As a very important passenger transportation model in the era of sharing economy, the online ride-hailing (ORH) has also caused new traffic management issues while improving resource allocation. Although regulations and policies have imposed macro-level supervision on the ORH market, they have not prevented some drivers from cheating on platforms' subsidies and jeopardizing passengers' safeties at the source. In order to realize the voluntary and sustainable ORH supervision, and enable relevant participants to actively supervise, report and comply with rules, this paper constructs an evolutionary game model among the platform, passengers and drivers. Based on the bounded rationality and expected benefits of the participants, the main factors determining the optimal strategies are analyzed. At the same time, the evolution path and the equilibrium state of the three game groups are studied by numerical simulation. The results show that important factors of realizing the benign supervision of ORH include minimizing the reporting costs of passengers, making penalties for drivers who violate the rules far greater than the illicit incomes, realizing the platform supervision costs less than the sum of penalty incomes and positive social effects. In addition, improving rewards for reporting can promote the continuity of passengers' participation but increase the possibility of false reports. Therefore, the platform needs to consider the cost of identifying false information when designing the reward amount.INDEX TERMS Tripartite evolutionary game, online ride-hailing platform, active supervision, passengers and drivers, numerical simulation.
In the decision-making process, it often happens that decision makers hesitate between several possible preference values, so the multiattribute decision-making (MADM) problem of hesitant triangle fuzzy elements (HTFEs) has been widely studied. In related research works, different operators are used to fuse information, and the weighting model is used to represent the degree of difference between information fusion on various indicators, but the mutual influence between information is often not considered. In this sense, the purpose of this paper is to study the MADM problem of the hesitant triangular fuzzy power average (HTFPA) operator. First, the hesitant triangular fuzzy power-weighted average operator (HTFPWA) and the hesitant triangular fuzzy power-weighted geometric (HTFPWG) operator are given, their properties are analyzed and special cases are discussed. Then, a MADM method based on the HTFPWA operator and the HTFPWG operator is developed, and an example of selecting futures products is used to illustrate the results of applying the proposed method to practical problems. Finally, the effectiveness and feasibility of the HTFPA operator are verified by comparative analysis with existing methods.
As one of the most important grain protection policies in China, the minimum purchase price policy prevents the fluctuation of grain output and protects the interests of farmers by regulating the prices of major grain varieties. For developing countries with a shortage of agricultural resources, represented by China, an in-depth study on the implementation effect and public satisfaction of this policy is of great significance for promoting the sustainable development of the grain industry. Based on the interest demands of the government, farmers, grain enterprises and consumers, this paper constructs a policy satisfaction evaluation model based on the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation. The research shows that the implementation effect of this policy has promoted the sustainable development of China’s grain in four aspects: improving farmers’ enthusiasm for planting, optimizing the structure of supply and demand, reducing the adverse impact of disasters, and ensuring the steady increase of output. However, due to the differences in natural resources and folk customs, the implementation effect of this policy varies in different regions.
The development of Internet technology and the rise of social networks have expanded the means of product information dissemination. Nowadays, consumers can obtain not only product quality information through real life contacts, but can also obtain product cognitive information through virtual networks, which constitute consumers’ information perception together. However, information in the market can be controlled, and companies can change the perceptions of their consumer base towards their products by enhancing the dissemination of information on the Internet, thus achieving higher corporate revenue. This article aims to study the evolution process of market demand under the control of consumers’ information perception, and a two-layer network model consisting of a cognitive information layer and a quality information layer were constructed. In order to improve product information dissemination efficiency, the opinion leaders who are more active in responding to mentions of the product across social networks are selected, and these opinion leaders are influenced in a stepwise manner using the maximum influence model, thus investigating the relationship between resources and corporate revenue. Using scale-free networks for simulation analysis, there are three main conclusions. First, the cognitive information and quality information of the product could affect market demand. Second, product demand and company profits would increase significantly if key individuals were added to the cognitive information layer. Third, the incremental marginal effect of key individuals decreases as their number increases.
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