Uncertainty and error

Agent-Based Models of Geographical Systems

Volumn , Issue , 2012, Pages 309-346

  Evans, Andrew ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84906250569     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-90-481-8927-4_15     Document Type: Chapter

References (153)

1. Araujo, M., Pearson, R. G., Thuiller, W., & Erhard, M. (2005). Validation of species-climate impact models under climate change. Global Change Biology, 11, 1504-1513.
2. Ballas, D., Rossiter, D., Thomas, B., Clarke, G. P., & Dorling, D. (2005). Geography matters: Simulating the local impacts of national social policies. York: Joseph Rowntree Foundation.
3. Barnett, V., & Lewis, T. (1994). Outliers in statistical data (3rd ed.). Chichester: Wiley. pp.604.
4. Beck, M. B. (1987). Water quality modeling: A review of the analysis of uncertainty. Water Resource Research, 23(8), 1393-1442.
5. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society B, 57, 289-2300.
6. Beven, K. J., & Binley, A. M. (1992). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6, 279-298.
7. Bjork, B-C., Roos, A., & Lauri, M. (2009). Scientific journal publishing: Yearly volume and open access availability. Information Research 14(1): Paper 391. http://InformationR.net/ir/14-1/paper391.html. Accessed 7 Feb 2011
8. Bobashev, G. V., & Morris, R. J. (2010). Uncertainty and inference in agent-based models. In Proceedings of the 2010 Second International Conference on Advances in System Simulation. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5601895. Accessed 31 Mar 2011.
9. Boyle, D. P., Gupta, H. V., & Sorooshian, S. (2000). Towards improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resources Research, 36, 3663-3674.
10. Brown, J. D. (2004). Knowledge, uncertainty and physical geography: Towards the development of methodologies for questioning belief. Transactions of the Institute of British Geographers NS, 29, 367-381.
11. Brown, J. D. (2010). Prospects for the open treatment of uncertainty in environmental research. Progress in Physical Geography,34(1), 75-100.
12. Cerioli, A., & Farcomeni, A. (2011). Error rates for multivariate outlier detection. Computational Statistics & Data Analysis, 55(1), 544-553.
13. Cho, W. K. T., & Gaines, B. J. (2007). Breaking the (Benford) Law: Statistical fraud detection in Campaign Finance. The American Statistician, 61(3), 218-223.
14. Chowdhury, S., & Sharma, A. (2007). Mitigating parameter bias in hydrological modelling due to uncertainty in covariates. Journal of Hydrology, 340, 197-204.
15. Clancy, D., Tanner, J. E., Mc William, S., & Spencer, M. (2010). Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo. Ecological Modelling, 221, 1337-1347.
16. Clark, J. S. (2005). Why environmental scientists are becoming Bayesians. Ecological Letters, 8, 2-14.
17. Clark, J. S., & Gelfand, A. E. (2006). A future for models and data in environmental science. Trends in Ecology and Evolution, 21(7), 375-380.
18. Collins, M., & Knight, S. (2007). Theme issue ‘Ensembles and probabilities: A new era in the prediction of climate change’. Philosophical Transactions of the Royal Society A, 365, 1957-2191.
19. Cook, J. R., & Stefanski, L. A. (1994). Simulation-extrapolation estimation in parametric measurement error models. Journal of the American Statistical Association, 89(428), 1314-1328.
20. Cornelis, B., & Brunet, S. (2002). A policy-maker point of view on uncertainties in spatial decisions. In W. Shi, P. F. Fisher, & M. F. Goodchild (Eds.), Spatial data quality (pp. 168-185). London: Taylor and Frances.
21. Cornford, D. (2011). Uncertainty and the OGC: An Aston University take. Presented 03/03/2011 at OGC TC Meeting, Universities DWG Session, Bonn. http://www.uncertweb.org/documents. Accessed 1 Mar 2011.
22. Costanza, R. (1989). Model goodness of fit: A multiple resolution procedure. Ecological Modelling, 47, 199-215.
23. Couclelis, H. (2003). The certainty of uncertainty: GIS and the limits of geographic knowledge. Transactions in GIS, 7(2), 165-175.
