In most exiting portfolio selection models, security returns are assumed to have random or fuzzy distributions. However, uncertainties exist in actual financial markets. Markets are associated not only with inherent risk, but also with background risk that results from the differences among individual investors. This paper investigated the compliance of stock yields to the fuzzy-natured high-order moments of random numbers in order to develop a high-moment trapezoidal fuzzy random portfolio risk model based on variance, skewness, and kurtosis. Data obtained from the Shanghai Stock Exchange and Shenzhen Stock Exchange was used to assess the influence on the proposed model of both background risk and the maximum level of satisfaction of the portfolio. The empirical results demonstrated that the differences between the maximum and minimum variance, skewness, and kurtosis values of the portfolio were positively correlated with the variance of the background risk.

In this paper we reanalyze Said’s (2011) work by retaining all his assumptions except that we use the first-price auction to sell differentiated goods to buyers in dynamic markets instead of the second-price auction. We conclude that except for the expression of the equilibrium bidding strategy, all the results for the first-price auction are exactly the same as the corresponding ones for the second-price auction established by Said (2011). This implies that the well-known "revenue equivalence theorem"holds true for Said’s (2011) dynamic model setting.

This paper considers a worst-case investment optimization problem with delay for a fund manager who is in a crash-threatened financial market. Driven by existing of capital inflow/outflow related to history performance, we investigate the optimal investment strategies under the worst-case scenario and the stochastic control framework with delay. The financial market is assumed to be either in a normal state (crash-free) or in a crash state. In the normal state the prices of risky assets behave as geometric Brownian motion, and in the crash state the prices of risky assets suddenly drop by a certain relative amount, which induces to a dropping of the total wealth relative to that of crash-free state. We obtain the ordinary differential equations satisfied by the optimal investment strategies and the optimal value functions under the power and exponential utilities, respectively. Finally, a numerical simulation is provided to illustrate the sensitivity of the optimal strategies with respective to the model parameters.

The E-supply chain is formed gradually along with the development of network, which is getting more attention among enterprises with unique advantages. Three E-supply chain operation modes are constructed in this paper, then the optimal pricing and advertising strategies under those modes are studied and compared, which are demonstrated with numerical examples. The results of comparison and analysis show that: Selling price, network platform service level, advertising investment and the profits of manufacturer, network platform and E-supply chain all increase with advertising effectiveness of stimulating demand growth. Under centralized decision-making mode, service level is highest, advertising investment is largest and the profit of E-supply chain is highest as well. When manufacturer leads decentralized decision-making mode, not only network service level, advertising investment and the profit of manufacturer can gain better results, but also profit of network platform can be higher while the advertisement effect of increasing demand is big enough. Additionally, it is confirmed that centralized decision-making is better than decentralized decision-making for system operation. Besides, decentralized decision-making mode led by manufacturer is superior to it led by network platform on the condition that advertisement effect is obvious.

We consider an M/M/2 queueing system with two-heterogeneous servers and multiple vacations. Customers arrive according to a Poisson process. However, customers become impatient when the system is on vacation. We obtain explicit expressions for the time dependent probabilities, mean and variance of the system size at time t by employing probability generating functions, continued fractions and properties of the modified Bessel functions. Finally, two special cases are provided.

An insurance-package is a combination being tie-in at least two different categories of insurances with different underwriting-yield-rate. In this paper, the optimal insurance-package and investment problem is investigated by maximizing the insureros exponential utility of terminal wealth to find the optimal combination-share and investment strategy. Using the methods of stochastic analysis and stochastic optimal control, the Hamilton-Jacobi-BellmanèHJBé equations are established, the optimal strategy and the value function are obtained in closed form. By comparing with classical results, it shows that the insurance-package can enhance the utility of terminal wealth, meanwhile, reduce the insureros claim risk.

This paper examines the extent of contagion and interdependence across the six Asian emerging countries stock markets (e.g., Bangladesh, China, India, Malaysia, the Philippine, and South Korea) and then try to quantify the extent of the Asian emerging market fluctuations which are described by intra-regional contagion effect. These markets experienced both fast growth and key upheaval during the sample period, and thus, provide potentially rich information on the nature of border market interactions. Using the daily stock market index data from January 2002 to December 2016 (breaking the 15 years data set into three sub periods; pre-crisis, crisis, and post crisis periods); particularly make attention to the global financial crisis of 2007~2008. The return and volatility spillovers are modeled through the GARCH (generalized autoregressive conditional heteroscedasticity), pairwise Granger causality tests, and the forecast error variance decomposition in a generalized VAR (vector auto regression) models. This paper shows that volatility and return spillovers behave very differently over time, during the pre-crisis, crisis, and post crisis periods. Importantly, Asian emerging stock markets interaction is less before the global financial crisis period. The return and volatility spillover indices touch their respective historical peaks during the global financial crisis 2007~2008, however Bangladeshi market faces this condition in 2009~2010.

High-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection (AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting (DIS) approach. The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine (SVM) and artificial neural network (ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics, information gain and ROC methods can achieve better prediction performance than the DIS approach. Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.

As the pace of urbanization is accelerating, increasing amount of floodplain has been projected as the future cities. Subsequently, urban flooding is being studied by global emergency management exports due to its increasingly significant impact on us. Some existing research on flooding emergency management based on the case-based reasoning (CBR) method have made tremendous progress, but the urban flooding case with its stratified data characteristics is required a new methodology which is different from the ones applied to flash floods. So, based on the case-based reasoning (CBR) method, this paper proposed a CPIE-CBR model with four layers, classification filtration, punctiform similarity, interval similarity and entropy weight method, to calculate the case similarity among the urban flooding case with stratified data characteristics. Then we carry out the numerical simulation with the real data about China and conduct some comparison with original ways so that we observe the validity and efficiency of our model in the end.

Rapid expansion of big-box store in developing country caused typical archetypal change in market structure: Success to the Successful, because big-box stores armed with modernized infrastructure and management capability are absorbing the once customers of the traditional market like a black hole. Facing rapid change in market structure and surmounting pleas from traditional market merchants, government took an inevitable intervention with law regulating the big-box store's business and improving traditional market's competence building. Not so long, however, did government confront policy resistance from both sides: Still ongoing polarization of both side's sales. This study articulates behavior over time of market structure with causal loop diagrams of which causalities are extracted from literatures. This study provides significant contribution to policy makers and traditional markets' merchants in other developing countries like India and China, as well as Korea.

Motivated by the double autoregressive model with order $p$ (DAR($p$) model), in this paper, we study the moving average model with an alternative GARCH error. The model is an extension from DAR($p$) model by letting the order $p$ goes to infinity. The quasi maximum likelihood estimator of the parameters in the model is shown to be asymptotically normal, without any strong moment conditions. Simulation results confirm that our estimators perform well. We also apply our model to study a real data set and it has better fitting performance compared to DAR model for the considered data.

A dual-rate preview control strategy for a type of discrete-time system is proposed based on the theory of multirate control. First, by using the discrete lifting technique, the general dual-rate discrete-time system is converted into a single-rate augmented system. On this basis, the augmented error system is constructed by introducing a first-order difference operator and the previewable reference signal. Then the tracking problem is transformed into a regulator problem of the augmented error system. The optimal preview control law of the augmented error system is obtained by using standard linear quadratic optimal preview control theory, and then the optimal preview controller of the original system is derived. In addition, the necessary and sufficient conditions for the controller are given. Finally, simulation results show the effectiveness of the proposed method.

