INTEGRATED METHOD OF EXTRACTION, FORMALIZATION AND AGGREGATION OF COMPETITIVE AGENTS EXPERT EVALUATIONS IN A GROUP

Authors

  • T. Kolpakova Zaporizhzhya National Technical University, Ukraine
  • A. Oliinyk Zaporizhzhya National Technical University, Ukraine
  • V. Lovkin Zaporizhzhya National Technical University, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2017-2-11

Keywords:

Еxpert evaluation, group evaluations, individual evaluations, expert competence, competitive agents

Abstract

Context. The problem of extraction, formalization and aggregation of expert evaluations performed during selection of the best solution from possible alternatives set was considered. The problem actuality is defined by different application areas, additional analysis necessity and group evaluation under uncertainty.

Objective. The research objective was to raise quality of the decisions made by group of experts due to increase of individual expert evaluation process efficiency and improvement of evaluation aggregation process.

Method. The integrated method which consists of individual and group evaluation was proposed for the problem solution.
The modified method of extraction and formalization of individual expert evaluations which is based on the analytic hierarchy process modification and includes absolute and relative evaluation phases was proposed. It evaluates expert competence based on confidence coefficient for expert judgments.
The modified method of agent group evaluation based on summation of individual expert evaluations of every alternative was proposed. It makes total evaluation based on relative quantitative importance of the agents and confidence coefficients for judgments of every group participant. It gives preference to judgments of qualified experts to increase solution quality.

Results. The experimental investigation of the proposed methods confirmed availability of the developed mathematical support. The proposed methods are able to detect and control nontrivial issues in tasks which are solved.

Conclusions. Scientific novelty of the paper consists in the proposed integrated method of extraction, formalization and aggregation of expert evaluations in group which enables to define integrated assessment of competitive agents as well as to evaluate confidence coefficient for expert judgments directly during individual evaluation process and to use it in the following group decision-making phase.
Practical significance of the paper results consists in the developed information technology which made it possible to put the method into practice for solving of the tender support tasks and experiment results.

Author Biographies

T. Kolpakova, Zaporizhzhya National Technical University

Senior lecturer of Software Tools Department

A. Oliinyk, Zaporizhzhya National Technical University

PhD, Associate Professor, Associate Professor of Software Tools Department

V. Lovkin, Zaporizhzhya National Technical University

PhD, Associate Professor of Software Tools Department

References

Karnyishev A. D., Ivanova E. A. Psihologiya deyatelnosti i upravleniya: ucheb. posobie. Irkutsk, IGEA, 2001, 186 p.

Mulen E. Kooperativnoe prinyatie resheniy: aksiomyi i modeli. Moscow, Mir, 1991, 464 p.

Mayers D. Sotsialnaya psihologiya. Piter, 2013, 800 p.

Ambrusa A., Greinerb B., Pathakc P. A. How individual preferences are aggregated in groups: An experimental study. Journal of Public Economics, 2015, Volume 129, pp. 1–13.

Budzi ski R., Becker J. Model of competence of experts in the computer decision support system. Quantitative Methods In Economics, 2013, Volume XIV, Issue 1, pp. 53-64.

Patel H. T. A Study on the Effectiveness of Group Activity and Group Discussion Method in English. International Journal of Research in Humanities and Social Sciences, 2014, Volume 2, Issue 1, pp. 13–15.

Parkes D. C., Xia L. A Complexity-of-Strategic-Behavior Comparison between Schulze’s Rule and Ranked Pairs. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012, pp. 1429–1435.

Roszkowska E. Rank ordering criteria weighting methods – a comparative overview. Optimum. Studia Ekonomiczne, 2013, No. 5 (65), pp. 14–33.

Saaty T. L., Vargas L. G. Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Springer US, 2012, 346p.

Nguyen H. T., Dawal S. Z. M., Nukman Y., Aoyama H., Case K. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation. PLOS One, 2015, No. 10 (9), 24 p.

