發(fā)布時間：2018-01-02 00:14

  本文關鍵詞：大數據環(huán)境下政府投資建設項目決策模型研究　出處：《華北電力大學(北京)》2017年博士論文　論文類型：學位論文

  更多相關文章： 政府投資建設項目 大數據環(huán)境 決策機制 概率區(qū)間數 隨機占優(yōu)度

【摘要】：政府投資建設項目是指為了實現政府職能,滿足社會公眾訴求,貫徹落實經濟社會發(fā)展戰(zhàn)略,利用國家財政預算內、外資金,或以財政性資金作為還款來源的借貸性資金建設的項目。大量上馬的政府投資建設項目對于改善居民生活環(huán)境,促進經濟社會發(fā)展,保證經濟平穩(wěn)增長等方面意義重大,但是同時也引起了社會各界對于政府投資建設項目決策科學性的高度關注和潛在擔憂。大數據技術的飛速發(fā)展為我們解決傳統(tǒng)問題提供了新的思路,越來越多的學者看好大數據技術在政府投資建設項目決策領域的應用潛力。然而,由于政府決策者對于大數據技術還比較陌生,無法運用大數據的思維思考決策問題,構建大數據環(huán)境下全新的決策邏輯框架,提出研發(fā)訴求;而熟悉大數據技術的研發(fā)人員又不了解現實的決策環(huán)境和決策人的訴求,無法提供對應的需求響應方案。在大數據技術人員和政府決策者間存在一條無法逾越的知識鴻溝,制約著大數據技術在政府投資決策領域內的應用。為了填補這條橫在政府決策者與大數據技術人員間的知識鴻溝,本文首先對當前的大數據采集、儲存、處理、決策支持技術的發(fā)展現狀進行了系統(tǒng)的梳理,厘清了大數據技術的技術邊界。隨后在可以實現的大數據技術范圍內,深入分析了大數據環(huán)境下政府投資建設項目的決策情境,并進一步提出了全新的大數據環(huán)境下的政府投資建設項目決策機制。最后提出了在全新的決策環(huán)境和機制下的決策模型。具體研究內容包括以下六個部分:(1)政府決策中的大數據技術應用研究。本文首先采用文獻分析法,從大數據采集技術、大數據存儲技術、大數據處理技術和大數據決策支持技術四個方面,對當前可用的大數據技術進行全面的梳理和分析,從而明確大數據技術的技術邊界,為之后的研究奠定技術基礎。(2)大數據環(huán)境下政府投資建設項目決策情境研究。在明確大數據技術邊界的基礎上,本文進一步研究了當前的大數據技術環(huán)境下,政府決策思維應該進行怎樣的轉變以及如何進行轉變。隨后通過構建公眾參與有效決策模型,分析了大數據技術對公眾參與決策帶來的巨大影響。并討論了政府投資建設項目決策中的數據種類、數據來源、數據分析方法以及數據安全問題。(3)大數據環(huán)境下政府投資建設項目決策機制設計。該部分研究基于大數據環(huán)境下政府投資建設項目決策的全新情境,以決策的科學化、民主化、合理化為目標,討論并重新定義了政府、公眾和專家在政府投資建設項目決策中的角色職責,并以此為基礎設計大數據環(huán)境下政府投資建設項目決策機制。(4)基于近似隨機占優(yōu)的大數據決策模型研究。根據大數據環(huán)境下政府投資建設項目決策的數據形式的特征,本研究提出了可以同時分析和處理實數、隨機數和區(qū)間數三類決策數據的算法理論——概率區(qū)間數及其運算規(guī)則。在此基礎上基于近似隨機占優(yōu)理論,提出一種考慮了所有公眾風險態(tài)度和效用偏好的決策模型,該模型能夠高效率低成本地處理超大決策群體的風險態(tài)度和效用偏好,在保證決策精度的同時,大大降低對大數據處理技術的要求。(5)政府與公眾異構偏好集結決策模型研究。針對政府和公眾給出的異構偏好,本文通過設計模型將政府決策者加工過的偏好信息還原為較原始的狀態(tài),把公眾與政府決策者的異構偏好轉化為同一形式,實現不同類型決策者異構偏好的集結。此外,針對決策中有多個政府主體參與決策的情況,本文還進一步提出了多個決策主體的異構偏好集結模型。(6)政府與公眾偏好趨同靶向調整模型研究。本文首先提出了基于Kendall和諧系數和基于修正前后的指標偏好權重向量間歐式距離的兩個滿意度評估模型。若認為滿意度不能滿足決策要求,則需要對決策者的偏好進行調整。針對大數據環(huán)境下政府投資建設項目決策的特點,本文構建了面向不同調整階段的政府與公眾風險和指標偏好調整模型,借助數學算法輔助決策雙方根據自身意愿對偏好進行精準快速的調整。
[Abstract]:The construction project of government investment is that in order to achieve the functions of the government, to meet the public demands, implement the strategy of economic and social development, the state budget, funds, or financial capital as a source of repayment of loan funds for construction projects. The government launched a large number of investment projects to improve the living environment, promote the economic society the development, guarantee the steady economic growth of great significance, but also aroused great concern for government investment in the construction of the scientific decision making of the project and potential concerns. The rapid development of information technology provides a new way for us to solve the traditional problems, more and more scholars are optimistic about the potential application of big data technology in the field of government decision making the investment in construction projects. However, because of government decision makers for big data technology is still relatively unfamiliar, not the use of big data thinking Dimensional thinking decision problem, construct the decision logic framework of the new big data environment, put forward a new demand; and familiar with big data technology R & D and do not understand the real decision-making environments and people's demands, to provide the corresponding demand response program. There is an insurmountable gap in knowledge and technical personnel and government big data decision makers, restricting the application of big data technology in government investment decisions in the field. In order to fill the knowledge gap across government policymakers and big data technology personnel, based on the current data collection, storage, processing, sorting out the system development present situation of decision support technology, clarify the technology of boundary data technology. Then in the big data technology range can be achieved within the in-depth analysis of the data under the environment of government investment construction project decision-making situation, and further. The decision mechanism of government investment construction project under the new environment of big data. Finally put forward the decision model in the decision-making environment