Recent frequent “thunderstorm incidents” of the internet financial platforms (IFPs) have caused the panic of investors. In order to measure and reduce the investment risk of IFPs, the focus of this study is to evaluate the reputation of IFPs regarding investment risk. First, the reputation evaluation indicator system of IFPs is constructed from two dimensions of direct and indirect reputations. Then, based on this system, an improved fuzzy evaluation approach (IFEA) integrating the method of fuzzy comprehensive evaluation (FCE), the analytic hierarchy process (AHP), and the factor analysis (FA) are proposed for evaluating the reputation of IFPs. Finally, a case study based on the data of 20 peer-to-peer (P2P) lending platforms from “Home of Online Loans” (HOL) in China is used to illustrate the IFEA. Results show that the IFEA can reduce uncertainty and randomness in the determination process of indicator weight and membership degree and therefore accurately obtain the reputation level of IFPs and help investors make better decisions. Meanwhile, the key factors in determining the reputation of IFPs are identified, thereby improving the reputation level of the IFPs.

To reduce the economic losses caused by debt evasion amongst lost-link borrowers (LBs) and improve the efficiency of finding information on LBs, this paper focuses on the cross-platform information collaborative search optimization problem for LBs. Given the limitations of platform/system heterogeneity, data type diversity, and the complexity of collaborative control in cross-platform information search for LBs, a collaborative search model for LBs’ information based on multi-agent technology is proposed. Additionally, a multi-agent Q-learning algorithm for the collaborative scheduling of multi-search subtasks is designed. We use the Q-learning algorithm based on function approximation to update the description model of the LBs. The multi-agent collaborative search problem is transformed into a reinforcement learning problem by defining search states, search actions, and reward functions. The results indicate that: (i) this model greatly improves the comprehensiveness and accuracy of the search for key information of LBs compared with traditional search engines; (ii) during searching for the information of LBs, the agent is more inclined to search on platforms and data types with larger environmental rewards, and the multi-agent Q-learning algorithm has a stronger ability to acquire information value than the transition probability matrix algorithm and the probability statistical algorithm for the same number of searches; (iii) the optimal search times of the multi-agent Q-learning algorithm are between 14 and 100. Users can flexibly set the number of searches within this range. It is significant for improving the efficiency of finding key information related to LBs.

In China, many Internet financial platforms (IFPs) are grappling with sustainability challenges due to elevated default rates, which have triggered widespread investor anxiety. To evaluate the sustainability practices of these platforms, we propose a reputation evaluation model designed to rank IFPs based on their sustainability. The economic sustainability of an IFP is decomposed into three components: scale strength, capital liquidity, and sustainable operating capability. Through an analysis of the correlation relationships between various indicators, we have identified nine significant indicators. Mathematical models are established to quantify these nine indicator variables. Subsequently, the score values of each indicator are integrated to establish a reputation evaluation model utilizing the weighted geometric mean method. Furthermore, the reputation evaluation values for 18 Chinese IFPs were calculated using the developed model, and the sustainability of the platforms was ranked according to the reputation evaluation value. A comparative analysis was also conducted between the sustainable rankings proposed in this study and the development rankings of the “Home of Online Loans” (HOL). The results reveal that our model effectively considers both the current operational strength and the sustainable development capability of the platform. It successfully identifies platforms with poor sustainability, assisting investors in making more informed decisions. Simultaneously, this study identifies key indicators influencing the sustainability of IFPs, providing valuable insights for managers seeking to enhance the sustainable operational levels of their platforms.

The entry of potential competitors disrupts the original market equilibrium, which in turn hinders the adoption of RFID technology. This paper focuses on a supply chain consisting of one supplier and two retailers in a master-slave relationship. It develops a decision model for RFID investment, employing a simultaneous game to analyze how RFID technology influences the revenue and strategic behavior of supply chain members. Based on numerical analysis, the key findings of this study are as follows: (1) The adoption of RFID technology increases the revenue of both retailers to some extent. (2) The incumbent retailer demonstrates a stronger ability to withstand risks. (3) The maximum label cost and revenue-sharing rate that suppliers can bear are influenced by the market equilibrium in the absence of RFID. This study offers both theoretical and practical insights for promoting RFID technology in competitive markets.

Dengue fever is an acute infectious disease transmitted by Aedes mosquitoes, posing a threat to public health. As vaccines are not widely available, non-ph