Developing a distributed linguistic decision making system

Demidovskij, Alexander; Babkin, Eduard

doi:10.17323/1998-0663.2019.1.18.32

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…There is an urgent need to develop DSSs, which is an urgent scientific and social aim for the entire world community [31][32][33][34][35][36]. The level of development of digitalization in society also demonstrates the need to implement DSSs [37].…”

Section: Literature Sources Analysis and Purpose Of Study Formulationmentioning

confidence: 99%

Enterprise Compensation System Statistical Modeling for Decision Support System Development

et al. 2021

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This article raises the issue of decision support system (DSS) development in enterprises concerning the compensation system (CS). The topic is relevant as the CS is one of the main components in human resource management in business. A key element of such DSSs is CS models that provide predictive analytics. Such models are able to give information about how a particular CS affects output, product quality, employee satisfaction, and wage fund. Thus, the main goal of this article is to obtain a CS statistical model and its formulas for determining the probability densities of resultant indicators. To achieve this goal, the authors conducted several blocks of research. Firstly, mathematical formalization of CS functionality was described. Secondly, a statistical model of CS was built. Thirdly, calculations of CS result indicators were made. Reliable scientific methods were used: black box modeling and statistical modeling. This article proposes a statistical and analytical model. As an example, a piecework-bonus system statistical model is demonstrated. The discussion derives formulas of integral estimations showing the probability density of the resulting CS indicators and the related statistical characteristics. These results can be used to predict the behavior of the workforce. This constitutes the scientific novelty of the study, which will establish significant advances in the development of DSSs in the field of labor economics and HR management.

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Section: Literature Sources Analysis and Purpose Of Study Formulationmentioning

confidence: 99%

Enterprise Compensation System Statistical Modeling for Decision Support System Development

et al. 2021

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“…In multi-criteria decision-making tasks, the knowledge of the problem situation is hierarchical [26,50,53,70] and may be represented as a tree, with leaves indicating linguistic evaluations and nodes representing corresponding elements such as criteria, sets of criteria, experts, and alternatives. The selection of linguistic evaluations is influenced by the need to account for context ambiguity, the unstructured character of expert assessments, and other factors.…”

Section: Possible Implementation Processmentioning

confidence: 99%

Integrated neurosymbolic decision support systems: problems and opportunities

Demidovskij¹,

Babkin²

2021

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The current problem of developing new kinds of decision support systems for different categories of management personnel is addressed in this study. A critical feature of such systems is their distributed and decentralized nature, which enables the construction of next-generation information systems in the form of Multi-Agent Systems, Internet of Things, or Fog Computing Architectures. Parallel models of the dynamics of artificial neural networks are produced under such realistic circumstances, demonstrating their potential for addressing a variety of issues. The purpose of this study is to conduct a critical analysis of the problem of integrating Artificial Neural Networks with decision support systems using a corpus of relevant scholarly literature. To tackle this question, the Design Science Research methodology was considered. According to this methodology, a literary search strategy was established, scientific literature was collected and analyzed, and key comparisons between different solutions were emphasized. The study resulted in the presentation of the most important findings, outstanding issues, and potential areas of fundamental and applied solutions. A consistent trend toward the development of decision support systems based on integrated neural-network methods has been observed, which is efficient and cost-effective since it enables the creation of distributed and trainable decision support systems.

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Section: Possible Implementation Processmentioning

confidence: 99%

“…As a result, it becomes possible to develop a fully trainable neural network-based aggregator of linguistic evaluations that over time adapts to incoming data and uses historical data. In particular, such an algorithm could independently assign weights of experts' assessments and criteria (as well as sets of criteria) according to a multi-level methodology of linguistic decision-making [70]. In this case, the result of the network operation becomes an instance of Tensor Representations, which turns into a linguistic assessment after decoding.…”

Section: Possible Implementation Processmentioning

confidence: 99%

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2021

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The mission of the journal is to develop business informatics as a new field within both information technologies and management. It provides dissemination of latest technical and methodological developments, promotes new competences and provides a framework for discussion in the field of application of modern IT solutions in business, management and economics.The journal publishes papers in the areas of, but not limited to: modeling of social and economic systems, digital transformation of business, innovation management, information systems and technologies in business, data analysis and business intelligence systems, mathematical methods and algorithms of business informatics, business processes modeling and analysis, decision support in management.The journal is included into the list of peer reviewed scientific editions established by the Supreme Certification Commission of the Russian Federation.The journal is included into Web of Science Emerging Sources Citation Index (WoS ESCI), Russian Science Citation Index on the Web of Science platform (RSCI), EBSCO.

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Developing a distributed linguistic decision making system

Cited by 10 publications

References 29 publications

Enterprise Compensation System Statistical Modeling for Decision Support System Development

Enterprise Compensation System Statistical Modeling for Decision Support System Development

Integrated neurosymbolic decision support systems: problems and opportunities

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