A mathematical model for evaluation of intelligence products value

References

• Albertetti, F., Grossrieder, L., Ribaux, O. and K. Stoffel. (2016). Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation. Applied Soft Computing, 40; 441-454. doi: 10.1016/j.asoc.2015.12.004
   Google Scholar
• Akram, M. and S. Nawaz. (2015). Operations on soft graphs. Fuzzy information and engineering, 7(4); 423-449. doi: 10.1016/j.fiae.2015.11.003
   Web of Science ®Google Scholar
• Barnes, A. (2016). Making intelligence analysis more intelligent: Using numeric probabilities. Intelligence and National Security, 31(3); 327-344. doi: 10.1080/02684527.2014.994955
   Web of Science ®Google Scholar
• Breakspear, A. (2013). A New Definition of Intelligence. Intelligence and National Security 28(5); 678–693. doi: 10.1080/02684527.2012.699285
   Google Scholar
• Brei, W. S. (1996). Getting Intelligence Right: The Power of Logical Procedure. Occasional Paper #2, Joint Military Intelligence College (JMIC).
   Google Scholar
• Camacho-Collados, M. and F. Liberatore. (2015) A Decision Support System for predictive police patrolling. Decision Support Systems, 75; 25-37. doi: 10.1016/j.dss.2015.04.012
   Web of Science ®Google Scholar
• Chang, J. R., C. H. Cheng, and L. S. Chen. (2007): A fuzzy-based military officer performance appraisal system. Applied Soft Computing, 7(3); 936-945. doi: 10.1016/j.asoc.2006.03.003
   Web of Science ®Google Scholar
• Chen, C.W., Liou, T.S. (2006). Evaluation and analysis of investment alternatives with different economic lives using fuzzy logic. Journal of Interdisciplinary Mathematics, 9(1); 77-97. doi: 10.1080/09720502.2006.10700429
   Google Scholar
• Chen, D., Tsang, E. C. C., Yeung, D. S. and Wang, X. (2005). The parameterization reduction of soft sets and its applications. Computers & Mathematics with Applications. 49 (5-6); 757-763.
   Google Scholar
• Chen, S. M. (1996). Evaluating weapon systems using fuzzy arithmetic operations. Fuzzy Sets and Systems, 77(3); 265–276. doi: 10.1016/0165-0114(95)00096-8
   Web of Science ®Google Scholar
• Cheng, C. H. (1999). Evaluating weapon systems using ranking fuzzy numbers. Fuzzy Sets and Systems, 107(1); 25–35. doi: 10.1016/S0165-0114(97)00348-5
   Web of Science ®Google Scholar
• Chrysafis K. A. and Papadopoulos B. K. (2009). On theoretical pricing of options with fuzzy estimators. Journal of Computational and Applied Mathematics, 223; 552–566. doi: 10.1016/j.cam.2007.12.006
   Web of Science ®Google Scholar
• Dhami, M. K., Mandel, D. R., Mellers, B. A. and P. E. Tetlock. (2015). Improving intelligence analysis with decision science. Perspectives on Psychological Science, 10(6); 753-757. doi: 10.1177/1745691615598511
   Web of Science ®Google Scholar
• Dockery, J. and E. Murray. (1987) A Fuzzy Approach to Aggregating Military Assessments. International Journal of Approximate Reasoning, 1(3); 251-271. doi: 10.1016/S0888-613X(87)80001-4
   Google Scholar
• Feng, L., Hao, S., Zhao, Y. and Zhang. C. (2016). Time prediction of serial criminals based on commensurability, Journal of Interdisciplinary Mathematics, 19(4), 635-643. doi: 10.1080/09720502.2016.1179484
   Web of Science ®Google Scholar
• Friedman, J. A. and R. Zeckhauser. (2012). Assessing Uncertainty in Intelligence. Intelligence and National Security, 27(6); 824-847. doi: 10.1080/02684527.2012.708275
   Google Scholar
• Friedman, J. A. and R. Zeckhauser. (2015). Handling and Mishandling Estimative Probability: Likelihood, Confidence, and the Search for Bin Laden. Intelligence and National Security, 30(1); 77-99. doi: 10.1080/02684527.2014.885202
   Web of Science ®Google Scholar
• Friedman, J. A. and R. Zeckhauser. (2016). Why Assessing Estimative Accuracy is Feasible and Desirable. Intelligence and National Security, 31(2); 178-200. doi: 10.1080/02684527.2014.980534
   Web of Science ®Google Scholar
• Gau, W.L. and Buehrer, D.J. (1993). Vague sets. IEEE Transactions on Systems, Man, and Cybernetics, 23 (2); 610-614. doi: 10.1109/21.229476
   Web of Science ®Google Scholar
• Goldman, J. (2006), Words of Intelligence A Dictionary. The Scarecrow Press.