In the existing electricity market, the traditional power suppliers and renewable energy generators coexist in the power supply side. In the power supply side, renewable energy generators generate power by wind and other natural conditions, leading renewable energy output a certain randomness. However, the low marginal generating cost and the reduction of carbon emissions, and thus brings a certain advantage for renewable energy compared to alternative energy. Electricity, as a special commodity, stable and adequate power supply is a necessary guarantee for economic and social development. Power shortage situation is not allowed in the power system, and the extra power needs to be handled for the purpose of safety. In this paper, the hybrid power generated by renewable energy generators and traditional energy generators is used as power supply, and then the electricity market sells hybrid power to electricity consumers, the hybrid power system determines the optimal daytime price, nighttime price, and the optimal installed capacity of the renewable energy suppliers. We find that the installed capacity of renewable energy increases first and then decreases with the increase of the price sensitivity coefficient of traditional energy supply. Electricity demand is negatively related to electricity price in the current period, and is positively related to price in the other period. The average price of day and night is only related to the total potential demand of day and night and the total generation probability of renewable energy. The price difference between daytime and nighttime is positively related to potential electricity demand, and negatively related to the sensitivity coefficient of electricity price.

According to new features of distributed technology innovation collaboration of complex products of China's aviation, in the multi-agent collaborative R&D process, between main manufacturer and suppliers, this paper summarizes the organizational structure and characteristics of collaborative mode of distributed technology innovation of complex products, basing on the technological innovation and resource integration of complex products, and treating resource contribution rate as a key parameter. It establishes multi-agent resource integration collaborative symbiosis model under distributed collaborative technology innovation mode based on symbiosis theory. And finds that some factors such as the basic attributes of the enterprise, the mutual influence of resources among enterprises and the behavior decision of the main manufacturer have some effects on the resource integration using stability analysis and simulation analysis, which provides a theoretical basis for the main manufacturer to optimize the behavior of suppliers' resource integration.

This paper analyses theoretically the effect of transportation and housing subsidies on urban sprawl, modal choice decisions and urban spatial structure using a spatial general equilibrium model in a monocentric city with two transport modes. Our analysis shows that public transit subsidy leads to urban shrink, whilst subsidizing automobile and housing make the city sprawl. We also find the effects of the other factors on urban sprawl, such as households income and demand, rural land rent, the income tax rate, the total fixed cost of public transit and automobile and the travelling marginal cost of public transit and automobile. Furthermore, this paper also studies how to maximize the urban-area-wide spatial equilibrium utility level.

In this paper, we study a centralized supply chain for a two-stage with selling price discount. This supply chain consists of a supplier and a retailer. Based on the feature that the product's selling season is short and the supply chain faces great demand uncertainty. We consider a two-stage scenario where, at the beginning of stage 1, the supplier reserves production capacity based on historic data in advance, stage 2 comes to us after some leadtime, both the supplier and the retailer update the demand information, the retailer then places an order not exceeding the reserved capacity based on the selling-pricing discount dependent demand. We make optimal decisions on the reserved capacity in stage 1, selling price discount and order quantity in stage 2. In this supply chain, the pattern in stage 2 is figured out first, and then stage 1 is cleared as well. Then we present a numerical example to give some insights. Finally we get some conclusions.

The purpose of this paper is to investigate the pricing European call option valuation problems under the exercise price, maturity, risk-free interest rate, and the volatility function. An advance methodology, Chebyshev simulated annealing neural network (ChSANN), is enforced for the Black-Scholes (B-S) model with boundary conditions. Our scheme is stable and easy to implement on B-S equation, for arbitrary volatility and arbitrary interest rate values. Also, the comparative results demonstrate that the attained approximate solutions are converging towards the exact solution. The graphical results show that the increasing flow of the European call option as the exponential increase takes place in assets. The presented algorithm can be further applied to other financial models with certain boundary conditions. The algorithm of the method shows that the approach can also be easily employed on time-fractional B-S equation.

Given n vertices in a plane and UCAV going through each vertex once and only once and then coming back, the objective is to find the direction (heading) of motion in each vertex to minimize the smooth path of bounded curvature. This paper studies the headings of UCAV. First, the optimal headings for two vertices were given. On this basis, an n-player two-strategy game theoretic model was established. In addition, in order to obtain the mixed Nash equilibrium efficiently, n linear equations were set up. The simulation results demonstrated that the headings given in this paper are effective.

