skip to main content
10.1145/2637748.2638410acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesi-knowConference Proceedingsconference-collections
research-article

Visual access to an agent-based simulation model to support political decision making

Authors Info & Claims
Published:16 September 2014Publication History

ABSTRACT

Decision making in the field of policy making is a complex task. On the one hand conflicting objectives influence the availability of alternative solutions for a given problem. On the other hand economic, social, and environmental impacts of the chosen solution have to be considered. In the political context, these solutions are called policy options. To tackle societal problems a thorough analysis of policy options needs to be executed before a policy can be put into practice. Computational simulation is a method considered for measuring the impacts of policy options. However, due to their complexity, the underlying models and their output may be difficult to access by decision makers. In this work, we present a visual-interactive interface for an agent-based simulation model that enables decision makers to evaluate the impacts of alternative policy options in the field of regional energy planning. The decision maker can specify different subsidy strategies for supporting public photovoltaic installations as input and evaluate their impact on the actual adoption via the simulation output. We show the usability and usefulness of the visual interface in a real-world example evolved from the European research project ePolicy.

References

  1. S. Afzal, R. Maciejewski, and D. S. Ebert. Visual analytics decision support environment for epidemic modeling and response evaluation. In Proc. of VAST, pages 191--200. IEEE, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  2. G. Andrienko, N. Andrienko, S. Bremm, T. Schreck, T. von Landesberger, P. Bak, and D. Keim. Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns. In VGTC Conf. on Visualization, pages 913--922. Eurographics, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Bernard, T. Ruppert, M. Scherer, J. Kohlhammer, and T. Schreck. Content-based layouts for exploratory metadata search in scientific research data. In JCDL, pages 139--148, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. Booshehrian, T. Möller, R. M. Peterman, and T. Munzner. Vismon: Facilitating analysis of trade-offs, uncertainty, and sensitivity in fisheries management decision making. CGF, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Bremm, T. von Landesberger, J. Bernard, and T. Schreck. Assisted descriptor selection based on visual comparative data analysis. In VGTC Conf. on Visualization, pages 891--900. Eurographics, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. R. J. Crouser and D. Kee. Two visualization tools for analyzing agent-based simulations in political science. IEEE Comp. Graph. and App., pages 67--77, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. S. Few. Now You See it: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Gavanelli, M. Milano, A. Holland, and B. O'Sullivan. What-if analysis through simulation-optimization hybrids. In K. G. Troitzsch, M. Möhring, and U. Lotzmann, editors, ECMS, 2012.Google ScholarGoogle Scholar
  9. G. N. Gilbert. Agent-based models. Quantitative applications in the social sciences. Sage, 2008.Google ScholarGoogle Scholar
  10. D. Guo, M. Gahegan, A. M. MacEachren, and B. Zhou. Multivariate analysis and geovisualization with an integrated geographic knowledge discovery approach. Journal of CaGIS, 32(2):113--132, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  11. M. Howlett, M. Ramesh, and A. Perl. Studying Public Policy: Policy Cycles and Policy Subsystems. Oxford University Press, 1995.Google ScholarGoogle Scholar
  12. P. G. Johnson, T. Balke, and L. Kotthof. Integrating optimisation and agent-based modelling. In European Conference on Modelling and Simulation, 2014 (accepted for publication).Google ScholarGoogle ScholarCross RefCross Ref
  13. D. A. Keim, J. Kohlhammer, G. Ellis, and F. Mansmann. Mastering the Information Age - Solving Problems with Visual Analytics. EG, 2010.Google ScholarGoogle Scholar
  14. J. Kohlhammer, K. Nazemi, T. Ruppert, and D. Burkhardt. Toward visualization in policy modeling. Comp. Graph. and App., pages 84--89, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. D. Kornhauser, U. Wilensky, and W. Rand. Design guidelines for agent based model visualization. J. of Artificial Societies and Social Simulation, 12(2), 2009.Google ScholarGoogle Scholar
  16. G. Pölzlbauer, M. Dittenbach, and A. Rauber. Advanced visualization of self-organizing maps with vector fields. Neural Netw., 19(6):911--922, July 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. T. Ruppert, J. Bernard, and J. Kohlhammer. Bridging knowledge gaps in policy analysis with information visualization. In EGOV/ePart Ongoing Research, volume 221 of LNI, pages 92--103. GI, 2013.Google ScholarGoogle Scholar
  18. B. Shneiderman. The eyes have it: a task by data type taxonomy for information visualizations. In IEEE Symposium on Visual Languages, pages 336--343, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Telea and J. J. van Wijk. Simplified representation of vector fields. In Proc. of the Conference on Visualization '99, pages 35--42. IEEE, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Unger and H. Schumann. Visual support for the understanding of simulation processes. In PacificVis, pages 57--64, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Visual access to an agent-based simulation model to support political decision making

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Other conferences
              i-KNOW '14: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business
              September 2014
              262 pages
              ISBN:9781450327695
              DOI:10.1145/2637748

              Copyright © 2014 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 16 September 2014

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Acceptance Rates

              i-KNOW '14 Paper Acceptance Rate25of73submissions,34%Overall Acceptance Rate77of238submissions,32%

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader