Design of a Big-data-Based Decision Support System for Rational Cultural Policy Establishment

This paper proposes a technique for designing a decision-making system based on big data to support rational cultural policy decisions. To identify a rational cultural policy, it is necessary to extract a comparable index for cultural policy and analyze and process factors in terms of cultural supply and cultural consumption. Analyzed and processed supply indices and consumption indices become the basic input data for calculating additional cultural indices that can be measured at the cultural level of each region. Regional cultural indices are treated as independent variables in terms of cultural supply, and target variables are considered in terms of cultural demand. Two corresponding types of regression models are established. Based on the eXtreme gradient boosting and light gradient boosting machine algorithms, which are representative algorithms for calculating cultural indicators, we attempted to construct and analyze a model of the proposed system. The developed model is designed to predict the demand index according to the regional cultural supply index. It was confirmed that the demand side could be changed based on supply-side items by using the proposed technique to support decision-making. Due to the complexity of the policy environment of modern society, mixing various policy tools targeting multiple functions is accepted as a common basis for policy design, but institutional arrangements are needed to reflect the results of various data analyses in budget decision-making. This will be possible to produce data based on effectiveness and suggest appropriate rational policies and decisions.