive analytics model of energy siting policy in a techno-social space. This allows us to simulate the multitude of human factors at each level (e.g. individual, county, region, and so on). Energy infrastructure siting is a complex and contentious process that can have major impacts on citizens, communities, and society as a whole. Furthermore, the process is sensitive to varying degrees of human input, of differing complexity, at multiple levels. When it comes to validating ABMs, the virtual cornucopia of techniques can easily confuse the modeler. As useful as historical data validation is, it seems to be underutilized, most likely due to the fact that it is hard to find data suitable data for many models. For the purpose of In-Site, historical data availability is excellent due to Environmental Impact Assessments (EIA) providing us with citizen and community based organization (CBO) preferences, and regulatory decisions being public. For the model, citizen and CBO preferences were decided by coding comments on the EIA procedure so as to allow for quantitative analysis, and then geocoding the locations of the commenters. The end results of this is that, we can literally overlay our simulation results with the actual, real world, results of the historical project. This will allow for a high degree of confidence in the validation procedure, as well as the ability to deal with the complexity of the networks of human interactions.

Keywords

Agent-based model GIS Energy infrastructure siting Community based organization Validation 

References

  1. 1.
    World Health Organization: Hidden Cities: unmasking and overcoming health inequities in urban settings. The WHO Centre for Health Development, Kobe, Japan, Chap. 1, p. 4 (2010)Google Scholar
  2. 2.
    Nelson, H., Cain, N., Yang, Z.: All politics are spatial: integrating an agent-based decision support model with spatially explicit landscape data. In: Campbell, H., et al. (eds.) Rethinking Environmental Justice in Sustainable Cities, pp. 168–189. Routledge Press, Abingdon (2015)Google Scholar
  3. 3.
    Johnston, K.M.: Agent Analyst. ESRI Press, Redlands (2013)Google Scholar
  4. 4.
    Duong, D.: Verification, validation, and accreditation (VV&A) of social simulations (2010)Google Scholar
  5. 5.
    Galán, J.M., Izquierdo, L.R., Izquierdo, S.S., Santos, J.I., del Olmo, R., López-Paredes, A., Edmonds, B.: Errors and artefacts in agent-based modelling. J. Artif. Soc. Soc. Simul. 12(1), 1 (2009). http://jasss.soc.surrey.ac.uk/12/1/1.html
  6. 6.
    Southern California Edison: Project Timeline. https://www.sce.com/wps/portal/home/about-us/reliability/upgrading-transmission/TRTP-4-11. Accessed 28 Feb 2018
  7. 7.
    Sargent, R.G.: Validation and verification of simulation models. In: Proceedings of the 2004 Simulation Conference, Winter, vol. 1. IEEE (2004)Google Scholar
  8. 8.
    Brown, D.G., Page, S., Riolo, R., Zellner, M., Rand, W.: Path dependence and the validation of agent-based spatial models of land use. Int. J. Geogr. Inf. Sci. 19(2), 153–174 (2005).  https://doi.org/10.1080/13658810410001713399CrossRefGoogle Scholar
  9. 9.
    Brown, D.G., Page, S., Riolo, R., Zellner, M., Rand, W.: Path dependence and the validation of agent-based spatial models of land use. Int. J. Geogr. Inf. Sci. 19(2), 153 (2005).  https://doi.org/10.1080/13658810410001713399CrossRefGoogle Scholar
  10. 10.
    Pontius, R.G.: Quantification error versus location error in comparison of categorical maps. Photogram. Eng. Remote Sens. 66, 1011–1016 (2000)Google Scholar
  11. 11.
    Pontius, R.G.: Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogram. Eng. Remote Sens. 68, 1041–1049 (2002)Google Scholar
  12. 12.
    Costanza, R.: Model goodness of fit: a multiple resolution procedure. Ecol. Model. 47, 199–215 (1989)CrossRefGoogle Scholar
  13. 13.
    Crooks, A.T., Heppenstall, A.J.: Introduction to agent-based modelling. In: Heppenstall, A.J., Crooks, A.T., See, L.M., Batty, M. (eds.) Agent-Based Models of Geographical Systems, pp. 85–105 (2012). Chap. 5Google Scholar
  14. 14.
    Batty, M., Torrens, P.M.: Modelling and prediction in a complex world. Futures 37(7), 745–766 (2005)CrossRefGoogle Scholar
  15. 15.
    Ngo, T.A., See, L.M.: Calibration and validation of agent-based models of land cover change. In: Heppenstall, A.J., Crooks, A.T., See, L.M., Batty, M. (eds.) Agent-Based Models of Geographical Systems, pp. 181–196 (2012)Google Scholar
  16. 16.
    Malerba, F., Orsenigo, L.: Innovation and market structure in the dynamics of the pharmaceutical industry and biotechnology: towards a history-friendly model. Ind. Corp. Change 11(4), 667–703 (2002)CrossRefGoogle Scholar
  17. 17.
    Malerba, F., Nelson, R., Orsenigo, L., Winter, S.: History-friendly’ models of industry evolution: the computer industry. Ind. Corp. Change 8(1), 3–40 (1999)CrossRefGoogle Scholar
  18. 18.
    Malerba, F., Nelson, R., Orsenigo, L., Winter, S.: History-friendly’ models of industry evolution: the computer industry. Ind. Corp. Change 8(1), 3 (1999)CrossRefGoogle Scholar
  19. 19.
    Windrum, P., Fagiolo, G., Moneta, A.: Empirical validation of agent-based models: alternatives and prospects. J. Artif. Soc. Soc. Simul. 10(2), 8 (2007)Google Scholar
  20. 20.
    Windrum, P., Fagiolo, G., Moneta, A.: Empirical validation of agent-based models: alternatives and prospects. J. Artif. Soc. Soc. Simul. 10(2), 12 (2007)Google Scholar
  21. 21.
    Abdollahian, M., Yang, Z., Nelson, H.: Techno-social energy infrastructure siting: sustainable energy modeling programming (SEMPro). J. Artif. Soc. Soc. Simul. 16(3), 6 (2013)CrossRefGoogle Scholar
  22. 22.
    Anselin, L., Syabri, I., Kho, Y.: GeoDa: an introduction to spatial data analysis. Geogr. Anal. 38(1), 5–22 (2006)CrossRefGoogle Scholar
  23. 23.
    Windrum, P., Fagiolo, G., Moneta, A.: Empirical validation of agent-based models: alternatives and prospects. J. Artif. Soc. Soc. Simul. 10(2), 11 (2007)Google Scholar
  24. 24.
    Werker, C., Brenner, T.: Empirical Calibration of Simulation Models, Papers on Economics and Evolution # 0410. Max Planck Institute for Research into Economic Systems, Jena (2004)Google Scholar
  25. 25.
    Law, A.M., Kelton, W.D.: Simulation Modeling and Analysis. McGraw-Hill, New York (1991)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Portland State UniversityPortlandUSA
  2. 2.Claremont Graduate UniversityClaremontUSA

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