This paper builds an information diffusion framework, in which investors gradually modify their ex ante over optimistic expectation and causes the asset price trajectory to exhibit a trade-off relationship between duration, amplitude and volatility. Empirical analysis using data from international stock markets confirms the existence of this trade-off relationship and shows its ability to explain the shape of China's bear stock markets. It is revealed that the sudden stock market crash in 2015 is a recurrence of its own periodicity. Our analysis reveals that volatility, as a conveyor of information, relates duration and amplitude of a bear market, and proposes a new theoretical perspective and analytical tool for government control over market fluctuation and long term investment strategy.
It is the fact that the sample distributions of trusted users and default users in P2P platform are unbalanced and the investors have different acceptable degrees in classification error. This paper establishes the disequilibrium fuzzy proximal support vector machine (DFPSVM) to assess the borrowers' credit risk in P2P platform by using the bilateral weighted error measuring method and mapping distance to measure fuzzy memberships of the positive and negative samples error term. Next, it proposes the borrowers' credit scoring and credit rating model in P2P platform. Finally, the empirical results based on the borrowers' information in Renrendai platform show that the proposed DFPSVM model has better generalization ability and higher classification accuracy than other existing models. It can effectively reduce the effect of disequilibrium samples and increase the classification accuracy of negative samples. The obtained borrowers' credit score, credit rating and the distribution of default rate is helpful to control the default risk of P2P platform and support the decision making process.
Trade credit and bank financing are two main patterns of supply chain finance. Based on retailers' heterogeneity, this paper proposed a core enterprise leading trade credit financing model. We analyze how retailers' heterogeneity affects the core enterprise's optimal choice of supply chain financing pattern. The results show that the optimal trade credit distribution strategies for the core enterprise uniquely exist, and that the core enterprise prefers retailers with severe capital-constrained while it refuses to distribute trade credit to other retailers under some circumstances. By comparing trade credit to bank financing, we find that under the condition that the external financing cost is not absolutely advantageous or disadvantageous, the core enterprise prefers trade credit financing pattern when the heterogeneity of retailer is large, but it prefers bank financing pattern when the heterogeneity of retailer is small.
The non-parametric estimator of volatility based on high frequency data is the current focus due to its high accuracy. All of these estimator have to choose their optimal bandwidth in the application. However, it is difficult to calculate the optimal bandwidth from the real data and to apply these estimator, since optimal bandwidth always take some awkward unknown parameters. In this paper, taking realized kernel as the representative, a new data-driven algorithm for selecting the bandwidth has been constructed. The stability of algorithm is proved and the selected bandwidth is consistent estimator of optimal bandwidth without bias. The convergence rate is O(n-1/5). It is shown from the numerical examples that the algorithm is adaptive and the finally selected bandwidth is independent on the original value. Simulation result shows that the estimator for volatility with bandwidth selected by our algorithm has higher accuracy. The proposed algorithm could be modified to select optimal bandwidth for other non-parametric estimator of volatility as well.
Reducing the CO2 emissions of China's high energy-consuming industry is one of the most important prerequisites in achieving the target of peaking its total CO2 before 2030. In this paper, we build a theoretical model on identifying the determinants of CO2 emissions by incorporating the energy-augmenting technical progress in the analytical framework. Based on this work, the growth rates of CO2 emissions in China's six high energy-consuming industries are decomposed into four aspects including energy mix effect, scale effect, factor substitution effect, and energy-saving technical progress effect; and the peak paths of each industry's CO2 emissions are also studied using scenario analysis method. The results indicate that:1) the scale effect plays a dominant role in increasing the CO2 emissions for all the industries, especially during the tenth five-year plan; on the contrary, energy-saving technical progress effect and factor substitution effect have limited the increase of industrial CO2 emissions to a certain extent; the energy mix effect is extremely minor. 2) The energy-saving technical progress is the most effective way to reduce the CO2 emissions for four industries although the effect decreased gradually, and the rest two mainly rely on factor substitution effect. 3) In the high emission scenario, the total CO2 emissions of the six high energy-consuming industries continue to grow rapidly, and it is difficult to reach the peak before 2030; in the medium and low emission scenarios, the total CO2 emissions of China's high energy-consuming industry will peak around 2017 and 2023, respectively.
