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Computer Modelling in Decision Making (CMDM 2019)
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Computer Modelling in Decision Making (CMDM 2019)

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (15 April 2020) | Viewed by 25363

Special Issue Editors

Dr. Sergei Sidorov

E-Mail Website
Guest Editor
Faculty of Mechanics and Mathematics, Saratov State University, Saratov 410026, Russia
Interests: econometric modelling; software development; data mining; statistical analysis
Dr. Dmitry Pavlyuk

E-Mail Website
Guest Editor
Department of Mathematical Methods and Modelling, Transport and Telecommunication Institute, Riga, LV-1019, Latvia
Interests: data mining; risk modeling; volatility modeling; complex networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Fourth Workshop Computer Modelling in Decision Making (CMDM 2019) will be held in Saratov State University, Saratov, Russia, 14–15 November 2019. The workshop’s main topic is computer and mathematical modelling in decision making in finance, insurance, banking, economic forecasting, investment and financial analysis. CMDM 2019 brings together researchers from different scientific communities working on areas related to computer modelling in decision making. The variety of scientific topics ranges from machine learning for decision making; data mining and econometrics; financial and economic network analysis; urban network analysis; mathematical economics; optimization models; other fields of computer science.

The papers invited for submission to this Special Issue should extend the scope of the contributions appearing in the conference proceedings and should include significantly new material. We hope that they will examine the latest problems and advances within the information-based decision making.

Dr. Sergei Sidorov
Dr. Dmitry Pavlyuk
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer modelling
  • decision making
  • data mining
  • econometrics
  • mathematical economics
  • optimization models
  • machine learning for decision making
  • financial and economic network analysis
  • economic forecasting
  • decision making in finance
  • decision making in insurance
  • financial analysis

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Published Papers (5 papers)

