Measurement Uncertainty in Decision-Making: How to Take Reliable Decisions Under Uncertainty

In this paper, the authors examine a common issue concerning the influence of measurement uncertainty on decisions. In fact, in some practical applications, it can be necessary to put in comparison measurement data with thresholds and limits. It occurs when the conformity with fixed specifications has to be verified or if warning and alert levels have to be not exceeded. In such a circumstance, to take reliable decisions in presence of uncertainty is a concrete problem. Measurement uncertainty may reasonably be the cause of unreliable decisions. In order to manage properly the uncertainty effect, the authors have developed a decision making procedure based on a methodical approach to measurement uncertainty. In detail, a fuzzy logic algorithm estimates the probability to take a wrong decision because of the uncertainty. Such information is so used in order to optimize the decisional criteria, improving the consistency of the final computing results. Risks and costs associated to the possibility to take a mistaken decision are minimized. Consequently the algorithm singles out the most reliable decision.

[1]  Scott L. Howell,et al.  Online Assessment And Measurement: Foundations And Challenges , 2005 .

[2]  David Mason.  Online Assessment and Measurement: Case Studies From Higher Education, K‐12 and Corporate , 2006 .

[3]  Gao-Xia Wang.  Multiple attribute decision making problems with mixed uncertain information , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[4]  Sami D. Alaruri.  Automated Multi-Diode Laser System for WDM Couplers Insertion Loss Measurements , 2012, Int. J. Meas. Technol. Instrum. Eng..

[5]  Mehdi Khosrow-Pour.  Cases on Assessment and Evaluation in Education , 2012 .

[6]  Shyi-Ming Chen,et al.  Fuzzy Decision-Making Based on Likelihood-Based Comparison Relations , 2010, IEEE Transactions on Fuzzy Systems.

[7]  G. Zingales.  The evaluation of uncertainties in industrial measurements for quality , 1996, Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Tec.

[8]  Stuart S. Gold.  A Case Study Analysis of the Use of Online vs. Proctored Final Exams in Online Classes , 2010 .

[9]  Betty Bergstrom,et al.  Defining Online Assessment for the Adult Learning Market , 2005 .

[10]  Matteo Cacciola,et al.  About Processing of Exponentially Damped Signals: A Hardware for Biomedical Applications , 2012, Int. J. Meas. Technol. Instrum. Eng..

[11]  Sagarika Pal,et al.  Design, Development and Testing of a Semi Cylindrical Capacitive Array Type Liquid Interface Level Sensor , 2011, Int. J. Meas. Technol. Instrum. Eng..

[12]  David Graf,et al.  Continuous Assessment for Improved Student Outcomes: Examples from WebCT's Exemplary Course Project , 2006 .

[13]  Monica Divitini,et al.  Providing Recommendations for Mobile Learning: Opportunities and Challenges from Context , 2012 .

[14]  Carmine Landi,et al.  MEASUREMENT OF QUALITY INDEXES IN PROCESS MONITORING: AN ORIGINAL APPROACH TO UNCERTAINTY ANALYSIS , 2003 .

[15]  T. L. Saaty.  A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[16]  Carol B. MacKnight.  Handbook of research on electronic surveys and measurements , 2008 .

[17]  Rosario Morello,et al.  A Fuzzy Approach to Decision Making About Compliance of Environmental Electromagnetic Field With Exposure Limits , 2009, IEEE Transactions on Instrumentation and Measurement.

[18]  Nilly Mor.  Eysenck Personality Questionnaire , 2010 .

[19]  L. Piper,et al.  Incidence of Risk Factors on the Onset of Gestational Diabetes Mellitus: An Empirical Research in Southern Italy , 2012, Int. J. Meas. Technol. Instrum. Eng..

[20]  D. Macii,et al.  Management of measurement uncertainty for effective statistical process control , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[21]  M. Catelani,et al.  On the quality of a measurement process: a statistical approach for the variability evaluation , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[22]  Thomas Keutel,et al.  System Design and Energy Management for Indoor Solar Energy Harvesting Under Consideration of Spectral Characteristics of Solar Cells , 2013, Int. J. Meas. Technol. Instrum. Eng..

[23]  Howard Castrup,et al.  Uncertainty Analysis for Risk Management , 1995 .

[24]  Uzay Kaymak,et al.  Model predictive control using fuzzy decision functions , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[25]  Liu Yuxun,et al.  Multi-attribute Group Decision-making Model Based on Trapezoid Fuzzy Number Expected Values , 2010, 2010 International Conference on Challenges in Environmental Science and Computer Engineering.

[26]  Evangelos Triantaphyllou,et al.  Development and evaluation of five fuzzy multiattribute decision-making methods , 1996, Int. J. Approx. Reason..

[27]  Kenneth Lightfoot,et al.  Alignment of Course Objectives and Assessment Items: A Case Study , 2010 .