创新是国家经济发展的动力,如何评估创新效率一直是研究热点.本文对国内外创新评估及创新调查的发展历程和发展现状进行了分析,在对创新评估原理和基本原则进行总结的基础上,基于新一代信息技术,详细分析了国内外现行的较为有效的创新评估方法.并采用创新调查技术中较为典型的DEA方法对我国20家不同领域的企业进行创新效率的比较分析.最后,针对基于新一代信息技术的创新调查方法的改善提出了建议.

研发和转化功能型平台作为一个集资源共享、信息共享、交流互动的开放性的服务体系越来越得到多方面的重视.研发和转化功能型平台吸引了大量的科技资源配置,引导该平台的发展进入新阶段,因而平台良性的考核评价机制有助于加大科技成果的转化,有助于经济发展创新体系的完善.

随着我国改革的不断深入,之前的改革发展模式不能完全适应我国当前的经济发展需求,“大众创业万众创新”概念应运而生,为改革找到了新的方向.但是我国的经济发展制度、人才培养制度、政府扶持制度等并不能完全适应“大众创业万众创新”的要求,面临很大的困境.基于这样的实际情况,在文献调研的基础上,总结了我国当前“大众创业万众创新”制度的不足与缺陷,并进行了回归模型分析,最终提出了改善“大众创业万众创新”制度的建议.

The post-financial crisis price transmission mechanism between RMPI, PPI, CGPI, and CPI is investigated by using extended stage of processing model. The empirical evidence shows that, changes in RMPI transmit to PPI, not vice versa, and PPI response to the changes in CGPI. After the financial crisis, the changes in CPI do not feedback to any stage in the price chain, the consumer demand is not the key factor in driving prices.

We use the methods of random matrix theory(RMT)to investigate the information structure of the covariance matrix between stock returns.The largest eigenvalue are found to represent the market information.Chinese stock portfolio has a particularly high value of largest eigenvalue,which is about 175 times larger than the RMT upper bound.Market information is the dominate factor determining the correlations between stock returns.The other eigenvalues deviating from the RMT upper bound represent information affecting stocks belonging to similar or related industries.

针对我国房地产开发投资规模的差异性,本文选取全国31个省市作为样本,以8个房地产开发投资规模为基础指标,对全国各地区房地产开发投资规模进行因子与聚类分析.结果表明:我国房地产开发投资规模呈现出显著的区域性.最后对各地区进行合理的房地产开发提出了相应对策.对于掌握我国房地产开发投资区域发展特点,制定相应的宏观调控政策具有重要意义.

Data envelopment analysis (DEA) methodology has recently been applied to weight derivation in the analytic hierarchy process (AHP). This paper proposes a cross-weight evaluation technique, which is similar to the cross-efficiency evaluation in DEA, for priority determination in the AHP. The cross-weight evaluation produces true weights for perfectly consistent pairwise comparison matrices and logical weights for inconsistent pairwise comparison matrices. Numerical examples are examined to illustrate the advantages and potential applications of the cross-weight evaluation in priority determination in the AHP.

The noise to the correlations between stocks returns are tested by using the RMT method.77.53% of the eigenvalues of the sample covariance matrix are found to fall within the RMT bounds and agree with the universal properties predicted by RMT-implying a large degree of noise.The noise content of the covariance matrix is filtered by applying Mean-value,and Zero-value schemes.With the filtered matrix,the ex post risk of the minimum variance portfolio is reduced significantly.

网上双向拍卖是将双向拍卖通过网络进行的一种拍卖形式,不仅具有跨地域性、无场地限制的优势,而且双向拍卖能使拍卖交易价格收敛到竞争均衡附近。文章假设拍卖的双方均以个人期望收益最大化为目标,以其中某一买家为视角,引入虚拟等价处理的方法简化拍卖过程中的双方竞争对手,构建不完全信息博弈的贝叶斯—纳什均衡模型,进行简化求解,获得买卖双方最佳报价的最优解,从而达到减少报价回合,提高网上拍卖效率的目的。

电子商务的信用评价体系在防范网络欺诈、建立良好信任关系、提高市场效率等方面发挥了积极的作用。但目前国内的C2C交易平台的信用评价体系在信用度计算、各类欺诈行为甄别与惩治等方面，仍存在很大缺陷，导致了信用炒作的盛行、差评敲诈的滋生。文章以国内最大的C2C交易平台---淘宝商城为研究对象，分析其现有信用评价体系，指出该体系的不足之处，并对信用评价体系尤其信用度计算模型提出针对性改进的建议，以更好地保护买卖双方的利益，提高交易效率。

The quality and security of agricultural products is the hot issue with public attention in China and also one of the issues that Chinese government attaches great importance to. This paper describes the principle of data mining technology and based on the environmental information data of agricultural production and the quality-security testing data of agricultural products, analyses the application of data mining technology in the quality and security of agricultural products.

DEAHP as a weight derivation procedure for analytic hierarchy process (AHP) has been found suffering from some significant drawbacks. Recently, Mirhedayatian and Saen (2011) [5] proposed a new procedure entitled Revised DEAHP for AHP weight derivation [S. M. Mirhedayatian, R. F. Saen, A new approach for weight derivation using data envelopment analysis in the analytic hierarchy process, Journal of the Operational Research Society 62 (2011) 1585-1595]. This paper provides a detailed note to reveal that (1) the Revised DEAHP cannot derive true weights from perfectly consistent pairwise comparison matrices, (2) it may produce irrational weights for inconsistent pairwise comparison matrices, (3) it still suffers from rank reversal problem when an efficient decision criterion or alternative is added or removed, (4) the use of the super-efficiency model in data envelopment analysis (DEA) for AHP weight derivation is redundant and meaningless when there exist multiple decision criteria or alternatives that are efficient in a pairwise comparison matrix, and (5) it may produce a completely reversed ranking that is totally opposite to the rank obtained by the eigenvector method in the case of hierarchical structures, leading to a wrong decision being made. (C) 2011 Elsevier Ltd. All rights reserved.

Cross-efficiency evaluation is an effective approach to ranking decision making units (DMUs) that utilize multiple inputs to produce multiple outputs. Its models can usually be developed in a way that is either aggressive or benevolent to other DMUs, depending upon the decision maker (DM)'s subjective preference to the two extreme cases. This paper proposes several new data envelopment analysis (DEA) models for cross-efficiency evaluation by introducing a virtual ideal DMU (IDMU) and a virtual anti-ideal DMU (ADMU). The new DEA models determine input and output weights from the point of view of distance from IDMU or ADMU without the need to be aggressive or benevolent to any DMUs. As a result, the cross-efficiencies measured by these new DEA models are neutral and more logical. Numerical examples are provided to illustrate the potential applications of these new DEA models and their effectiveness in ranking DMUs.

Existing methods for generating common weights in data envelopment analysis (DEA) are either very complicated or unable to produce a full ranking for decision making units (DMUs). This paper proposes a new methodology based on regression analysis to seek a common set of weights that are easy to estimate and can produce a full ranking for DMUs. The DEA efficiencies obtained with the most favorable weights to each DMU are treated as the target efficiencies of DMUs and are best fitted with the efficiencies determined by common weights. Two new nonlinear regression models are constructed to optimally estimate the common weights. Four numerical examples are examined using the developed new models to test their discrimination power and illustrate their potential applications in fully ranking DMUs. Comparisons with a similar compromise approach for generating common weights are also discussed.

This paper proposes a correlation coefficient (CC) and standard deviation (SD) integrated approach for determining the weights of attributes in multiple attribute decision making (MADM) and a global sensitivity analysis to the weights determined. The CCSD integrated approach determines the weights of attributes by considering SD of each attribute and their CCs with the overall assessment of decision alternatives, where CCs are determined by removing each attribute from the overall assessment of decision alternatives. If the CC for an attribute turns out to be very high, then the removal of this attribute has little effect on decision making; otherwise, the attribute should be given an important weight. The global sensitivity analysis to the weights of attributes is proposed to ensure the stability of the best decision alternative or alternative ranking. A numerical example about the economic benefit assessment of the industrial economy of China is investigated to illustrate the potential applications of the CCSD method in determining the weights of attributes. Comparisons with existing weight generation methods are also discussed.

It has been widely recognized that data envelopment analysis (DEA) lacks discrimination power to distinguish between DEA efficient units. This paper proposes a new methodology for ranking decision making units (DMUs). The new methodology ranks DMUs by imposing an appropriate minimum weight restriction on all inputs and outputs, which is decided by a decision maker (DM) or an assessor in terms of the solutions to a series of linear programming (LP) models that are specially constructed to determine a maximin weight for each DEA efficient unit. The DM can decide how many DMUs to be retained as DEA efficient in final efficiency ranking according to the requirement of real applications, which provides flexibility for DEA ranking. Three numerical examples are investigated using the proposed ranking methodology to illustrate its power in discriminating between DMUs, particularly DEA efficient units.

Analytic hierarchy process (AHP) has been criticized for its possible rank reversal phenomenon caused by the addition or deletion of an alternative. This paper shows the fact that the rank reversal...

In a recent paper by Bana e Costa and Vansnick [C.A. Bana e Costa, J.C. Vansnick, A critical analysis of the eigenvalue method used to derive priorities in AHP, European Journal of Operational Research 187 (3) (2008) 1422–1428], analytic hierarchy process (AHP), particularly its eigenvector method (EM) used for deriving priorities from pairwise comparison matrices, was criticized for the violation of a so-called condition of order preservation (COP). Due to this violation, the EM was considered to have a serious fundamental weakness which makes the use of AHP as a decision support tool very problematic. The consistency ratio (CR) index in the AHP was also criticized for its failure to act as an alert of this violation of COP. In this paper, we look into decision makers' overall judgments which can be obtained through the aggregation of their direct and indirect judgments and then re-examine Bana e Costa and Vansnick's numerical examples with a detailed analysis to show the invalidity of their criticisms.

The maximizing set and minimizing set method is a popular ranking approach for fuzzy numbers, which ranks them based on their left, right and total utilities. This paper presents an alternative ranking approach for fuzzy numbers called area ranking based on positive and negative ideal points, which defines two new alternative indices for the purpose of ranking. The two new indices are defined in terms of a decision maker (DM)'s attitude towards risks and the left and the right areas between fuzzy numbers and the two ideal points. It is shown that the area ranking approach has strong discrimination power and can rank fuzzy numbers that are unable to be discriminated by the maximizing set and minimizing set method. It is also shown that the DM's attitude towards risks may have a significant impact on the ranking of fuzzy numbers. As a side product, a new defuzzification formula is also developed and discussed.

Data envelopment analysis (DEA) requires input and output data to be precisely known. This is not always the case in real applications. This paper proposes two new fuzzy DEA models constructed from the perspective of fuzzy arithmetic to deal with fuzziness in input and output data in DEA. The new fuzzy DEA models are formulated as linear programming models and can be solved to determine fuzzy efficiencies of a group of decision-making units (DMUs). An analytical fuzzy ranking approach is developed to compare and rank the fuzzy efficiencies of the DMUs. The proposed fuzzy DEA models and ranking approach are applied to evaluate the performances of eight manufacturing enterprises in China.