   Google Scholar
• Goodman, M.S. and Omand, D. (2008). Teaching Intelligence Analysts in the UK What Analysts Need to Understand: The King’s Intelligence Studies Program, Studies in Intelligence, 52(4); 1-12.
   Google Scholar
• Heuer R. J. (1999). Psychology of Intelligence Analysis. Washington, D.C.: Center for the Study of Intelligence, Central Intelligence Agency.
   Google Scholar
• Ho, E. H., Budescu, D. V., Dhami, M. K. and D. R. Mandel. (2015). Communicating uncertainty effectively: Lessons from global climate change and intelligence analysis domains. Behavioral Science & Policy, 1(2); 43–55.
   Google Scholar
• Javorsek, D. II. and J. G. Schwitz. (2014). Probing Uncertainty, Complexity, and Human Agency in Intelligence. Intelligence and National Security, 29(5); 639-653. doi: 10.1080/02684527.2013.834218
   Google Scholar
• Jones, R. E. T., Connors, E. S., Mossey, M. E., Hyatt, J. R., Hansen, N. J., Endsley, M. R. (2011). Using fuzzy cognitive mapping techniques to model situation awareness for army infantry platoon leaders, Computational and Mathematical Organization Theory, 17(3); 272-295. doi: 10.1007/s10588-011-9094-6
   Web of Science ®Google Scholar
• Kahn, B.K., Strong, D.M. and Wang, R.Y (2002). Information Quality Benchmarks: Product and Service Performance. Communications of the ACM, 45(4): 184-192. doi: 10.1145/505248.506007
   Google Scholar
• Korac, D. and Simic, D. (2017). Design of Fuzzy Expert System for Evaluation of Contemporary User Authentication Methods Intended for Mobile Devices. Control Engineering and Applied Informatics, 19(4); 93-100.
   Web of Science ®Google Scholar
• Kramar, D., Cica, D. Sredanovic, B. and Kopac, J. (2015). Design of fuzzy expert system for predicting of surface roughness in high-pressure jet assisted turning using bioinspired algorithms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 30(1); 96-106. doi: 10.1017/S0890060415000189
   Web of Science ®Google Scholar
• Maji, P.K., Roy, A.R. and Biswas, R. (2002). An application of soft sets in a decision making problem. Computers & Mathematics with Applications, 44 (8), 1077-1083. doi: 10.1016/S0898-1221(02)00216-X
   Web of Science ®Google Scholar
• Mamdani, E., and Assilian, S. (1975). An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, 7(1), 1-13. doi: 10.1016/S0020-7373(75)80002-2
   Google Scholar
• Mandel D. R. (2015). Accuracy of Intelligence Forecasts from the Intelligence Consumer’s Perspective. Policy Insights from the Behavioral and Brain Sciences, 2(1); 111-120. doi: 10.1177/2372732215602907
   Google Scholar
• Mandel, D. R. and A. Barnes. (2014). Accuracy of forecasts in strategic intelligence. Proceedings of the National Academy of Sciences, 111(30); 10984-10989. doi: 10.1073/pnas.1406138111
   PubMed Web of Science ®Google Scholar
• Marrin, S. (2011). The 9/11 Terrorist Attacks: A Failure of Policy Not Strategic Intelligence Analysis. Intelligence and National Security 26 (2-3): 182-202.