According to the precise ephemeris has only provided satellite position that is discrete not any time, so propose that make use of interpolation method to calculate satellite position at any time. The essay take advantage of IGS precise ephemeris data to calculate satellite position at some time by using Lagrange interpolation, Newton interpolation, Hermite interpolation, Cubic spline interpolation method, Chebyshev fitting method respectively, which has a deeply analysis in the precision of five interpolations. The results show that the precision of Cubic spline interpolation method is the worst, the precision of Chebyshev fitting is better than Hermite interpolation method. Lagrange interpolation and Newton interpolation are better than other methods in precision. Newton interpolation method has the advantages of high speed and high precision. Therefore, Newton interpolation method has a certain scientific significance and practical value to get the position of the satellite quickly and accurately.

In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM (Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import, export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.

The article is about solving the last mile delivery problem in rural town or village. We want to test the drone's potential in parcel delivery. The objectives are 1) to introduce the cluster and truck-drone in tandem delivery method, 2) to compare the new method with the traditional TSP method in aspect of truck running distance, energy using and time occupation. The parcel delivery demand is sparse, so it is not dense enough for a truck to carry on delivery. We try to identify the best route for the drone to deliver the goods. We use k-mean method to carry on clustering, then we use enumeration method to fulfill the centroids delivery, which comes from the depot. We design a model and calculate the energy, time and distance saving between drone using method (DTSP) and traditional TSP method. The drone attended delivery saves truck delivery distance, energy consumption and time. The truck running distance of DTSP method saves 91.87%, the truck running distance is shortened from 189.69 km to 15.4252 km. The DTSP method saves 90.45% of energy. The DTSP method brings a 29.75% cutoff in time aspect when there are two drone in running. The research introduces the cluster and TSP combination method, which is a good way to carry on last mile delivery. The result shows a bright future for drone to attend parcel delivery. The e-commerce corporation can apply this method in practice.

Based on the concept of productive capital stock, this paper estimated capital input by three asset types of China's 36 service industries in 2003-2015, and compared with the results of wealth capital stock. This study found that the wealth capital stock method underestimates the actual capital input in each sector in varying degrees, and it may interference the accuracy of productivity evaluation in sectors. According to the new estimation results of capital input, this paper further applied four stages bootstrap-DEA method to estimate industrial productivity, and calculated its confidence intervals. This study found that, the years of education and the average wage have a significant positive impact on the productivity of service industries; the productive services have a short board effect in the whole service industry.

In the analysis on economic growth factors, researchers usually use the production function model to calculate and measure influencing factors' contribution rates to economic growth. Common production functions include the CD (Cobb-Douglas) production function, the CES (Constant Elasticity of Substitution) production function, the VES (Variable Elasticity of Substitution) production function, and so on. In consideration of the diversity and complementarity of models, the paper combines the CD production function with the CES production function and then proposes a mixed production function. With regard to the parameter estimation of model, the paper gives an improved firefly algorithm with the high precision and a fast rate of convergence. With regard to the calculation of factors' contribution rates, traditional methods generally have big errors and are not applicable to complicated models, so the paper offers a new method which can calculate contribution rates scientifically. Finally, the paper calculates the contribution rates of factors affecting Chinese economic growth and gets a good result.

This paper considers a discrete-time Geo/G/1 queue in a multi-phase service environment, where the system is subject to disastrous breakdowns, causing all present customers to leave the system simultaneously. At a failure epoch, the server abandons the service and the system undergoes a repair period. After the system is repaired, it jumps to operative phase i with probability q_i, i=1, 2 …, n. Using the supplementary variable technique, we obtain the distribution for the stationary queue length at the arbitrary epoch, which are then used for the computation of other performance measures. In addition, we derive the expected length of a cycle time, the generating function of the sojourn time of an arbitrary customer, and the generating function of the server's working time in a cycle. We also give the relationship between the discrete-time queueing system to its continuous-time counterpart. Finally, some examples and numerical results are presented.