24. Dassonville, L., Vauglin, F., Jakobsson, A., & Luzet, C. (2002). Quality management, data quality and users, metadata for geographical information. In W. Shi, P. F. Fisher, & M. F. Goodchild (Eds.), Spatial data quality (pp. 202-215). London: Taylor and Frances.
25. Dewulf, A., Craps, M., Bouwen, R., Taillieu, T., & Pahl-Wostl, C. (2005). Integrated management of natural resources: Dealing with ambiguous issues, multiple actors and diverging frames. Water Science & Technology, 52, 115-124.
26. Di Paolo, E. A, Noble, J, & Bullock, S (2000) Simulation models as opaque thought experiments. In Seventh International Conference on Artificial Life (pp. 497-506) Cambridge: MIT Press,. http://eprints.ecs.soton.ac.uk/11455/
27. Drecki, I. (2002). Visualisation of uncertainty in geographical data. In W. Shi, P. F. Fisher, & M. F. Goodchild (Eds.), Spatial data quality (pp. 140-159). London: Taylor and Frances.
28. Dubois, G., & Saisana, M. (2002). Optimal spatial declustering weights — Comparison of methods. In Proceedings of the Annual Conference of the International Associationfor Mathematical Geology, 15-20 Sept Berlin, 479-484. http://composite-indicators.jrc.ec.europa.eu/Document/Optimizing Spatial Declustering Weights - Comparison of Methods.pdf. Accessed 31 Mar 2011.
29. Evans, A. J., & Waters, T. (2007). Mapping vernacular geography: Web-based GIS tools for capturing “fuzzy” or “vague” entities. International Journal ofTechnology, Policy and Management, 7(2), 134-150.
30. Evensen, G. (1992). Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model. Journal of Geophysical Research, 97(C11), 17905-17924.
31. Evensen, G. (1994). Sequential data assimilation with nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 143-162.
32. Faber, M., Manstetten, R., & Proops, J. (1992). Humankind and the environment: An anatomy of surprise and ignorance. Environmental Values, 1, 217-241.
33. Farcomeni, A. (2008). A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion. Statistical Methods in Medical Research, 17, 347-388.
34. Fisher, P., Wood, J., & Cheng, T. (2004). Where is Helvellyn? Fuzziness of multi-scale landscape morphometry. Transactions of the Institute of British Geographers, 29(1), 106-128.
35. Funtowicz, S. O., & Ravetz, J. R. (1990). Uncertainty and quality in science for policy. Dordrecht: Kluwer Academic Publishers.
36. Funtowicz, S. O., & Ravetz, J. R. (1993). Science for the post-normal age. Futures, 25, 739-755.
37. Galán, J. M., Izquierdo, L. R., Izquierdo, S. S., Santos, J. I., del Olmo, R., López-Paredes, A., & Edmonds, B. (2009). Errors and artefacts in agent-based modelling. Journal of Artificial Societies and Social Simulation 12(1): 1. http://jasss.soc.surrey.ac.uk/12/1/1.html. Accessed 25 Mar 2011.
38. Gallagher, M., & Doherty, J. (2007). Parameter estimation and uncertainty analysis for a watershed model. Environmental Modelling and Software, 22, 1000-1020.
39. Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns: Elements of reusable object-oriented software. Reading, Mass: Addison Wesley. pp.416.
40. Gan, E., & Shi, W. (2002). Error metadata management systems. In W. Shi, P. F. Fisher, & M. F. Goodchild (Eds.), Spatial data quality (pp. 251-266). London: Taylor and Frances.
41. George, E. I. (2000). The variable selection problem. Journal of the American Statistical Association, 95(452), 1304-1308.
42. Getis, A. (2007). Reflections on spatial autocorrelation. Reg Sci UrbanEcon, 37, 491-496.
43. Glahn, H., & Lowry, D. (1972). The use of Model Output Statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology, 11, 1203-1211.
44. Gneiting, T., & Raftery, A. E. (2005). Weather forecasting with ensemble methods. Science, 310(5746), 248-249.
45. Green, S. B., & Babyak, M. A. (1997). Control of type I errors with multiple tests constraints in structural equation modeling. Multivariable Behavioural Research, 32, 39-51.
