THREE-WAY GOVERNMENT DECISION ANALYSIS WITH DECISION-THEORETIC ROUGH SETS

By considering the risks in policy making procedure, a three-way decision approach based on the decision-theoretic rough set model is adopted to risk government decision-making. A three-way decision is made based on a pair of thresholds on conditional probabilities. A positive rule makes a decision of executing, a negative rule makes a decision of non-executing, and a boundary rule makes a decision of deferment. The loss functions are used to calculate the required two thresholds to describe the decision risk with the Bayesian decision procedure. A case study of government petroleum risk investment demonstrates the proposed method.

[1]  Yiyu Yao,et al.  A Decision Theoretic Framework for Approximating Concepts , 1992, Int. J. Man Mach. Stud..

[2]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[3]  Jie Lu.  A Model for Evaluating E-Commerce Based on Cost/Benefit and Customer Satisfaction , 2003, Inf. Syst. Frontiers.

[4]  Jie Lu,et al.  Cost benefit factor analysis in e‐services , 2003 .

[5]  Denis José Schiozer,et al.  Risk Analysis Applied To Petroleum Exploration And Production: An Overview , 2004 .

[6]  Jie Lu,et al.  Development, Distribution and Evaluation of Online Tourism Services in China , 2004, Electron. Commer. Res..

[7]  Dominik Slezak,et al.  Rough Sets and Bayes Factor , 2005, Trans. Rough Sets.

[8]  Dominik Slezak,et al.  The investigation of the Bayesian rough set model , 2005, Int. J. Approx. Reason..

[9]  Chenggang Bai,et al.  E-Service Cost Benefit Evaluation and Analysis , 2007, E-Service Intelligence.

[10]  Rob Goudey,et al.  Do statistical inferences allowing three alternative decisions give better feedback for environmentally precautionary decision-making? , 2007, Journal of environmental management.

[11]  Da Ruan,et al.  E-Service Intelligence: An Introduction , 2007, E-Service Intelligence.

[12]  Jie Lu,et al.  Fuzzy bilevel Programming: Multi-Objective and Multi-Follower with Shared Variables , 2008, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[13]  Da Ruan,et al.  Intelligent decision and policy making support systems , 2008 .

[14]  Yiyu Yao,et al.  Probabilistic rough set approximations , 2008, Int. J. Approx. Reason..

[15]  Ronald R. Yager,et al.  Risk Modeling for Policy Making , 2008 .

[16]  Wojciech Ziarko,et al.  Probabilistic approach to rough sets , 2008, Int. J. Approx. Reason..

[17]  Jie Lu,et al.  Fuzzy-Set Decision Support for a Belgian Long-Term Sustainable Energy Strategy , 2008 .

[18]  Chenggang Bai,et al.  Cost-benefit factor analysis in e-services using bayesian networks , 2009, Expert Syst. Appl..

[19]  Joseph P. Herbert,et al.  Criteria for choosing a rough set model , 2009, Comput. Math. Appl..

[20]  Yi-Ming Wei,et al.  A model based on stochastic dynamic programming for determining China's optimal strategic petroleum reserve policy , 2009 .

[21]  Thomas Moutos,et al.  EMU and the Greek crisis: The political-economy perspective , 2010 .

[22]  Lei Zhu,et al.  A real options based model and its application to China's overseas oil investment decisions , 2010 .

[23]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[24]  Wuyi Yue,et al.  Dynamic risk management in petroleum project investment based on a variable precision rough set model , 2010 .

[25]  Da Ruan,et al.  Probabilistic model criteria with decision-theoretic rough sets , 2011, Inf. Sci..

[26]  Yiyu Yao,et al.  Three-way Investment Decisions with Decision-theoretic Rough Sets , 2011, Int. J. Comput. Intell. Syst..

[27]  Dun Liu,et al.  A Multiple-category Classification Approach with Decision-theoretic Rough Sets , 2012, Fundam. Informaticae.