By introducing embedded low-carbon services, carbon emission-dependent enterprises are often faced the project risk of low-carbon energy saving level information asymmetry. In view of this, this paper considers the investment level of low carbon service provider (LCSP) and the design of the optimal incentive contract between embedded low-carbon service participants with unobservable low-carbon energy saving capabilities under the background of low-carbon economy. In the meantime, by analyzing the optimal incentive contracts in this situation, we introduce "embedding degree" to describe the common value of the embedded low-carbon service participants. The influence of the embedding degree on the optimal incentive contracts is analyzed. The research shows that low level of embedding degree will lead to a larger project risk for low carbon service providers and a contract mode that requires the low carbon service providers to advance the deposit on each period. High level of embedding degree can promote low carbon emission reduction efficiency of embedded low-carbon services.
Since 2011, the Chinese government has issued a series of fund policies (The Fund Policy) on recycling and disposal of waste electrical and electronic equipment (WEEE). This has had a great impact on the Chinese closed-loop supply chain (CLSC), causing structure reorganization. We study this structure reorganization and the dominant modes of CLSC. There are four dominant modes formed by The Fund Policy's impact, are studied in this paper. And the optimal decisions of the four dominant modes are compared analysis. This study found the following:1) In the four dominant modes, manufacturers and retailers are not motivated to adjust the selling price of products; the differences between the manufacturers and the retailers' bargaining power is reflected in the wholesale price. 2) The dominant power of each member of the CLSC is closely related to the profit of the members, which means whichever member holds the greater portion of the channel power, the higher their profits are. However, the profit of the sellers is not affected by the reverse channel dominant mode, the profit of the dismantling enterprise is not affected by the forward channel of the dominant mode, and both the recycling and transfer prices are not affected by the forward channel of the dominant mode. 3) The effect of Chinese Disposal Fund to the manufacturers could be either positive or negative. The research conclusion has the certain reality instruction value, and beneficial to implement and perfect The Chinese Fund Policy.
The research background is about the sales of fresh agricultural products in "agriculture-supermarket jointing" and E-commerce channel. Not only the effect of factors such as channel price and cross-price elasticity coefficient of channel are considered, but also the factor of quality loss of fresh agricultural products which change with time is taken into consideration. Then the market demand function of fresh agricultural products is set up and the model is built. The agricultural cooperatives and supermarkets' different optimal discount rates and the market clearing strategies in different situations of disperse policy decision and centralized decision-making are analyzed in different conditions such as no-discounting, one-time discounting and multiple discounting. Furthermore, it analyses the impact of market share of traditional channels, sensitivity of channel price, cross-price elasticity coefficient of channel on the optimal discount rate of farm cooperation, supermarkets and supply chain profit. Accordingly, management suggestions are provided by example simulation. Finally, the influence of different times on the supply chain profits is analyzed and it shows that the times of discounting do not affect the trend of impact of dual channel's parameters on the supply chain profit.
In recent years, advance sales has become a popular way to launch new products. However, in the advance sales transactions may occur the situation that the physical value and consumer valuation are quite different, so that the consumers who choose advance sales run a big risk. As one of the financial instruments to avoid risks, the option can make up for the shortcomings caused by the difference between the consumer valuation and the physical value in the advance sales strategy. So the option theory is introduced into the advance sales strategy in this paper, the option advance sales model is established to obtain the optimal order quantity and the optimal option execution price, then compared with the ordinary advance sales strategy. The study find that there are certain critical values of the range of the consumer and purchase cost, which are the key for the retailer to choose the advance sales strategies. When the consumer is risky, if the valuation reference is greater than zero, the retailer implements the option advance sale strategy; If the valuation reference is less than zero, the choice of the advance sales strategy depends on the cost:if the cost is greater than the critical value, the option advance sales strategy is better, if the cost is less than the critical value, the general advance sales strategy is better. When the consumer is risk aversion, due to the existence of risk resistance items, implementing the option advance sales strategy has more relaxed conditions. Meanwhile, and the bigger the risk aversion coefficient is, the more obvious the advantages of option advance sales.
