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Additive Criterion to Evaluate Object Innovation


The article discusses some aspects of the object descriptions having significant innovation potential. The procedure for selecting such descriptions consists of two consecutive phases. The first phase involves generating effective search queries with a special genetic algorithm. In the second phase, the model developed determines the likely innovativeness of the object. Meanwhile the values of additive selection criteria are calculated. In the latter case, the criterion is the index of innovativeness. The purpose of the article is to justify the additive criterion applicability for calculating the value of the object innovativeness. The article describes general conditions of applying additive evaluation criteria and shows how these conditions are met in the case in question. The analysis of the partial criteria gives grounds to assert their additive independence and, therefore, the correct use of additive n-dimensional utility function. Some additional reasons for applying additive criterion are also given. In general, the article proposes a unified approach to generating global assessment criteria and the relevance of their unified formal structure is shown. Note that earlier the authors proposed a similar approach to the fitness function formation of the genetic algorithm used. Despite the different physical meaning and purpose of the criteria, their relevance to common formal structure is proved.

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  1. Data Warehousing Based on Search Agent Intellectualization and Evolutionary Model of Target Information Selection. The Project Summary. Accessed 2021.

  2. P. C. Fishburn, Utility Theory for Decision Making (Wiley, New York, 1970).

    Book  Google Scholar 

  3. S. Shahryar and Y. Parsia, ‘‘Modified weighted sum method for decisions with altered sources of information,’’ Math. Stat. 7 (3), 57–60 (2019).

    Article  Google Scholar 

  4. V. V. Podinovskiy, ‘‘Sensitivity of multi-criteria choice to changes in assessments of the importance of heterogeneous criteria,’’ Inform. Tekhnol. Nauke, Obrazov. Upravl. 4, 23–27 (2017).

    Google Scholar 

  5. E. V. Soboleva, ‘‘Modifications of generalized utility criteria in problems of identification of multicriteria choice,’’ Sist. Doslidzh. Inform. Technol. 3, 58–65 (2012).

    Google Scholar 

  6. R. L. Keeney and H. Raiffa, Decisions with Multiple Objectives: Preferences and Value Trade-Offs (Cambridge Univ. Press, Cambridge, 1993).

    Book  Google Scholar 

  7. P. C. Fishburn, ‘‘Independence in unity theory with whole product sets,’’ Oper. Res. 13, 28–45 (1965).

    Article  Google Scholar 

  8. A. M. Anokhin, V. A. Glotov, V. V. Pavel’ev, and A. M. Cherkashin, ‘‘Methods for determination of criteria importance coefficients,’’ Avtom. Telemekh. 8, 3–35 (1997).

    MathSciNet  MATH  Google Scholar 

  9. P. A. Gudkov, Benchmarking Methods (Penz. Gos. Univ., Penza, 2008) [in Russian].

    Google Scholar 

  10. S. V. Karpushkin, Design Decision Making Theory (Tamb. Gos. Tech. Univ., Tambov, 2015) [in Russian].

    Google Scholar 

  11. Jia Hongli, Song Xing, Gao Jian, Wang Qian, and Ma Quanyue, ‘‘Equipment management information system evaluation based on entropy weight method and AHP model,’’ in Proceedings of the 3rd International Conference on Computer Science and Information Engineering ICCSIE 2018 (2018), pp. 522–530.

  12. Liu Heng, ‘‘Research on civil aviation security management system in dealing with air security threats,’’ in Proceedings of the 2nd International Conference on Information Science and Electronic Technology ISET 2019 (2019), pp. 32–37.

  13. B. Al-Shargabi, O. Sabri, and S. Aljawarneh, ‘‘An enhanced arabic information retrieval using genetic algorithms: An experimental study and results,’’ Aust. J. Basic Appl. Sci. 7 (13), 242–248 (2013).

    Google Scholar 

  14. R. B. Tucker, Driving Growth through Innovation: How Leading Firms are Transforming their Futures, 2nd ed. (Berrett-Koehler, San Francisco, 2008).

    Google Scholar 

  15. Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation, The Measurement of Scientific, Technological and Innovation Activities, 4th ed. (OECD, Paris/Eurostat, Luxembourg, 2018).

  16. S. Mouhallab and J. Wei, ‘‘Standing points of innovation capacity,’’ J. Econ. Business Manag. 4, 53–57 (2016).

    Article  Google Scholar 

  17. A. Hevner, B. Donnellan, and J. Anderson, The DRIVES (Design Research for Innovation Value, Evaluation and Sustainability), Vol. 388 of Model of Innovation, Communications in Computer and Information Science, Ed. by M. Helfert and B. Donnellan (Springer, Berlin, 2012).

  18. B. L. Goldense, ‘‘Top 10 product development metrics: Then and now,’’ Machine Des. 89 (9), 96 (2017).

    Google Scholar 

  19. R. Lingannavar and P. Yammiyavar, ‘‘Product innovation index using Linkograph analysis,’’ in Research into Design for a Connected World, Vol. 134 of Smart Innovation, Systems and Technologies (Springer, Singapore, 2019), pp. 297–306.

  20. Web of Science platform: Derwent Innovations Index. Accessed 2021.

  21. P. ter Haar, ‘‘Measuring innovation: A state of the science review of existing approaches,’’ Intangible Capital 14, 409–428 (2018).

    Article  Google Scholar 

  22. D. Rainey, Product Innovation: Leading Change through Integrated Product Development (Cambridge Univ. Press, Cambridge, 2005).

  23. E. Nissan and F. Niroomand, ‘‘Technology diffusion indexes across countries,’’ J. Econ. Studies 39, 31–43 (2012).

    Article  Google Scholar 

  24. G. A. Shafer, Mathematical Theory of Evidence (Princeton Univ. Press, Princeton, NJ, 1976).

    Book  Google Scholar 

  25. R. Yager and L. Liping, Classic Works of the Dempster–Shafer Theory of Belief Functions (London, Springer, 2010).

    MATH  Google Scholar 

  26. V. K. Ivanov, B. V. Palyukh, and A. N. Sotnikov, ‘‘Features of data warehouse support based on a search agent and an evolutionary model for innovation information selection,’’ in Proceedings of the 4th International Scientific Conference on Intelligent Information Technologies for Industry IITI’19 (Springer Nature, Switzerland, 2020), pp. 120–130.

  27. V. K. Ivanov, B. V. Palyukh, and A. N. Sotnikov, ‘‘Additive criteria to evaluate relevance of innovative objects in data warehouse,’’ Lobachevskii J. Math. 41 (12), 2535–2541 (2020).

    Article  MATH  Google Scholar 

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This work was done at the Tver State Technical University with supporting of the Russian Foundation of Basic Research (project no. 20-07-00199) and at the Joint Supercomputer Center of the Russian Academy of Sciences—Branch of NIISI RAS within the framework of the State assignment (research topic 0580-2021-0016).

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Correspondence to V. K. Ivanov, B. V. Palyukh or A. N. Sotnikov.

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(Submitted by A. M. Elizarov)

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Ivanov, V.K., Palyukh, B.V. & Sotnikov, A.N. Additive Criterion to Evaluate Object Innovation. Lobachevskii J Math 42, 2537–2544 (2021).

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  • innovation
  • novelty
  • demand
  • implementability
  • additive criterion
  • utility function
  • additive independence
  • partial criterion