46. Groves, R. M., Fowler, F. J., Jr., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology. Hoboken: Wiley-Blackwell. pp.488.
47. Gupta, H. V. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135-143.
48. Gupta, H. V., Sorooshian, S., & Yapo, P. O. (1998). Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information. Water Resources Research, 34(4), 751-763.
49. Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32, 135-154.
50. Hartley, S., Harris, R., & Lester, P. J. (2006). Quantifying uncertainty in the potential distribution of an invasive species: Climate and the Argentine ant. Ecology Letters, 9, 1068-1079.
51. Hasings, D., & Mc Manus, H. (2004). A framework for understanding uncertainty and its mitigation and exploitation in complex systems. In 2004 Engineering Systems Symposium, 29-31 Mar 2004, MIT. http://esd.mit.edu/symposium/pdfs/papers/hastings.pdf. Accessed 28 February 2011.
52. Hassan, S., Garmendia, L., & Pavon, J. (2010). Introducing uncertainty into social simulation: Using fuzzy logic for agent-based modelling. International Journal of Reasoning-based Intelligent Systems, 2(2), 118-124.
53. Hatton, L. (1997). The T experiments: Errors in scientific software. Computational Science & Engineering, IEEE, 4(2), 27-38.
54. Hayes, B. (2003). A Lucid interval. American Scientist, 91(6), 484-488.
55. Helton, J. C., Johnson, J. D., Salaberry, C. J., & Storlie, C. B. (2006). Survey of sampling based methods for uncertainty and sensitivity analysis. Reliability Engineering and System Safety, 91, 1175-1209.
56. Heppenstall, A. J., Evans, A. J., & Birkin, M. H. (2007). Genetic algorithm optimisation of a multiagent system for simulating a retail market. Environment and Planning B, 34(6), 1051-1070.
57. Heuvelink, G. B. M. (1998). Error propagation in environmental modelling with GIS. London: Taylor and Francis. pp.127.
58. Hornberger, G., & Spear, R. (1981). An approach to the preliminary analysis of environmental systems. Journal of Environmental Management, 7, 7-18.
59. Hyde, R. (2004). Write great code 1: Understanding the machine. San Francisco: No Starch Press. pp. 440.
60. Isaaks, E. H., & Srivastava, R. M. (1990). Applied geostatistics. Oxford: Oxford University Press. pp.592.
61. Izquierdo, L. R., & Polhill, J. G. (2006). Is your model susceptible to floating-point errors? Journal of Artificial Societies and Social Simulation 9(4): 4. http://jasss.soc.surrey.ac.uk/9/4/4.html. Accessed 25 Mar 2011.
62. Janssen, J. A. E. B., Krol, M. S., Schielen, R. M. J., Hoekstra, A. Y., & de Kok, J.-L. (2010). Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models. Ecological Modelling, 221, 1245-1251.
63. JCGM (2008a) Evaluation of measurement data - Supplement 1 to the “Guide to the expression of uncertainty in measurement” - Propagation of distributions using a Monte Carlo method, Joint Committee for Guides in Metrology 100. http://www.bipm.org/utils/common/documents/jcgmJCGM_101_2008_E.pdf. Accessed 15 Feb 2011.
64. JCGM (2008b) Evaluation of measurement data - Guide to the expression of uncertainty in measurement, Joint Committee for Guides in Metrology 100. http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf. Accessed 15 Feb 2011.
65. Johnson, J. B., & Omland, K. S. (2004). Model selection in ecology and evolution. Trends in Ecology & Evolution, 19, 101-108.
66. Kardos, J. D., Moore, A., & Benwell, G. L. (2003). Visualising uncertainty in spatially-referenced attribute data using hierarchical spatial data structures. In Proceedings of the 7th International Conference on GeoComputation. University of Southampton, 8-10 Sept 2003. http://www. geocomputation.org/2003/. Accessed 25 Mar 2011
67. Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42, W03407.
68. Keesman, K., & van Straten, G. (1989). Identification and prediction propagation of uncertainty in models with bounded noise. International Journal of Control, 49, 2259-2269.