An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method, out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved, such as for the DGM(1, 1) model, based on a concave sequence, the modeling error will be larger. In this paper, firstly the definition of sequence convexity is given out, and it is proved that the output sequence of DGM(1, 1) model is a convex sequence. Next, the residual change law of DGM(1, 1) model based on the concave sequence is discussed, and the non-equidistance DGM(1, 1) model is proposed. Finally, by introducing the symmetry transformation, a concave sequence is transformed into a convex sequence, called the symmetric sequence of the concave sequence, and then construct the non-equidistance DGM(1, 1) model based on the convex sequence. The example results show that the novel method is more accurate than the direct modeling for a concave sequence.

This paper combines the perspective of co-evolution with studies in the area of business model, reveals the phenomenon of business model co-evolution and explores the business model co-evolution mechanism between firms through a case study. Findings and what other companies can learn from this study are as follows. Firstly, companies, be it leaders or not, can act as enablers for other party's business model innovation. Secondly, companies can enable each other's business model innovation by taking the perspective of business model co-evolution; Thirdly, the interdependence of the business model co-evolution process can be explained by the four levers named novelty, lock-in, complementarity and efficiency. Companies can select novelty, lock-in, complementarity, efficiency or the combination of the four as mediators to achieve business model co-evolution.

This paper addresses the integrated Earth observation satellite scheduling problem. It is a complicated problem because observing and downloading operations are both involved. We use an acyclic directed graph model to describe the observing and downloading integrated scheduling problem. Based on the model which considering energy constraints and storage capacity constraints, we develop an efficient solving method using a novel quantum genetic algorithm. We design a new encoding and decoding scheme that can generate feasible solution and increase the diversity of the population. The results of the simulation experiments show that the proposed method solves the integrated Earth observation satellite scheduling problem with good performance and outperforms the genetic algorithm and greedy algorithm on all instances.

The gravity p-median model is an important improvement to the widely-used p-median model. However, there is still a debate on its validity in empirical applications. Previous studies even doubt the significance of the gravity p-median model. Using a case study of tertiary hospitals in Shenzhen, China, this study re-examines the difference between the gravity p-median model with the p-median model, by decomposing the difference between the two models into gravity rule and variant attraction. This study also proposes a modified gravity p-median model by incorporating a distance threshold. The empirical results support the validity of the gravity p-median model, and also reveal that only when the attractions of candidate facility locations are variable will the gravity p-median model lead to different results with the p-median model. The difference between the modified gravity p-median model and the gravity p-median model is also examined. Moreover, the impacts of the distance-decay parameter and distance threshold on solutions are investigated. Results indicate that a larger distance-decay parameter tends to result in a more dispersed distribution of optimal facilities and a smaller average travel time, and a smaller distance threshold can better promote the spatial equity of facilities. The proposed method can also be applied in studies of other types of facilities or in other areas.

In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing (RTP) as a two-stage optimization problem. At the first stage, based on cost function, we propose a continuous supply function bidding mechanism to model the utility companies' profit maximization problem, by which the analytic expression of electricity price is further derived. At the second stage, considering that individually optimal solution may not be socially optimal, we employ convex optimization with linear constraints to model the price anticipating users' daily payoff maximum. Substitute the analytic expression of electricity price obtained at the first stage into the optimization problem at the second stage. Using customized proximal point algorithm (C-PPA), the optimization problem at the second stage is solved and electricity price is obtained accordingly. We also prove the existence and uniqueness of the Nash equilibrium in the mentioned twostage optimization and the convergence of C-PPA. In addition, in order to make the algorithm more practical, a statistical approach is used to obtain the function of price only through online information exchange, instead of solving it directly. The proposed approach offers RTP, power production and load scheduling for multiple utility companies and multiple users in smart grid. Statistical approach helps to protect the company's privacy and avoid the interference of random factors, and C-PPA has an advantage over Lagrangian algorithm because the former need not obtain the objection function of the dual optimization problem by solving an optimization problem with parameters. Simulation results show that the proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.

