Support For Managerial Decision-Making In Implementation Of Regional Socio-Economic Programs

Abstract

The problem of the management of the implementation of regional socio-economic programs considered. Significant implementation time, characteristic of this type of programs, requires, after each stage of their adjustment, due to both a change in the socio-economic situation in the region and possible mismatch between the planned and actual results of the previous stage. The purpose of the work is to develop tools to support for managerial decision-making to determine the system of requirements for the projects, intended for implementation at the next stage of the socio-economic program in the face of uncertainty and risk. The methodological apparatus of multi-criteria expert assessment, game theory and artificial intelligence theory are used as the theoretical foundations of the developed toolkit. The developed procedure includes a survey of experts (which allows determining the results of the last stage of the program and clarifying the required targets for the next stage), drawing up a decision-making task (forming a list of strategies and possible situations, as well as expert evaluation of the results of applying strategies in relevant situations), choosing a managerial decision (based on the Hurwicz criterion in determining the coefficient of pessimism /optimism using a system of linguistic variables, the system fuzzy production rules and the fuzzy inference algorithm of Mamdani). The approach proposed in the work allows taking into account the specifics of the regions and increase the scientific feasibility of managerial decisions made in the process of implementing socio-economic programs.

Keywords: Socio-economic programdecision-makinguncertainty

Introduction

The uneven socio-economic development of the regions is one of the most significant factors of instability in the world, stimulating negative processes, ranging from social, national and religious extremism to environmental degradation of territories caused by the use of outdated industrial technologies.

The effective mechanism for managing the socio-economic development of regions is the development and implementation at the regional level of specialized national programs. For example, in the Russian Federation, such federal target programs as “Development of Education”, “Housing”, “Integrated rural development”, and others are currently being implemented. As part of these programs, regional authorities form their own regional development programs in these areas, taking into account their specifics (Telegin, 2018). This regional specificity is to some extent taken into account by the federal target programs aimed at the socio-economic development of territories uniting several regions (for example, the program “Protection of Lake Baikal and the Socio-Economic Development of the Baikal Natural Territory for 2012–2020”), but this is the same. Individual regions have their own characteristics, which should be reflected in the relevant regional programs.

Problem Statement

Despite a significant number of fundamental works devoted to the general problems of sustainable development of regions (for example, Blewitt, 2015; Tausch, 2012), as well as to some special legal issues (Rakhmatullin, 2019), organizational (Malikova, 2017), financial (Poltarykhin, Ryabova, Minaeva, & Malsagov, 2019) support and assessment of progress (Firov, 2018; Khusainova & Bakhvalov, 2018) of regional socio-economic programs, regional authorities in their practice, often face problems when making organizational decisions, e.g. particular an effective and science-based management of the implementation of these programs. This is due to the insufficient level of methodological research for practical use and (as a result) the lack of effective decision support tools for managing regional programs, taking into account the uncertainty and risk that are characteristic of the subject area.

Research Questions

The construction of decision support tools in the form of uncertainty and risk in relation to the task of managing the implementation of programs requires the study of two interrelated issues

Purpose of the Study

The purpose of the work is to develop tools to support for managerial decision-making to determine the system of requirements for the projects, intended for implementation at the next stage of the socio-economic program in the face of uncertainty and risk.

Research Methods

The methodological apparatus of multi-criteria expert assessment, game theory and artificial intelligence theory are used as the theoretical foundations of the developed toolkit.

Findings

The proposed selection procedure (from the set of possible options SR ={S ystReq ₁, SystReq ₂ ,…, SystReq _n }) of one of the requirements systems for projects planned for the next stage is presented in a formalized form in Figure 01 with the input and output data flows controlling flow and implementation mechanism (IDFF0 notation).

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In Figure 01 , the following control flows are indicated by numbers:

1 – domain knowledge principles;

2 – regulation documents;

3 – multi-criteria expert assessment methods;

4 – game theory and artificial intelligence methods.

The decomposition diagram of the process under consideration is presented in Figure 02 .