69. Keesman, K., & van Straten, G. (1990). Set-membership approach to identification and prediction of lake eutrophication. Water Resources Research, 26, 2643-2652.
70. Kelejian, H., & Prucha, I. (2010). Spatial models with spatially lagged dependent variables and incomplete data. Journal of Geographical Systems, 12(3), 241-257.
71. Kennedy, M. C., & O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society B, 63(3), 425-464.
72. Kiesling, J. B. (undated) Charting Electoral Fraud: Turnout Distribution Analysis as a Tool for Election Assessment. Diplomacy Lessons. http://www.bradykiesling.com/election_fraud_anal-ysis.htm. Accessed 10 Jan 2010.
73. King, G. (1986). How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science, 30, 666-687.
74. Knudsen, C. D., & Fotheringham, A. S. (1986). Matrix comparison, goodness-of-fit and spatial interaction modeling. International Regional Science Review, 10(2), 127-147.
75. Ku, H. (1966). Notes on the use of propagation of error formulas. Journal of Research of National Bureau of Standards C. Engineering and Instrumentation, 70(4), 263-273.
76. Laffan, S. W. (1999). Spatially assessing model error using geographically weighted regression. In Proceedings of the 4th International Conference on GeoComputation. Fredericksburg: Mary Washington College, 5-28 July 1999. http://www.geocomputation.org/1999/. Accessed 25 Mar 2011.
77. Lane, S. N. (2001). Constructive comments on D Massey ‘Space-time, “science” and the relationship between physical geography and human geography’. Transactions of the Institute of British Geographers, 6, 243-256.
78. Lauridsen, J., & Kosfeld, R. (2007). Spatial cointegration and heteroscedasticity. Journal of Geographical Systems, 9(3), 253-265.
79. Legendre, P. (1993). Spatial Autocorrelation: Trouble or New Paradigm? Ecology, 74, 1659-1673.
80. Leith, C. E. (1974). Theoretical skill of Monte Carlo forecasts. Monthly Weather Review, 102(6), 409-418.
81. Li, Y., Brimicombe, A. J., & Li, C. (2007). Agent-based services for validating multi-agent models. In Proceedings of the 9th International Conference on GeoComputation, Maynooth: Eire.
82. Lodwick, W. A. (1989). Developing confidence limits on errors of suitability analyses in geographical information systems. In M. Goodchild & S. Gopal (Eds.), Accuracy of spatial databases (pp. 69-80). London: Taylor and Francis.
83. Lopez, C. (1997). Quality of geographic data - Detection of outliers and imputation of missing values (Unpublished PhD thesis, Universidad ORT, Uruguay). http://www.thedigitalmap. com/~carlos/papers/PhDthesis/phdthesis.pdf. Accessed 1 Mar 2011.
84. Lorenz, E. N. (1963). Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 20, 130-141.
85. Luoto, M., Marmion, M., & Hjort, J. (2010). Assessing spatial uncertainty in predictive geomor-phological mapping: A multi-modelling approach. Computers & Geosciences, 36, 355-361.
86. Lutz, W., Sanderson, W., & Scherbov, S. (1996). Probabilistic population projections based on expert opinion. In W. Lutz (Ed.), The future population of the world. What can we assume today? (pp. 397-428). London: Earthscan Publications. Chapter 16.
87. Lyman, R. L., & Ames, K. M. (2007). On the use of species-area curves to detect the effects of sample size. Journal of Archaeological Science, 34, 1985-1990.
88. Malinaric, S., & Dunsek, P. (2004). Sensitivity coefficients analysis. In Proceedings of Thermophysics2004. http://www.tpl.fpv.ukf.sk/engl_vers/thermophys/2004/Mal-Dur.pdf. Accessed 14 Feb 2011.
89. Malleson, N. (2010). Agent-based modelling of Burglary (Unpublished PhD thesis, School of Geography, University of Leeds, Leeds). http://www.geog.leeds.ac.uk/fileadmin/downloads/school/people/postgrads/n.malleson/thesis-final.pdf. Accessed 1 Mar 2011.
