网络治理的权力基础:一个跨案例研究

网络组织是当前经济活动中的普遍现象,网络治理并非信任基础上的自主治理,而是受组织间权力关系的规制,权力是决定网络组织运行的重要关系并影响了网络组织运行绩效.本文通过一个跨案例研究,分析了网络治理的权力基础,提出组织间权力来源于企业资源、结构位势和制度压力.三种权力基础的作用机理不同,其中企业资源和网络中心性显著地表现为个体层面的权力,而网络密度和制度压力则体现为网络层面的权力,个体层面权力与网络层面权力的相互作用导致网络组织呈现不同的演变趋势.     

具有最优学习率的RBF神经网络及其应用

传统固定学习率的RBF神经网络在金融时间序列预测方面已经有比较成功的应用,但网络学习率的选择问题却给传统RBF神经网络的使用带来了不便.利用梯度下降法及优化方法推导出了RBF神经网络的动态最优学习率并将其应用于网络学习算法,具有最优学习率的RBF神经网络能够在保证网络稳定学习的同时兼顾网络的收敛速度.为了检验具有动态最优学习率的RBF神经网络的预测效果,对沪深300指数波动率进行了预测实验.实验结果表明,具有动态最优学习率的RBF神经网络比传统的固定学习率的RBF神经网络有着更快的收敛速度,同时也避免了人为选定学习率的不便.     

基于Fréchet Copula的欧式脆弱期权定价

运用Fréchet Copula和相关性测度Kendallτ来刻画脆弱期权行权概率与对手方违约之间的相关结构,并给出了欧式脆弱看涨期权价格的闭形式表达式.然后由Kendallτ和标的资产价格与执行价格比率的不同数值,对欧式脆弱看涨期权的价格进行了数值计算和敏感性分析.结果表明,随着τ值的增加和标的资产价格与执行价格比率的降低,欧式脆弱看涨期权的价值相应地呈下降趋势,这与理论结果和实际都是相符的.     

The Potential for Cannibalization of New Products Sales by Remanufactured Products*

The potential for cannibalization of new product sales by remanufactured versions of the same product is a central issue in the continuing development of closed‐loop supply chains. Practitioners have no fact‐based information to guide practice at firms and academics have no studies available to use as the basis for assumptions in models. We address the cannibalization issue by using auctions to determine consumers’ willingness to pay (WTP) for both new and remanufactured products. The auctions also allow us to better understand the potential impact of offering new and remanufactured products at the same time, which provides us insights into the potential for new product cannibalization. Our results indicate that, for the consumer and commercial products auctioned, there is a clear difference in WTP) for new and remanufactured goods. For the consumer product, there is scant overlap in bidders between the new and remanufactured products, leading us to conclude that the risk of cannibalization in this case is minimal. For the commercial product, there is evidence of overlap in bidding behavior, exposing the potential for cannibalization.     

Irregular Identification,Support Conditions,and Inverse Weight Estimation

In weighted moment condition models, we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models. In particular, if it is necessary for (point) identification that the weights take arbitrarily large values, then the parameter of interest, though point identified, cannot be estimated at the regular (parametric) rate and is said to be irregularly identified. This rate depends on relative tail conditions and can be as slow in some examples as n^−1/4. This nonstandard rate of convergence can lead to numerical instability and/or large standard errors. We examine two weighted model examples: (i) the binary response model under mean restriction introduced by Lewbel (1997) and further generalized to cover endogeneity and selection, where the estimator in this class of models is weighted by the density of a special regressor, and (ii) the treatment effect model under exogenous selection (Rosenbaum and Rubin (1983)), where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score. Without strong relative support conditions, these models, similar to well known “identified at infinity” models, lead to estimators that converge at slower than parametric rate, since essentially, to ensure point identification, one requires some variables to take values on sets with arbitrarily small probabilities, or thin sets. For the two models above, we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to Andrews and Schafgans (1998) for the sample selection model.     

A multiple-item budget-constraint newsboy problem with a reservation policy

In contrast to the classical newsboy-style problem, this paper develops a model for the multiple-item budget-constraint newsboy problem considering a reservation policy to meet marketing needs. A discount rate is provided to those customers who are willing to make a reservation. In addition to the demand from the original customers, extra demand is also included in the model due to the motivation of the discount rate. A solution algorithm, namely the MCR algorithm, is proposed to solve the problem. The proposed algorithm can actually be considered as a generalization of the classical newsboy-style problem. The MCR algorithm not only provides a business unit with the optimal order quantity, but also the discount rate necessary to achieve the maximal total expected profit under a limited budget. From the illustrated example, it is shown that the expected profit from the proposed model is greater than that from the classical model, due to the consideration of reservations, even though the budget constraint is tight. The increase in the expected profit could be treated as an information value from the willingness function. Although the proposed model is developed for multiple-item problems, it also can be applied to single-item ones. From the sensitivity analysis, the application of the single-item problem to the proposed model can still obtain greater expected profit than the classical model under different budgetary levels.