   Google Scholar
• Marrin, S. (2012). Evaluating the Quality of Intelligence Analysis: By What (Mis) Measure? Intelligence and National Security 27(6); 896-912. doi: 10.1080/02684527.2012.699290
   Google Scholar
• Molodtsov, D. (1999), Soft set theory-first result, Computers and Mathematics with Applications 37; 19-31. doi: 10.1016/S0898-1221(99)00056-5
   Web of Science ®Google Scholar
• Molodtsov, D. (2004). The theory of soft set, URSS Publishers, Moscow.
   Google Scholar
• Moon, C., Lee J. and S. Lim. (2010). A performance appraisal and promotion ranking system based on fuzzy logic: An implementation case in military organizations. Applied Soft Computing, 10(2); 512-519. doi: 10.1016/j.asoc.2009.08.035
   Web of Science ®Google Scholar
• National Intelligence Estimation (2007). Iran: Nuclear Intentions and Capabilities. http://www.isisnucleariran.org/assets/pdf/2007_Iran_NIE.pdf (Accessed December 20, 2016). p. 1-9.
   Google Scholar
• Office of the Director of National Intelligence [ODNI], U.S. National Intelligence: An Overview (Washington, D.C.: ODNI 2011). https://www.dni.gov/files/documents/IC_Consumers_Guide_2011.pdf (Accessed December 20, 2016).
   Google Scholar
• Pawlak, Z. (1982). Rough Sets, International Journal of Computer and Information Science, 11; 341-356. doi: 10.1007/BF01001956
   Web of Science ®Google Scholar
• Peng, Z. and Iwamura, K. (2013). A Sufficient and Necessary Condition of Uncertainty Distribution. Journal of Interdisciplinary Mahematics, 13(3); 277-285. doi: 10.1080/09720502.2010.10700701
   Google Scholar
• Phythian, M. Policing Uncertainty: Intelligence, Security and Risk. Intelligence and National Security, 27(2) (2012): 187-205. doi: 10.1080/02684527.2012.661642
   Google Scholar
• Schmidt, J. M. (2015). Intelligence, Strategic Warning, and Foresight: Completing the Package for Decision-Makers. Journal of Intelligence & Analysis, 22(2): 11-30.
   Google Scholar
• Timmermans, J., De-Haan, H. and Squazzoni, F. (2008). Computational and mathematical approaches to societal transitions, Computational and Mathematical Organization Theory, 14; 391–414. doi: 10.1007/s10588-008-9035-1
   Web of Science ®Google Scholar
• Wang, R. Y. and Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4); 5-3 doi: 10.1080/07421222.1996.11518099
   Google Scholar
• Weiss, C. (2008). Communicating uncertainty in intelligence and other professions. International Journal of Intelligence and CounterIntelligence, 21(1); 57-85. doi: 10.1080/08850600701649312
   Google Scholar
• Wheaton, K. (2012). The Revolution Begins on Page Five: The Changing Nature of NIEs” International Journal of Intelligence and CounterIntelligence, 25; 330-349. doi: 10.1080/08850607.2012.652548
   Google Scholar
• Xie, N.X. (2016). An algorithm on the Parameter Reduction of Soft Sets. Fuzzy Information and engineering, 8; 127-145. doi: 10.1016/j.fiae.2016.06.001
   Web of Science ®Google Scholar
• Yager, R.R. (2006). Fuzzy set methods for uncertainty management in intelligence analysis. International Journal of Intelligent Systems, 21(5); 523-544. doi: 10.1002/int.20143
   Web of Science ®Google Scholar
• Yu, Y. and T. Jin. (2011). The return policy model with fuzzy demands and asymmetric information. Applied Soft Computing 11(2); 1669-1678. doi: 10.1016/j.asoc.2010.05.004
   Web of Science ®Google Scholar
• Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8(3): 338-353. doi: 10.1016/S0019-9958(65)90241-X
   Google Scholar
• Zhu, H. and Wang, R. Y. (2009). An Information Quality Framework for Verifiable Intelligence Products. Data Engineering: Mining, Information and Intelligence, Vol. 132 of the series International Series in Operations Research & Management Science, pp. 315-333, Springer US.
   Google Scholar