90. Marin, C. M., Guvanasen, V., & Saleem, Z. A. (2003). The 3MRA risk assessment framework - A flexible approach for performing multimedia, multipathway, and multireceptor risk assessments under uncertainty. Human and Ecological Risk Assessment: An International Journal, 9(7), 1655-1677.
91. Martin, D. (2003). Extending the automated zoning procedure to reconcile incompatible zoning systems. International Journal of Geographical Information Science, 17, 181-196.
92. Martin, D., Dorling, D., & Mitchell, R. (2002). Linking censuses through time: problems and solutions Area, 34, 82-91.
93. Matott, L. S., Babendreier, J. E., & Parucker, S. T. (2009). Evaluating uncertainty in integrated environmental models: A review of concepts and tools. Water Resources Research 45: WO6421. http://www.agu.org/journals/wr/wr0906/2008WR007301/2008WR007301.pdf. Accessed 2 Feb 2011.
94. Mc Kay, M. D., Beckman, R. J., & Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21(2), 239-245.
95. Mearns, L. O. (2010). The drama of uncertainty. Climatic Change, 100, 77-85.
96. Mebane, W. R Jr., & Kalinin, K. (2009). Comparative election fraud detection. In Annual Meeting of the American Political Science Association, Toronto 3-6 Sept 2009. http://www-personal. umich.edu/~wmebane/apsa09.pdf. Accessed 10 Jan 2010.
97. Mitchell, S., Beven, K., & Freer, J. (2009). Multiple sources of predictive uncertainty in modeled estimates of net ecosystem CO2 exchange. Ecological Modelling, 220(23), 3259-3270.
98. Monaco, J. H., & Anderson, R. L. (1994). Tai’s formula is the trapezoidal rule. Diabetes Care, 17(10), 1224-1225.
99. Moreno, N., Wang, F., & Marceau, D. J. (2008). An object-based land-use cellular automata model to overcome cell size and neighborhood sensitivity. In Proceedings OF GEOBIA 2008 - Pixels, Objects, Intelligence GEOgraphic Object Based Image Analysis for the 21st Century. Calgary, 5-8 Aug. http://www.isprs.org/proceedings/XXXVIII/4-C1/Sessions/Session6/6753_Marceau_Proc_pap.pdf. Accessed 25 Mar 2011.
100. Morss, R. E., Demuth, J. L., & Lazo, J. K. (2008). Communicating uncertainty in weather forecasts: a survey of the US public. Weather and Forecasting, 23, 974-91.
101. Nagele, P. (2001). Misuse of standard error of the mean (SEM) when reporting variability of a sample. A critical evaluation of four anaesthesia journals. British Journal of Anaesthesia, 90(4), 514-516.
102. National Research Council of the National Academies (NRC). (2006). Completing the forecast: Characterizing and communicating uncertainty for better decisions using weather and climate forecasts. Washington, DC: National Academies Press.
103. Ngo, T. A., & See, L. M. (2012). Calibration and validation of agent-based models of land cover change. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 181-196). Dordrecht: Springer.
104. NIST/SEMATECH (2010). e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/. Accessed 10 Jan 2011.
105. O’Neill, R. V. (1973). Error analysis of ecological models. In D. J. Nelson (Ed.), Radionuclides in ecosystems. CONF-710501 (pp. 898-908). Springfield: National Technical Information Service.
106. O’Neill, R. V., & Rust, B. (1979). Aggregation error in ecological models. Ecological Modelling, 7(2), 91-105.
107. O’Neill, R. V., Gardner, R. H., & Mankin, J. B. (1980). Analysis of parameter error in a nonlinear model. Ecological Modelling, 8, 297-311.
108. Openshaw, S. (1989). Learning to live with errors in spatial databases. In M. Goodchild & S. Gopal (Eds.), Accuracy of spatial databases (pp. 263-276). London: Taylor and Francis.
109. Polhill, J. G., Izquierdo, L. R., & Gotts, N. M. (2006). What every agent-based modeller should know about floating point arithmetic. Environmental Modelling & Software, 21(3), 283-309.
