The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - Nov.-Dec. (2011 vol.28)
pp: 56-61
Joshua Introne , MIT Center for Collective Intelligence
Robert Laubacher , MIT Center for Collective Intelligence
Thomas Malone , MIT Center for Collective Intelligence
ABSTRACT
Models play a central role for climate change policy-makers, but they're often so complex and computationally demanding that experts must run them and interpret their results. This reduces stakeholders' ability to explore alternative scenarios, increases perceptions of model complexity and opacity, and can ultimately reduce public confidence . The Radically Open Modeling Architecture (ROMA) is a Web service designed to address these problems by providing two core functionalities: the creation and running of surrogate simulations, which are fast approximations of much larger integrated assessment models, and a componentized view of models and stored model runs that allow clients to combine components to create new executable composite models.
INDEX TERMS
modeling, modeling methodologies, simulation support systems, domain-specific architectures, earth science, atmospheric sciences
CITATION
Joshua Introne, Robert Laubacher, Thomas Malone, "Enabling Open Development Methodologies in Climate Change Assessment Modeling", IEEE Software, vol.28, no. 6, pp. 56-61, Nov.-Dec. 2011, doi:10.1109/MS.2011.115
REFERENCES
1. M. Matthies, C. Giupponi, and B. Ostendorf, "Environmental Decision Support Systems: Current Issues, Methods and Tools," Environmental Modelling & Software, vol. 22, no. 2, 2007, pp. 123–127.
2. I.S. Mayer et al., "Collaborative Decision Making for Sustainable Urban Renewal Projects: A Simulation-Gaming Approach," Environment and Planning B: Planning and Design, vol. 32, no. 3, 2005, pp. 403–423.
3. B. Friedman et al., "Laying the Foundations for Public Participation and Value Advocacy: Interaction Design for a Large-Scale Urban Simulation," Proc. 2008 Int'l Conf. Digital Govt. Research, Digital Gov't Soc. North America, 2008, pp. 305–314.
4. T.W. Malone, R. Laubacher, and C. Dellarocas, "The Collective Intelligence Genome," Sloan Management Rev., vol. 51, no. 3, 2010, pp. 21–31.
5. J. Introne et al., "The Climate CoLab: Large Scale Model-Based Collaborative Planning," Proc. 2011 Conf. Collaboration Technologies and Systems, IEEE CS Press, 2011, pp. 40–47.
6. L. Hong and S.E. Page, "Groups of Diverse Problem Solvers Can Outperform Groups of High-Ability Problem Solvers," Proc. Nat'l Academy of Sciences of the United States of America, Nat'l Academy of Sciences, vol. 101, no. 46, 2004, pp. 16385–16389.
7. D. Gorissen et al., "A Surrogate Modeling and Adaptive Sampling Toolbox for Computer-Based Design," J. Machine Learning Research, vol. 11, 2010, pp. 2051–2055.
8. T. Fiddaman et al., C-ROADS Simulator Reference Guide, Climate CoLab, 2011.
9. P.W.D. Nordhaus, A Question of Balance: Weighing the Options on Global Warming Policies, Yale Univ. Press, 2008.
10. M.L. Parry, O.F. Canziani, and J.P. Palutikof, "Technical Summary. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the 4th Assessment Report of the Intergovernmental Panel on Climate Change," Report of the Intergovernmental Panel on Climate Change, M.L. Parry et al., eds., Cambridge Univ. Press, 2007, pp. 23–78.
11. N.H. Stern, The Economics of Climate Change: The Stern Review, Cambridge Univ. Press, 2007.
12. L. Clarke et al., "International Climate Policy Architectures: Overview of the EMF 22 International Scenarios," Energy Economics, vol. 31, no. 2, 2009, pp. S64–S81.
21 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool