摘要:

A method of scenario computing is developed for modeling systems with deep uncertainty. The method consists three complementary parts: hybrid modelling, diverse computing, and interactive validation. Hybrid modelling is to dynamically develop models with merging historical knowledges and observed information. Diversity computation is to simulate multiple plausible scenarios about system future. Interactive validation helps scenario computing process being on the right way instead of deviating. Two cases are provided in this paper applying scenario computing, one is earthquake and the other is driving and transportation. The results show good performance of scenario computing method in modeling uncertainty systems. © 2018, The Editorial Board of Journal of System Simulation. All right reserved.

附注:

Export Date: 9 November 2022CODEN: XFXUFReferences: Agusdinata, B., Exploratory modeling and analysis: a promising method to deal with deep uncertainty (2008), pp. 1-2. , TU Delft, Holland: TRAIL Research School (Delft University of Technology); Kwakkel, J.H., Walker, W.E., Marchau, V.A.W.J., From predictive modeling to exploratory modeling: How to use non-predictive models for decision making under deep uncertainty (2010) Proceedings of Uncertainty and Robustness in Planning and Decision Making. Portugal, pp. 1-10; Funtowicz, S.O., Ravetz, J.R., Uncertainty and quality in science for policy (1990), p. 15. , Berlin, Germany: Springer Science & Business Media; Walker, W.E., Harremoës, P., Rotmans, J., Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support (2003) Integrated Assessment, 4 (1), pp. 5-17; Walker, W.E., Lempert, R.J., Kwakkel, J.H., Deep uncertainty, in Encyclopedia of operations research and management science (2013), pp. 395-402. , Berlin, Germany: Springer; Lempert, R.J., Shaping the next one hundred years: new methods for quantitative, long-term policy analysis (2003), p. 12. , USA: Rand Corporation; Ben-Haim, Y., Info-gap decision theory: decisions under severe uncertainty (2013) Ecological Modelling, 211 (1), p. 249; Kwakkel, J.H., Pruyt, E., Exploratory Modeling and Analysis, an approach for model-based foresight under deep uncertainty (2013) Technological Forecasting and Social Change, 80 (3), pp. 419-431; Lempert, R.J., A new decision sciences for complex systems (2002) Proceedings of the National Academy of Sciences, 99 (10), pp. 7309-7313; Makridakis, S., Hogarth, R.M., Gaba, A., Forecasting and uncertainty in the economic and business world (2009) International Journal of Forecasting, 25 (4), pp. 794-812; Kwakkel, J.H., Haasnoot, M., Walker, W.E., Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world (2015) Climatic Change, 132 (3), pp. 373-386; Walker, W.E., Haasnoot, M., Kwakkel, J.H., Adapt or perish: a review of planning approaches for adaptation under deep uncertainty (2013) Sustainability, 5 (3), pp. 955-979; Gangqiao, W., Yi, L., Pan, Y., Study on decision- making method for complex crisis (2015) Systems Engineering-Theory & Practice, 35 (10), pp. 2449-2458. , 王刚桥, 刘奕, 杨盼, 等. 面向突发事件的复杂系统应急决策方法研究[J]. 系统工程理论与实践, 2015, 35(10): 2449-2458; Hudson, A., Tilley, D.R., Assessment of uncertainty in energy evaluations using Monte Carlo simulations (2014) Ecological Modelling, 271, pp. 52-61; Lempert, R.J., Groves, D.G., Popper, S.W., A general, analytic method for generating robust strategies and narrative scenarios (2006) Management Science, 52 (4), pp. 514-528; Guzzi, R., Data Assimilation: Mathematical Concepts and Instructive Examples (2016), pp. 25-28. , Berlin, Germany: Springer; Houtekamer, P., Zhang, F., Review of the ensemble Kalman filter for atmospheric data assimilation (2016) Monthly Weather Review, 144 (12), pp. 4489-4532; Poli, P., Hersbach, H., Dee, D.P., ERA-20C: An atmospheric reanalysis of the twentieth century (2016) Journal of Climate, 29 (11), pp. 4083-4097; Jewell, C.P., Brown, R.G., Bayesian data assimilation provides rapid decision support for vector-borne diseases (2015) Journal of the Royal Society Interface, 12 (108). , S1742-5689; Hao, Z., Aghakouchak, A., Nakhjiri, N., Global integrated drought monitoring and prediction system (2014) Scientific Data, 1. , S2052-4463; Reichle, R.H., Lannoy, G.J.M.D., Forman, B.A., Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS (2014) Surveys in Geophysics, 35 (3), pp. 577-606; Bankes, S., Exploratory modeling for policy analysis (1993) Operations Research, 41 (3), pp. 435-449; Gazis, D.C., Herman, R., Rothery, R.W., Nonlinear follow-the-leader models of traffic flow (1961) Operations Research, 9 (4), pp. 545-567; Treiber, M., Hennecke, A., Helbing, D., Congested traffic states in empirical observations and microscopic simulations (2000) Physical Review E Statistical Physics Plasmas Fluids & Related Interdisciplinary Topics, 62 (2), pp. 1805-1824; Gipps, P.G., A behavioural car-following model for computer simulation (1981) Transportation Research Part B Methodological, 15 (2), pp. 105-111; Kesting, A., Treiber, M., Calibrating Car-Following Models using Trajectory Data: Methodological Study (2008) Transportation Research Record, 2088 (1), pp. 148-156; Tampère, C.M.J., Human-kinetic multiclass traffic flow theory and modelling: with application to advanced driver assistance systems in congestion (2004), pp. 185-212. , TU Delft, Holland: TRAIL Research School (Delft University of Technology)UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064503238&doi=10.1...