110. Poli, R, Langdon, W. B., and Mc Phee, N. F., with Koza, J. R. (2008). A Field Guide to Genetic Programming. http://www.gp-field-guide.org.uk/. Accessed 28 February 2011.
111. Poulton, E. C. (1989). Bias in quantifyingjudgments. Hove: Erlbaum. pp.328.
112. Powers, A. C. (2005). Simulating patterns of uncertainty in postclassification change detection. In Proceedings of the 8th International Conference on GeoComputation. University of Michigan, 31 July-3 Aug 2005. http://www.geocomputation.org/2005/. Accessed 25 Mar 2011.
113. Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review,133, 1155-1174.
114. Refsgaard, J. C., Henriksen, H. J., Harrar, W. G., Scholten, H., & Kassahun, A. (2005). Quality assurance in model based water management - Review of existing practice and outline of new approaches. Environmental Modelling & Software, 20(10), 1201-1215.
115. Refsgaard, J. C., van der Sluijs, J. P., Etejberg, A. L., & Vanrolleghem, P. A. (2007). Uncertainty in the environmental modeling process - A framework and guidance. Environmental Modelling and Software, 22, 1543-1556.
116. Reinke, K., & Hunter, G. J. (2002). A theory for communicating uncertainty in spatial databases. In W. Shi, P. F. Fisher, & M. F. Goodchild (Eds.), Spatial data quality (pp. 76-101). London: Taylor and Frances.
117. Rogers, J. P., Barbara, D., & Domeniconi, C. (2009). Detecting spatio-temporal outliers with kernels and statistical testing. In 17th International Conference on Geoinformatics. http://dx.doi. org/10.1109/GE0INF0RMATICS.2009.5293481. Accessed 1 Mar 2011.
118. Rowe, W. D. (1977). An anatomy ofrisk. New York: John Wiley and Sons.
119. Saltelli, A., Chan, K., & Scott, E. M. (2000). Sensitivity analysis. Chichester: Wiley. pp.475.
120. Schonfisch, B.,&de Roos, A. (1999). Synchronous and asynchronous updating in cellular automata Biosystems, 51(3), 123-143.
121. Shackley, S., & Wynne, B. (1996). Representing uncertainty in global climate change science and policy: Boundary ordering devices and authority. Science Technology Human Values, 21, 275-302.
122. Smith, L. A., Cuellar, M. C., Du, H., & Judd, K. (2010a). Exploiting dynamical coherence: A geometric approach to parameter estimation in nonlinear models. Physics Letters A, 374, 2618-2623.
123. Smith, A. H. C., Ponci, F., & Monti, A. (2010b). Bounding the dynamic behavior of an uncertain system via polynomial chaos-based simulation. Simulation, 86, 31-40.
124. Smithson, M. (1989). Ignorance and uncertainty: Emergingparadigms. New York: Springer.
125. Spear, R. C. (1970). The application of Kolmogorov-Renyi statistics to problems of parameter uncertainty in systems design. International Journal of Control, 11, 771-778.
126. Spetzler, C. S., & von Holstein, S. (1975). Probability encoding in decision analysis. Management Science, 22(3), 340-358.
127. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit (with Discussion). Journal of the Royal Statistical Society B, 64, 583-639.
128. Suter, G. W., Barnthouse, L. W., & O’Neill, R. V. (1987). Treatment of risk in environmental impact assessment. Environmental Management, 11, 295-303.
129. Tang, S., & Wang, Y. (2001). A parameter estimation program for error-in-variable model. Ecological Modelling, 156, 225-236.
130. Tobler, W. R. (1979). Smooth pycnophylactic interpolation for geographical regions. Journal of the American Statistical Association, 74, 519-530.
131. Toth, Z., & Kalnay, E. (1993). Ensemble forecasting at NMC: The generation of perturbations. Bulletin of American Meteorological Society, 74, 2317-2330.
132. Urban, N. M., & Fricker, T. E. (2010). A comparison of Latin hypercube and grid ensemble designs for the multivariate emulation of an Earth system model. Computers & Geosciences, 36, 746-755.