On the basis of rough set theory, the strengths of dynamic reduction are elaborated compared with traditional non-dynamic methods. A systematic concept of dynamic reduction from sampling process to the generation of the reduct set is presented. A new method of sampling is created to avoid the defects of being too subjective. And in order to deal with the over-sized time consuming problem in traditional dynamic reduction process, a quick algorithm is proposed within the constraint conditions. We have also proved that dynamic core possesses the essential characteristics of a reduction core on the basis of the formalized definition of the multi-layered dynamic core.

Using the semi-tensor product of matrices, this paper investigates cycles of graphs with application to cut-edges and the minimum spanning tree, and presents a number of new results and algorithms. Firstly, by defining a characteristic logical vector and using the matrix expression of logical functions, an algebraic description is obtained for cycles of graph, based on which a new necessary and sufficient condition is established to find all cycles for any graph. Secondly, using the necessary and sufficient condition of cycles, two algorithms are established to find all cut-edges and the minimum spanning tree, respectively. Finally, the study of an illustrative example shows that the results/algorithms presented in this paper are effective.

As an advanced process detection technology, electrical impedance tomography (EIT) has wide application prospects and advantages in medical imaging diagnosis. However, a series of issues need to be addressed before applying EIT for bedside monitoring. Medical diagnosis and bedside monitoring are dynamic measuring process, where the positions of measuring electrodes and the shape of the detected field are changing dynamical. Due to the inability to cope with the changeable electrode positions and various dynamic fields, existing EIT systems are mainly used for industrial detection in condition of static measurement and visualization. In this paper, we investigate the dynamic measurement and visualization of human breast in EIT field, describe the design of the measuring sensor system, and expound the measuring principle. The main component of the hardware system is a builtin servo electrical resistance tomography sensor with capacitive sliding rod, which can adapt to the crowd of different chest contour and the change of chest shape in the dynamic process of breathing. The corresponding measuring principle is extracting all real-time positions of measuring electrodes, then obtaining the dynamic boundary, finally dividing the detection field rapidly. Experimental results confirmed that the proposed system can obtain real-time location of boundary sensor and dynamically solve the problem of arbitrary-shape boundary measurement. The imaging results validate the availability of designed sensor system and the effectiveness of the corresponding measuring principle.

Service providers often adopt the mechanism of customer classification due to the heterogeneity of customer waiting cost. However, the classification service may cause unfairness feeling of regular customers, then affect the revenue and social welfare. This paper provides the first exact analysis about the situation that service providers offer two classes of non-preemptive priority service when customer fairness perception is explicitly modeled. We model customer fairness perception as a negative utility on regular customers that's proportional to the waiting time difference between the two queues. By analyzing a stylized M/M/1 queue in monopoly service system, we can derive important results some of which reaffirm existed research results. First, from the perspective of revenue maximization, service providers should adopt the mechanism of customer classification and set up the two kinds of customers where they can see each other. Next, considering customer utility maximization, service providers should cancel the mechanism of customer classification, and keep one queue (regular customers) only. Then, from the perspective of social welfare maximization, service providers should also adopt the mechanism of customer classification but set up the two kinds of customers where they cannot feel each other. Finally, this paper concludes the optimal pricing based on customer classification in the above three different perspectives. This research shows important reference value and practical significance for service providers who adopt the mechanism of classification service.

This research examines the micro-level correlation between traditional marketing actions (TV ads and public relations) and pre-release consumers' social learning about videogame consoles (Wii and PS3, launched in 2006). We evaluate consumers' learning processes via the perusal of information in online communities using "pageview" data for multiple websites from a clickstream panel as indicators. We propose a bivariate Bayesian learning model combined with complementary purchase choices. The proposed model enables simpler estimation of parameters and allows to accommodate detailed information about interactions between social and personal learning processes. From the results, we find empirical evidence that companies' traditional marketing actions have a greater impact on social learning than on regular personal learning during the pre-launch period. When consumers make purchase decisions, their social beliefs about product quality are weighed at least three times more heavily than their personal beliefs. Counterfactual simulations suggest that by optimizing marketing actions, firms can stimulate consumers' learning and promote increased product engagement.

