Intellectual Evaluation of the Economic Systems’ Performance in Post-Industrial Society

Antonina Kazelskaya () and Igor Stepnov ()
Additional contact information
Antonina Kazelskaya: Audit of Your Business LLC
Igor Stepnov: Moscow State Institute of International Relations (MGIMO University)

Chapter Chapter 15 in Post-Industrial Society, 2020, pp 181-192 from Springer

Abstract: Abstract The chapter deals with the difficulty of forecasting economic systems’ work, due to the influence of rapid, largely unpredictable changes in the external environment. Lacking a universal risk management methodology, the search is underway for an individual approach to this issue in each organization. In connection to this, algorithms are used for analysis and risk management, alongside the use of technologies for cognitive modelling of qualitative analysis of economic systems, as well as neuro-fuzzy networks and their quantitative characteristics. Cognitive modelling includes determining target factors, base factors, complex problem situations, and control effects. The cognitive map is drawn, charting the formation of the production programme in the form of an oriented, weighted graph; the vertices of this correspond to the aforementioned factors. Qualitative analysis of this cognitive map is then carried out: arcs between vertices correspond to cause-and-effect relationships between factors and indicate the force of their influence. Processes of perturbation propagation by pathways and cycles are investigated. The hierarchy analysis method is used to verify this. The cognitive map was developed by updating the values of the links without either changing the vertices themselves or adding or removing the links themselves. It is proposed that, to increase the accuracy of forecasting, a neuro-fuzzy network should be created, using the most influential factors of the trained cognitive map for its input. This approach can be used in business intelligence systems for the knowledge economy, based on intelligent decision support systems that use cognitive methods of analysis of expert data, and can also increase efficiency by the parametric configuration of intelligent decision support systems.

Keywords: Uncertainty; Cognitive modelling; Neural networks; Intellectual evaluation; Economic systems; P41 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-59739-9_15

Ordering information: This item can be ordered from
http://www.springer.com/9783030597399

DOI: 10.1007/978-3-030-59739-9_15

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().