Open AccessAssessment of the risks of losing investments aimed at the development of Smart city systems
Author(s) -
Valeriy Lakhno,
AUTHOR_ID,
V. P. Malyukov,
Raissa Uskenbayeva,
T. S. Kartbayev,
K. O. Togzhanova,
Dietmar Bayer,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
vestnik nacionalʹnoj inženernoj akademii respubliki kazahstan
Language(s) - English
Resource type - Journals
eISSN - 2709-4707
pISSN - 2709-4693
DOI - 10.47533/2020.1606-146x.118
Subject(s) - investment (military) , computer science , risk analysis (engineering) , class (philosophy) , process (computing) , bilinear interpolation , return on investment , novelty , quality (philosophy) , financial risk , operations research , engineering , actuarial science , business , economics , artificial intelligence , philosophy , theology , epistemology , production (economics) , politics , political science , law , computer vision , macroeconomics , operating system
The article proposes a model for the computational core of the decision support system (DSS) in assessing the risks of investment loss during the dynamic planning (DP) of Smart City development. In contrast to the existing solutions, the proposed model provides specific recommendations when assessing the risks of loss. In case of an unsatisfactory risk forecast, it is possible to flexibly adjust the parameters of the investment process in order for the parties to achieve an acceptable financial result. The scientific novelty of the results is that for the first time it is proposed to apply a new class of bilinear multistep games. This class allowed us to adequately describe the process of assessing the risks of investment loss, using the example of dynamic planning for the placement of financial resources of players in Smart City projects. A distinctive feature of the considered approach is the use of tools based on the solution of a bilinear multistep game of both quality with several terminal surfaces, and a game of degree solved in the class of mixed strategies. Computational experiments were carried out in the Maple mathematical modeling package, and a DSS was developed in which a risk assessment model was implemented. The developed DSS allows to reduce the discrepancies between the data for predicting the risks of investment loss during the Smart City DP and the real return on investment.