Oliinyk A., Zaiko T., Subbotin S. Training Sample Reduction Based on Association Rules for Neuro-Fuzzy Networks Synthesis. Optical Memory and Neural Networks (Information Optics), 2014, Vol. 23, No. 2, pp. 89–95.

Oliinyk A., Subbotin S.A. The decision tree construction based on a stochastic search for the neuro-fuzzy network synthesis. Optical Memory and Neural Networks (Information Optics), 2015, Vol. 24, No. 1, pp. 18–27.

Subbotin S. A. The method of diagnostic model synthesis based on radial basis neural networks with the support of generalization properties. Radio Electronics. Computer Science. Control, 2016, No. 2, pp. 64–69.

Oliinyk A. Production rules extraction based on negative selection. Radio Electronics. Computer Science. Control, 2016, No. 1, pp. 40–49.

Oliinyk A. O., Skrupsky S. Yu., Subbotin S. A. Experimental Investigation with Analyzing the Training Method Complexity of Neuro-Fuzzy Networks Based on Parallel Random Search. Automatic Control and Computer Sciences, 2015, Vol. 49, Issue 1, pp. 11–20.

Bodyanskiy Ye. V., Tyshchenko O. K., Boiko O. O. An evolving cascade system based on neurofuzzy nodes. Radio Electronics. Computer Science. Control, 2016, No. 2, pp. 40–45.

Oliinyk A. A., Subbotin S. A. Association Rules Extraction for Pattern Recognition. Pattern Recognition and Image Analysis, 2016, Vol. 26, No. 2, pp. 419–426.

Oliinyk A. O., Skrupsky S. Yu., Subbotin S. A. Using Parallel Random Search to Train Fuzzy Neural Networks. Automatic Control and Computer Sciences, 2014, Vol. 48, Issue 6, pp. 313–323.

Subbotin S., Oliinyk A., Skrupsky S. Individual prediction of the hypertensive patient condition based on computational intelligence. Proceedings of the International Conference on Information and Digital Technologies, 2015, pp. 336–344.

Pangeran M. H., Pribad K. S. Conceptual model of analytical network process for prioritizing risk in a PPP infrastructure project. Proceedings of the First Makassar International Conference on Civil Engineering, 2010, pp. 1217–1227.

Jao C. S. Efficient Decision Support Systems – Practice and Challenges in Multidisciplinary Domains. InTech, 2011, 478p.

Rush C., Roy R. Expert Judgement in Cost Estimating: Modelling the Reasoning Process. Concurrent Engineering, 2001, No. 9, pp. 271–284.

Andreychikov A. V., Andreychikova O. N. Analiz, sintez, planirovanie resheniy v ekonomike. M., Finansyi i statistika, 2012, 368 p.

Shrotriya S., Pandey A. Prediction of the Winner by Using a Weighted Approach of Preferential Balloting Systems on the Basis of Their Satisfied Criterions and Artifice Behavioral Complexity. 3rd International Conference on Information Security and Artificial Intelligence (ISAI 2012), Singapore, 2012, pp. 142–146.

Wang B., Qian Y. Determining decision makers’ weights in group ranking: a granular computing method. International Journal of Machine Learning and Cybernetics, 2015, Volume 6, Issue 3, pp. 511–521.

Tao L. Decision Support for Contractor Selection: Incorporating consolidated Past Performance Information. University of Hong Kong, 2010, 184 p.

How to Cite

Kolpakova, T., Oliinyk, A., & Lovkin, V. (2017). INTEGRATED METHOD OF EXTRACTION, FORMALIZATION AND AGGREGATION OF COMPETITIVE AGENTS EXPERT EVALUATIONS IN A GROUP. Radio Electronics, Computer Science, Control, (2), 100–108. https://doi.org/10.15588/1607-3274-2017-2-11

Issue

Section

Progressive information technologies