133. Van der Sluijs, J. P., Potting, J., Risbey, J., van Vuuren, D., de Vries, B., Beusen, A., Heuberger, P., Quintana, S. C., Funtowicz, S., Kloprogge, P., Nuijten, D., Petersen, A. C., & Ravetz, J. (2002). Uncertainty assessment of the IMAGE = TIMER B1 CO2 emissions sScenario, using the NUSAP method. (Report No. 410 200 104). Bilthoven: Dutch National Research Program on Climate Change.
134. Van der Sluijs, J. P., Risbey, J., Kloprogge, P., Ravetz, J. R., Funtowicz, S. O., Quintana, S. C., Pereira, A. G., De Marchi, B., Petersen, A. C., Janssen, P. H. M., Hoppe, R., & Huijs, S. W. F. (2003). RIVM/MNP guidance for uncertainty assessment and communication. http://www. nusap.net/downloads/detailedguidance.pdf. Accessed 14 Feb 2011.
135. van der Wel, F. J. M., van der Gaag, L. C., & Gorte, B. G. H. (1996). Visual exploration of uncertainty in remote sensing classifications. In Proceedings of the 1st International Conference on GeoComputation. Leeds: University of Leeds, 17-19 Sept 1996. http://www.geocomputation. org/1996/. Accessed 25 Mar 2011.
136. van Straten, G. (1985). Analytical methods for parameter-space delimitation and application to shallow-lake phytoplankton-dynamics modeling. Applied Mathematics and Computation, 17, 459-482.
137. Verkuilen, J. (2005). Assigning membership in a fuzzy set analysis. Sociological Methods & Research, 33(4), 462-496.
138. Viard, T., Caumon, G., & Levy, B. (2011). Adjacent versus coincident representations of geospa-tial uncertainty: Which promote better decisions? Computers & Geosciences, 37(4), 511-520.
139. Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., & Gupta, H. V. (2003). Towards reduced uncertainty in conceptual rainfall-runoff modelling: Dynamic identifiability analysis. Hydrological Processes, 17(2), 455-476.
140. Walker, W. W. (1982). A sensitivity and error analysis framework for lake eutrophication modeling. Water Resources Bulletin, 18(1), 53-60.
141. Walker, W. E., Harremoes, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P., et al. (2003). Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1), 5-18.
142. Warren, H. S., Jr. (2002). Hacker’s delight. Boston: Addison Wesley. pp. 306.
143. Williams, M., Cornford, D., Bastin, L., & Ingram, B. (2008). UncertML: an XML schema for exchanging uncertainty. In Proceedings of GISRUK 2008. http://www.uncertml.org/publications. php. Accessed 1 Mar 2011
144. Wooldridge, M. (2009). An introduction to multiagent systems (2nd ed.). Chichester: Wiley. pp. 484.
145. Wyss, G. D., & Jorgensen, K. H. (1998). A user’s guide to LHS: Sandia’s Latin Hypercube Sampling Software. SAND98-0210 Albuquerque: Sandia National Laboratories.
146. Young, P. C. (2002). Advances in real-time flood forecasting. Philosophical Transactions of the Royal Society of London A, 360, 1433-1450.
147. Young, P. C., Parkinson, S., & Lees, M. J. (1996). Simplicity out of complexity in environmental modelling: Occam’s razor revisited. Journal of Applied Statistics, 23, 165-210.
148. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-1. Information Sciences, 8, 199-249.
149. Zadeh, L. A. (1976). A fuzzy-algorithmic approach to the definition of complex or imprecise concepts. International Journal of Man-Machine Studies, 8(3), 249-291.
150. Zadeh, L. A. (2005). Toward a generalized theory of uncertainty (GTU) - An outline. Information Sciences, 172, 1-40.
151. Zellner, M. L. (2008). Embracing complexity and uncertainty: The potential of agent-based modeling for environmental planning and policy. Planning Theory & Practice, 9(4), 437-457.
152. Zhang, J., & Goodchild, M. (2002). Uncertainty in geographical information (p. 266). London: Taylor and Francis.
153. Zizek, S. (2004). What Rumsfeld doesn’t know that he knows about Abu Ghraib. In These Times, 21 May 2004. http://www.lacan.com/zizekrumsfeld.htm. Accessed 10 Jan 2010.