and new mechanism. The specific research contents include the following six parts: (1) research on big data technology application in government decision-making. Firstly, using the method of literature analysis, from big data acquisition technology, data storage technology, four aspects of large data processing technology and data decision support for comprehensive analysis and analysis of the currently available data technology, so as to clear the boundary technology of data technology, and lay a foundation for the study. (2) after the big data environment government research project making investment and construction. Based on big data technology boundary, this paper further studies the big data technology under the current environment, government decision-making should be how to change and how to Change. Then through the construction of public participation in the effective decision-making model, analysis of large data technology of public participation in the huge impact brought by decision-making. And discussed the construction of government investment project decision of data types, data sources, data analysis and data security issues. (3) big data under the environment of government investment construction project decision-making mechanism design. This part of the study based on the new situation of the government under the big data environment construction project investment decision, to the scientific decision-making, democratic, rational discussion as the goal, and a new definition of the government, the public and experts in the construction project of government investment decision-making in the role, and on the basis of the design of large data under the environment of government investment construction the project decision-making mechanism. (4) research on big data approximate decision model based on stochastic dominance. According to government data environment construction project investment decision according to the number of forms The characteristics, this study proposes can also analyze and deal with real numbers, random number and interval number decision-making data algorithm theory: the probability interval number and its operation rules. Based on this approximation based on stochastic dominance theory, proposes a decision-making model considering all public risk attitudes and preferences, the model is capable of high efficiency and low cost processing of large groups of decision risk attitudes and preferences, in which the decision accuracy at the same time, greatly reduce the large data processing requirements. (5) the government and the public on the heterogeneous preference decision model. According to the preference of the government and the public are heterogeneous, the design model of government decision makers processed preference information reduction as compared to the original state, the heterogeneous preference public and government decision makers into the same form, different types of decision makers of heterogeneous aggregation of preference. In addition, according to the decision of a number of government participation in decision making, this paper further puts forward the main decision-making multiple heterogeneous preference aggregation model. (6) to study the adjustment model of the government and the public preference is given in this paper. The convergence of the target two satisfaction index preference weight vector based on Euclidean distance based on Kendall coefficient and harmony before and after the correction between the satisfaction evaluation model. If that can not meet the demand of decision, requires policymakers preference adjustment. According to the characteristics of big data in government investment project decision-making environment, this paper constructs the government and the public and the risk index of preference adjustment model for different adjustment stage, with the help of mathematical decision algorithm according to both sides own preferences for accurate rapid adjustment.

【學位授予單位】：華北電力大學(北京)
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP311.13;F282
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本文編號：1366837