This paper explores the macroeconomic determinants of non-performing loans (NPL) in 19 Asian countries (low to high income economies) using the Generalized Method of Moments estimation approach based on the economic data for the period between 1998 and 2015. The categorization of the economies is based on the average gross national income per capita as set by the World Bank. Specifically, the paper aims to evaluate if the determinants of NPL vary with the income levels of the countries. The results indicate that the NPL is strongly influenced by the inflation rate. The effect is, however, negative in the high-income and the middle-income countries and positive in the low-income countries. The GDP per capita has a dynamic negative relationship with the NPL in the high-income and the low-income countries. The remittance has a significant positive association in the high-income and a significant negative association in the low-income countries. Similarly, the unemployment rate has a positive effect on NPL in the middle-income and the low-income countries. With the rise in the official exchange rate, the NPL level increases in the low-income countries. The overall estimation results suggest that the NPL in Asian banking system depend on some key macroeconomic variables, such as unemployment rate, inflation rate, official exchange rate, remittance received and gross domestic product per capita, and these associations vary with the income level of the countries. Therefore, economic level of a country should be carefully considered while formulating credit policy to minimize credit risks in the banking system.

The management strategies of a firm are inevitable affected by individual behavior preferences. The effect of individual preference on the evolutionary dynamics for supply chains is studied by employing replicator dynamics. Each firm has three behavior preferences: selfishness, fairness, and altruism. Firstly, the case that the strategy set of manufacturers and retailers including two pure strategies is considered and the effect of preference parameter on the equilibrium outcome in the shortterm interaction is discussed. Secondly, the equilibrium state in the short-term is always disturbed because the change of the environment, firm's structure, and so forth. Using the replicator dynamics, the evolutionary stable strategies of manufacturers and retailers in the long-term interaction are analyzed. Finally, the extend case that the strategy set of manufacturers and retailers include three pure strategies is investigated. These results are found that the strategy profile in which both manufacturer and retailer choose fairness or altruism, or one player chooses fair or altruistic strategy and the other player chooses selfish strategy may be evolutionary stable, the stability of these equilibria depends on the the preference parameters.

This paper deals with the issue of priority setting in health care under uncertainties about the severity of the illness and the effectiveness of medical treatment. We examine the effect of a disease uncertainty (a treatment uncertainty) on the allocation of health care resources in the presence of a treatment risk (a disease risk) and identify preference conditions under which the social planner allocates more resources to higher risk population. We allow for the simultaneous presence of two risks and investigate the joint effect of two-source uncertainties on health care allocation when the two risks are either small or positively quadrant dependent. The effect of inequality aversion on health care allocation is also analyzed by introducing an equity weighting function. Our work extends the previous model of health care priority to two-risk framework and provides new insights into the problem of health care decision making under uncertainty.

With the vigorous development of equipment manufacturing industry in China, higher requirements to the equipment supportability are put forward. How to evaluate the supportability of equipments (especially the aviation equipment-aircraft) objectively and correctly is the problem to be solved in the development of aviation equipments construction, demonstration and battle application. Aimed at the needs of the supportability analysis of complex equipment systems-aircraft, a model of aircraft support concept evaluation based on DEA (data envelopment analysis) and PCA (principal component analysis) is proposed. The model is used to evaluate a certain aircraft support concept. The process and the results of evaluation show that proposed model is feasible and effective. The model is suitable for advanced aircraft support concept evaluation. The feasibility and effectiveness of the proposed model is verified by the analysis of the evaluation results. This method is applicable to the evaluation of aircraft support concepts.