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Research

19 pages, 3107 KiB  
Article
Exploring Technology Influencers from Patent Data Using Association Rule Mining and Social Network Analysis
by Pranomkorn Ampornphan and Sutep Tongngam
Information 2020, 11(6), 333; https://doi.org/10.3390/info11060333 - 22 Jun 2020
Cited by 33 | Viewed by 5575
Abstract
A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention [...] Read more.
A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to patent analysis, for example, technology overview, investment volume, and the technology life cycle. There are also data mining methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making. The main contribution of this study is to apply data mining methods and social network analysis to gain knowledge in emerging technologies and find informative technology trends from patent data. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data analyzed include the International Patent Classification (IPC) code and patent titles. Association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. The results showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions. Full article
(This article belongs to the Special Issue Computer Modelling in Decision Making (CMDM 2019))
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24 pages, 1832 KiB  
Article
Efficiency Analysis of Regional Innovation Development Based on DEA Malmquist Index
by Anna Firsova and Galina Chernyshova
Information 2020, 11(6), 294; https://doi.org/10.3390/info11060294 - 31 May 2020
Cited by 35 | Viewed by 4862
Abstract
The aim of the work was to evaluate the dynamics of regional innovation development and compare the Russian regions according to their innovation efficiency, used resources, and achieved results. To estimate direct and indirect innovation effects, this study used the data on Russian [...] Read more.
The aim of the work was to evaluate the dynamics of regional innovation development and compare the Russian regions according to their innovation efficiency, used resources, and achieved results. To estimate direct and indirect innovation effects, this study used the data on Russian regions according to variables of the innovative product volume, the share of high-tech products in the gross regional product (GRP) structure, the number of used patents, and investment in innovation activity for 2006–2017. To obtain a representative sample, a cluster analysis was applied as a preliminary step, which made it possible to select a group of regions that were most advanced in terms of their innovative development. Output-oriented data envelopment analysis models were applied for Malmquist Productivity Index calculation. The obtained results indicate the average growth of total factor productivity of regional innovation development over time. The main source of innovative development is largely derived from the economy of scale, while the effectiveness of regional innovation systems is basically increasing through broader resource bases, rather than through its effective utilization. The research findings can be applied to diagnose regional innovation effectiveness, justify public investment in research and development (R & D), and identify the priorities of regional innovation policy for specific regions. Full article
(This article belongs to the Special Issue Computer Modelling in Decision Making (CMDM 2019))
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19 pages, 949 KiB  
Article
Economic Growth Patterns: Spatial Econometric Analysis for Russian Regions
by Vladimir Balash, Olga Balash, Alexey Faizliev and Elena Chistopolskaya
Information 2020, 11(6), 289; https://doi.org/10.3390/info11060289 - 29 May 2020
Cited by 14 | Viewed by 4073
Abstract
In this article, we analyze the σ - and β -convergence, using the data of the socio-economic development of Russian areas, and discover the role of spatial autocorrelation in regional economic development. We are considering 80 areas of the Russian Federation for the [...] Read more.
In this article, we analyze the σ - and β -convergence, using the data of the socio-economic development of Russian areas, and discover the role of spatial autocorrelation in regional economic development. We are considering 80 areas of the Russian Federation for the period of 2010–2017. Moran coefficients were used to estimate spatial autocorrelation. We compare the Moran scatterplots for GDP per capita and GDP growth rates per capita in 2017 and in 2014. We study the impact on raising investment in leading capital and the costs of technological innovation. We evaluate a wide range of specifications of spatial econometric models for all kinds of weight matrices. We combine standard geographical proximity with specialization proximity to assess whether they are substitutes or additions to converging economic growth rates. The weight matrix of the neighborhood and specialization similarities are used. The weight matrix of specialization similarities of the regional economies is based on data on the structure of tax payments in 82 industries. The specialization structure of the region’s economy is related to its location. Clusters obtained by matrices of specialization proximity are well separable from each other in space. The connectivity within clusters and the boundaries between them become more apparent over time. It is shown that according to the results of estimation of conditional β -convergence models, the models of 2010–2014 and 2014–2017 differ significantly. There is a statistically significant β -convergence for the period 2010–2014. There is also the presence of spatial autocorrelation. Based on the results of valuation models constructed from data after 2014, it can be concluded that the coefficient estimates for the explanatory variables are not significantly different from zero, and accordingly there is no tendency towards regional convergence in terms of economic development. The results obtained in the work are stable for the proposed models and spatial weight matrices. Territorial proximity is a more important factor than the similarity of specialization for explanation the economic growth rates of Russian regions. Full article
(This article belongs to the Special Issue Computer Modelling in Decision Making (CMDM 2019))
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12 pages, 852 KiB  
Article
Hedonic Pricing on the Fine Art Market
by Anna Zhukova, Valeriya Lakshina and Liudmila Leonova
Information 2020, 11(5), 252; https://doi.org/10.3390/info11050252 - 4 May 2020
Cited by 2 | Viewed by 4246
Abstract
In conditions of the stock market instability the art assets could be considered as an attractive investment. The fine art market is very heterogeneous which is featured by uniqueness of the goods, specific costs and risks, various peculiarities of functioning, different effects and, [...] Read more.
In conditions of the stock market instability the art assets could be considered as an attractive investment. The fine art market is very heterogeneous which is featured by uniqueness of the goods, specific costs and risks, various peculiarities of functioning, different effects and, hence, needs special treatment. However, due to the diversity of the fine art market’s goods and the absence of the systematic information about the sales, researchers do not come to the same opinion about the merits of the art assets conducting studies on single segments of the market. We make an attempt to investigate attractiveness of the fine art market for investors. Extensive data was collected to obtain a complete pattern of the market analyzing it within different segments. We use the Heckman model in order to estimate the art asset return and find out the most influential factors of art price dynamics. Based on the estimates obtained we construct monthly art price index and compare it with S&P500 benchmark. Full article
(This article belongs to the Special Issue Computer Modelling in Decision Making (CMDM 2019))
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17 pages, 275 KiB  
Article
Decision-Making Techniques for Credit Resource Management Using Machine Learning and Optimization
by Ekaterina V. Orlova
Information 2020, 11(3), 144; https://doi.org/10.3390/info11030144 - 4 Mar 2020
Cited by 19 | Viewed by 6003
Abstract
Credit operations are fundamental in the banks’ activities and provide a significant share of their income. Under an increased demand for credit resources, credit risks are growth. It keeps the importance of the problem of an increase in the efficiency of lending management [...] Read more.
Credit operations are fundamental in the banks’ activities and provide a significant share of their income. Under an increased demand for credit resources, credit risks are growth. It keeps the importance of the problem of an increase in the efficiency of lending management processes in financial institutions. The aim of the work is the justification and development of new technology and models for the management of bank lending that reduce credit risks and increases lending efficiency. The research materials are statistical data from the Bank of Russia and Rosstat. The methods of system analysis, methods of control theory, methods of statistics, optimization methods and machine learning are used. The positive results of the implementation of the proposed technology and credit management models are of practical importance to ensure the profitability growth of credit organization and contribute to its competitiveness. Full article
(This article belongs to the Special Issue Computer Modelling in Decision Making (CMDM 2019))
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