Modeling Uncertainties of Technological Learning with Stochastic Optimization

摘要：

Stochastic optimization is a popular method for analyzing techno-economic system with uncertain technological learning. To date, optimization models of uncertain endogenous technological change models commonly add cost resulting from overestimating technological learning rates into an objective function with a subjective risk factor. This paper argues that applying risk-constrained cost minimization to uncertain endogenous technological change models could be more practicable for decisionmaking support since it is more explicit and tangible to set an acceptable extra cost than to set a pure subjective risk factor value. This paper explores two risk-constrained methods to analyze a simplified energy system with three technologies.

关键词： technological learning stochastic optimization

作者: Tieju Ma Yoshiteru Nakamori

作者单位: School of Knowledge Science, Japan Advanced Institute of Science and Technology 1-1 Asahidai, Nomi, Ishikawa 923-1292 Japan

会议类型: 国际会议

会议名称: The 9th International Symposium on Knowledge and Systems Sciences,The 4th Asia-Pacific International Conference on Knowledge Management(第九届国际知识与系统科学学术年会暨第四届亚太国际知识管理年会)

会议地点: 广州

会议语种:英文

页码: 37-42

在线出版日期: 2008-12-11（万方平台首次上网日期，不代表论文的发表时间）