In this paper, we will discuss how two competing energy-intensive manufacturers facing self-saving and shared savings options choose their optimal energy saving modes. First we develop a multi-stage game model based on the assumption that two manufactures are symmetric, then analyze the equilibrium strategies on the choice between energy saving modes for two manufactures. We show that there are two symmetric Nash equilibriums and that two manufactures will prefer the second mode to the first when the investment cost coefficient ratio of the energy service company to the manufacturer is small; otherwise, two manufactures will prefer the first mode to the second. Furthermore, the basic model is extended to situations with asymmetric investment coefficients of two manufactures, asymmetric initial energy efficiencies of two manufactures, shared time less than the life cycle of the energy system and we also show that the equilibrium strategies on the choice between energy saving modes in these situations. Interestingly, we find that the competition intensity between two manufactures has no impact on the equilibrium strategies in the four situations.
Motivated by the phenomena in intelligent electronics supply chains, we study capacity/output decision-making in a supply chain with limited capacity. Based on game theory and constrained extreme value theory, we build a three-stage game model, characterize the corresponding equilibrium and the profit division amongst the supply chain. We also conduct sensitivity analysis to see the effect of the parameters. We find that the supplier (also as a competitor to the buyer) may leverage the limited capacity to push the buyer out of the market, while an additional buying option does not necessarily neutralize the supply shortage threat for the buyer. Our conclusions can provide meaningful managerial insights with respect to the quantity competition in intelligent electronics supply chains.
This paper studies how enterprise makes production decision of traditional product and green product based on the assumption of perfect rationality and bounded rationality respectively, considering consumers' environmental awareness, under the cap-and-trade mechanism of emission permits. Through the analysis of joint production and separate production of the two products under the above assumptions, some conclusions are drawn. Under the assumption of perfect rationality, there is a range of green subsidy when the two products are jointly produced, which makes the company's total output increases and total discharge decreases with the consumer's environmental awareness increases. At the same time, the output and discharge of joint production are all smaller than those of the separate production, while the total profit is quite the contrary. The above results are still valid under the assumption of bounded rationality. The difference is that the difference of expected production, expected discharge, and expected profit under the two modes are decrease with the increases of the degree of bounded rationality. No matter which production mode the enterprise adopts, the expected production and expected discharge under the assumption of bounded rationality are always higher than Nash equilibrium, while the expected profit is the opposite. What else, the former and the latter increases and decreases with the degree of bounded rationality respectively. Therefore, when enterprises produce multi-category products, joint production has more advantages than separate production, but the advantages will decrease with the degree of bounded rationality increases. What's more, bounded rationality can lead to the formation of "bullwhip effect", which must be taken seriously by enterprises.
After a disaster, emergency production will become significant method for ensuring emergency material's demand, if the storage can not satisfy emergency material's demand. Implementation of the government's mobilization policy and change in raw material supply was introduced to describe its influence to emergency material production capacity. A multi-objective programming model for emergency material production and transportation was established to minimize the time of emergency material to the disaster area and minimize the total production and transportation cost. The model contained multiply species of raw material suppliers, multiple emergency material manufacturers and one affected area. The paper applied the NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm Ⅱ) to the solution of the model and produced Pareto optimal solution set, which could provide decision makers with a variety aids decision-making solutions of emergency materials mobilization production. Simulation case analysis shows that the government needs to implement a plan to mobilize manufacturers and suppliers in order to maximize the manufacturer's productivity advantage.
Government, who has a primary stake in social safety and welfare, undertakes the task of monitoring enterprises to make sure that they reserve and replace emergency supplies and equipment as required. Nevertheless, the reality is unsatisfactory, which is caused by the hidden information about the reserves' quality and performance. This paper considers a kind of emergency supplies and equipment that are needed to be replaced periodically to guarantee their usability and availability. A government-enterprise game model is established to analyze the enterprise's shirking behaviors and the government's monitoring strategies. The experience based equilibria are generated from a reinforcement learning algorithm. Results demonstrate the effectiveness of utilizing the experience-learning method to solve this kind of moral hazard problem. This study further puts forward managerial implications by analyzing enterprise's replacement strategies and government's monitoring patterns when the enterprise faces different levels of social losses.