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Uncertainty the choice of a system of requirements is associated with the unknown socio-economic state of the region at the next stage of the program. Predictive modeling can give only possible situations from a certain list S = { S ₁, S ₂ …, S _n }. We will assume that experts are able to quantify (at least with some accuracy) the result of the project when using the system of requirements SystReq _i, in the situation S _j . We will denote this assessment by a _ij . The matrix A= ( a _ij , i =1,2,…, m ; j =1,2,…, n ) is the matrix of game of incomplete information, where SR – the strategies of the player (the person making the decision on choosing the competitive requirements system) and S – moves by nature. To find the optimal solution, we will use the Hurwicz criterion (Harrington, 2008). In accordance with this criterion, optimal is the strategy on which

$$\underset{\text{i}}{\text{max}}\text{{k}\underset{\text{j}}{\text{min}}{\text{a}}_{\text{ij}}\text{+}\left(\text{1-k}\right)\underset{\text{i}}{\text{max}}{\text{a}}_{\text{ij}}\text{}}$$

Here, k is the pessimism / optimism coefficient, which can take values in the range from 0 to 1, with a value of 1 corresponding to an extreme degree of optimism, and a value of 0 representing an extreme degree of optimism in assessing risks. As a rule (for example, Baganov, 2019), experts set the value of k , based on their own intuitive ideas, which can cause excessive subjectivity when choosing a solution. Within the framework of the proposed procedure, a formalization of determining the coefficient k , using linguistic variables and representing knowledge of the subject area in the form of a system of fuzzy production rules is proposed.

Let SocRisk and EcRisk denote linguistic variables corresponding to the characteristics of the significance of social and economic risks in the implementation of the next stage of the regional socio-economic program. We will assume that the numerical values of these variables expressed in points in the range from 0 to 100 (the basic set of a linguistic variable) are obtained on the basis of a questionnaire survey of experts using methods for processing the results of collective examinations (Naydis & Naydis, 2018).

The semantics of the verbal values (terms) “low” and “high” of these variables, as well as the linguistic variable PessOpt (which corresponds to the pessimism / optimism coefficient by Hurwicz and has the interval [0, 1] as the base set) are represented by bi trapezoidal correspondence functions (membership functions of the corresponding fuzzy sets) shown in Figure 03 (Zadeh, 1965).

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In Figure 03 numerical constants a , b , c , d , e are different for different variables and are assumed to be obtained using the standard procedure for constructing correspondence functions based on expert surveys.

The relationship between the variables SocRisk , EcRisk and the variable PessOpt is determined by fuzzy logical production rules, the form of which is determined by the specifics of the socio-economic program. As an example, we give a possible system of such rules:

R1: if ((( SocRisk - “low”) and ( EcRisk - “low”) or (( SocRisk - “low”) and ( EcRisk - “high”)) or (( SocRisk - “high”) and ( EcRisk - “low”))) then ( PessOpt - “low”)

R2: if (( SocRisk - “high”) and ( EcRisk - “high”)) then ( PessOpt - “high”)

After fuzzification of numerical values expressed in points (obtained as a result of a survey of experts) of linguistic variables SocRisk , EcRisk , basing on a system of fuzzy production rules like R1, R2 and using the Mamdani fuzzy logic inference algorithm (Mamdani, 1974) and subsequent defuzzification, ohe can get the numerical value of the linguistic variable PessOpt , i.e. the value of the coefficient of pessimism of optimism Hurwicz. The proposed formalized procedure allows lowering the level of subjectivity in determining this coefficient, which is important for solving the entire problem, what means increasing the scientific validity of decisions on choosing a system of requirements for projects at the next stage of the regional socio-economic program.

Conclusion

The approach proposed in this work to solving the problem of supporting managerial decision-making for managing regional socio-economic programs, based on the integration of multi-criteria expert assessment methodologies, game theory and fuzzy logic, allows taking into account the high level of uncertainty inherent in the subject area under consideration. The procedure developed on the basis of the proposed approach includes interviewing experts, formalizing the task of choosing a set of requirements for projects to be implemented at a later stage, in the form of a game with incomplete information, and also solving a game based on Hurwicz criterion with a coefficient defined on the basis of fuzzy inference of pessimism / optimism.

The practical significance of the results is determined by the possibility of using the constructed procedure in the development of information and analytical support for the work of the competition commissions for the selection of projects for the next stage of the program. Moreover, to select the project that best meets the found requirements, multi-criteria hierarchical selection procedures can be used, for example, Analytic Hierarchy / Network Process (Saaty, 2008). More complex is the task of not adjusting, but of synthesizing a complex of projects to be implemented (in accordance with certain requirements) at subsequent stages. Due to the large number of options in this case, it will be necessary to use a special methodological apparatus (for example, evolutionary models and methods for the intelligent synthesis of discrete systems (Lomazov, Petrosov, Dobrunova, Lomazova, & Matorin, 2016; Petrosov, Lomazov, Dobrunova, Matorin, & Lomazova, 2015), which may serve as a direction for further research.

Acknowledgments

The reported study was funded by Russian Foundation for Basic Research, project number 20-07-00855.

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