Governments, civilians and terrorists have different time preferences as terrorism becomes increasingly permanent. The article constructs the anti-terrorist repeated game model with different time preference. Firstly, the paper establishes the stage game model including the two anti-terrorism scenarios:government-civilian collaboration, anti-terrorism failure. Secondly, the players' strategies in the repeated game designs as two types of randomized strategy:one tends to the equilibrium and the other deviates from the equilibrium, then the influence of different time preferences and participant's deviating behaviors on the standardized payment function is compared under the two randomized strategies. Some results are showed in the article:Firstly, as governments or terrorists adopt a strategy of randomization, they will focus on long-term gains and as governments or terrorists adopt strategies that are increasingly irrational and destabilizing, their behaviour will tend to be short-lived. Secondly, due to the government, the civilian and the terrorists are close to a zero-sum game in the game against terrorism, any one taking the disequilibrium randomized behavior will lead to other two sides increasing incomes, but there is a collaborative effect between governments and civilians where all is lost or all is glory. Thirdly, if terrorists adopt a randomized strategy closed to equilibrium, governments take full advantage of the terrorists' mistakes to maximize the governments' short-term gains. However, if terrorists take a randomized strategy tendency to deviate from equilibrium, governments are focused on developing a long-term counter-terrorism strategy.
Due to the difference of individuals in the team of science and technology innovation, team members are prone to team process conflict due to the differences in tasks and team goals. Based on the moderating effect of process conflict on team collaboration, this paper constructs a process conflict evolutionary game model that focuses on the self-interest of members of S&T team and analyzes the evolutionary stability of the strategy (keeping proper process conflict). The results show that when the net income of team members choosing conflict is greater than the excess return of choosing cooperation, the team members will gradually produce destructive conflict (non-cooperation) in the process of long-term evolutionary learning; the net income of team members choosing conflict is less than the choice during the long-term evolutionary learning process of members, due to the limited rationality of the team members, they can not achieve full cooperation with each other through the learning ability of the members of the game. However, by influencing the team members in the outcome of the evolutionary game, strategic choice of the key parameters of the adjustment, you can gradually generate constructive conflicts, so that the team members to maintain the purpose of cooperation with each other.
To optimize the ship scheduling in multi-harbor basin port, this paper focuses on the influence of large ships in/outbound harbor with tidal condition. A safe navigation distance for ships in/outbound harbor in one-way channel, in/outbound harbor time alternating conditions and berth limit of continuous berth is considered. A mixed integer linear programming model is proposed to minimize the total waiting time of all ships. Based on the characteristics of ship scheduling optimization, the original problem is decomposed into five sub-problems to obtain initial solution, and the improved harmony search algorithm is designed to solve the problem. In the numerical experiments, the proposed algorithm is compared with the lower bound of the model to illustrate the validity of the algorithm. Results show that the average relative deviation is 2.19%, and the computation time is less than 1 minute. The analyzing results are compared with those under two existing scheduling rules, in which the optimization rates of average objective values are 13.30% and 27.35% respectively for different experiment sizes. The proposed model and algorithm can significantly improve the service efficiency of the port for ships and verify the effectiveness of the schedules.
In the highway PPP projects, the traffic uncertainty and inflation would have a serious effect on revenue of private sectors. Government usually provides government guarantees, such as minimum traffic guarantee (MTG), tariff and so on, to attract private sectors. In order to evaluate the value of government guarantees, this paper established model of MTG, model of tariff and model of traffic cap (TC). The binomial lattice and Monte Carlo simulation was adopted to simulate operating traffic in these models. Finally, these models were applied to a highway PPP project. The authors find that the impact of tariff on the net present value is greater than that of minimum traffic guarantee in the project. In addition, as for the project, the optimal government guarantee option is a compound option of MTG and TC. The proposed models not only can help government develop reasonable guarantee policies but also can help private sectors to make better investment decisions.
To analyze the policy variables and interaction factors of provincial level medical treatment demands, and control heterogeneity of area and time effect, to provide a scientific basis for the estimation of medical reform effects and regional rational distribution of resources. With control variables of medical supply and demand variables, regional characteristics to establish the index system, post-double-selection-LASSO method is used to select potential variables and function forms. The contrasted results of first difference, all control variables and standard deviation cluster model shows that, the standard deviation cluster model is better control the time trend and initial differences, especially confirmed the complex medical treatment demands factors including hospital scale, medical services, medical price, regional characteristics, interaction and medical income and population density etc. It should considered transfer path of different instrumental variables, different regional characteristics and initial differences, interaction factors on medical behavior, in order to achieve the equalization of medical resources in space.
There are two problems within the approach built on Choquet integral model (CIM) for hierarchical multiple attribute decision-making (MADM), called TOYLC analytic hierarchy process (shorted as TOYLC-AHP). First, it does not in effect guarantee the satisfaction commensurability that CIM requires. Second, the method that is built on the measuring attractiveness by a categorical-based evaluation technique and that TOYLC-AHP adopts to determine the capacities of attribute sets, also suffers from making judgements on non-specific alternatives. To develop TOYLC-AHP, the prescriptive commensurable method (PCM) for alternatives' satisfaction values on multiple attributes, is proposed to measure the commensurable satisfaction values of alternatives' performances on the specific attributes in a hierarchy. Based on PCM, a judgement mode for determining attribute-set capacities, similar to the swing weighting mode widely used in simple MADMs, is firstly presented to help dicision-makers make meaningful preference comparisons. Then, based on the new judgement mode and PCM, a new approach called targets-oriented AHP for ordinal preference dependence (ToAHPOPD) is given to substitute for TOYLC-AHP. A case study shows that ToAHPOPD is able to make better discriminations on alternatives than TOYLC-AHP, and thus verified to be superior to TOYLC-AHP.
The probabilistic linguistic term set (PLTS) in which each provided linguistic term has a probability, make the initial decision information can be fully utilized, to improve the scientificity of the multiple criteria decision making with linguistic term set. Taking this problem into account, this paper proposes a multiple criteria decision making method based on the probabilistic linguistic entropy and probabilistic linguistic cross entropy weighting method for probabilistic linguistic multiple criteria decision making environment. We first calculate the weights of the criteria by using probabilistic linguistic entropy and probabilistic linguistic cross entropy, considering both the single effect of each PLTS under each criterion and the interaction between any two PLTSs. Then, the probabilistic linguistic term set alternative queuing method (PLTS-AQM) combining with directed graph and 0-1 precedence relation matrix is developed to more intuitively get the ranking results of alternatives and their predominance degrees. Finally, an illustrative example solving a hypothetical enterprise strategy planning problem is shown and the comparison analysis with the existing methods is given to demonstrate the applicability and superiority of the proposed method.
In the complicated condition, the states of the complex equipment system of compliant mechanism, in the whole life cycle, often degenerative transfer since the critical components are very easy to generate fatigue damage. To deal with the difficult problem of the assessment of the system reliability during the dynamic degradation process, this article proposes a novel multi-state degenerate homogeneous continous time Markov process (HCTMP) model which combines HCTMP with the dynamic degradation mechanism of the complex equipment system of compliant mechanism. Then, we simulate the risk of degradation failure of the complex equipment system of compliant mechanism by Monte Carlo method. Afterward, the dynamic expected value, dynamic normalized integrated expected value and expected length of time are adopted to assess the complex equipment reliability dynamic degradation processes. The case study results show that the proposed approach can effectively solve the reliability problem of evaluating multi-state dynamic degradation process and provide an important foundation for the maintenance of replacement decision-making.
In this paper, the analysis method of STPA is introduced to analyze the flight safety in approach and landing, and the occurrence of unsafe events in the approach and landing stage is regarded as the emergent characteristic of the system, and the safety problem is transformed into the control problem. This paper firstly introduces the background of the research, the shortcomings of the traditional security analysis methods and the application of the STPA method in other fields. Secondly, the basic principles of the STAMP model and the STPA analysis method are briefly introduced, which lays the foundation for the safety analysis of the approach flight. Thirdly, the STPA method is applied to the flight safety analysis in approach and landing, which defines the unsafe control action at this stage, analyzes the scene of the unsafe control behavior; taking the action that providing incorrect height, velocity and slip angle (UCA1) as example, we analyze the specific causal factors of this unsafe control action of pilots. Finally, based on the simplified kinematic calculation framework, the kinematics calculation and analysis is carried out. This paper makes a specific calculation of UCA1, and carries out the quantitative safety analysis based on STPA, which shows the effectiveness of STPA method in the safety analysis of approach and landing.
Considering the problem of civil and military integration pre-decision evaluation for military information system projects, an improved AHP and cloud model based civil and military integration demand degree evaluation model for military information system projects is proposed, which explored a new approach to study the civil and military integration evaluation work. The normal interval number is used to express the evaluation data, and then the interval number backward cloud generator is used to generate the cloud model of each evaluation indicator. Finally the normal cloud model integrated similarity measure method based on shape and distance is used to comprehensively evaluate the civil and military integration demand degree. The simulation experiment results show that the proposed evaluation model is reasonable and feasible.