Data ownership can either be considered as an indivisible monistic right or separated into ownership and usufruct, but the economic significance of data ownership and usufruct separation remains controversial. Digital enterprises' data-based services incentivize users to provide data, but also result in loss of privacy for users through personalized pricing. This paper constructs a monopoly firm model to explore the impact of data collection behavior and welfare under the definition of lack of usufruct and possession of usufruct when there is personalized pricing and privacy loss. The study finds that without usufruct, data collection will not occur if the privacy cost is high, but excessive data collection will occur if the privacy cost is low. When the collector has the initial usufruct and the user has ownership of the data, efficient data collection can be achieved, improving user surplus and social welfare. Data ownership only affects the distribution of social welfare between the collector and user. Therefore, we should consider the right definition framework of data ownership and usufruct separation and discuss the ownership of data usufruct in different scenarios. When the privacy cost is low, data collectors should be granted usufruct, and users should be allowed to exercise ownership through the "deletion right" and other ways to improve social welfare and user surplus. When the cost of privacy is high or users underestimate the cost of privacy, the method of prohibiting data collection should be adopted to protect sensitive data, rather than confirming rights.

In the era of the digital economy, the overlap between antitrust and information protection issues poses a great challenge to the formulation and implementation of public policies. This paper constructs a duopoly model where there are vertical differences in product quality and consumers' sensitivity to the quality of different products is negatively correlated. It explores the impact of different information protection policies with different strength on the pricing strategies of duopolists and consumer decisions and analyzes the specific welfare effects of policies. The conclusion indicates that in the market where consumers' sensitivity to the quality of different products is negatively correlated, a strong information protection policy leads to the impairment of social welfare, and vice versa. The reason is that information weakens the monopoly power by promoting competition. Meanwhile, considering different product characteristics, a strong protection policy and a weak one exert different effects on consumers' surplus and manufacturers' profits because information will bring about the "monopoly effect" and "competition effect", both of which play against each other to make the market present different welfare distribution patterns. Thus, the conclusion of this paper has certain policy implications for promoting and deepening the actualization of China's antitrust and information protection.

By considering the imperfect competition among platforms and sellers, this paper investigates the incentives behind the "pick one of two" exclusive dealing used by asymmetric platforms, as well as potential effects on market competition, consumers, and welfare. The study finds that only when the platform differentiation is sufficiently small and the homogeneity among sellers is strong, can dominant platform exclude its competitor by signing exclusive contracts with sellers in the form of "pick one of two." At this time, the "pick one of two" behavior will increase the commission level set by the platform, raise the price of seller's products, and ultimately reduce consumer surplus and social welfare. Therefore, prohibiting platforms from implementing "pick one of two" may not necessarily increase the profits of sellers and competing platforms, but it can increase consumer surplus and welfare. This suggests that the purpose of prohibiting the use of exclusive dealing such as "pick one of two" by platforms should not be to protect sellers or small competitive platforms, but to maintain consumer interest and guarantee the efficiency of platform markets. The paper clarifies some of the controversies in practice, provides theoretical support for the judgment of the anti-competitive effects of platform "pick one of two," and provides practical guidance for regulating such behaviors in the platform economy. To determine whether the dominant platform's "pick one of two" behavior should be considered as an abuse of market dominance, it is necessary to comprehensively consider factors such as the number of sellers within the platform, the contract forms provided by the platforms, and the contract information disclosed by the platform, in addition to focusing on the degree of differentiation between platforms and the degree of competition among sellers within the platform.

This paper is based on the bilateral market theory and considers the asymmetric market scale between platforms. We divide the merger model into two types: Strong expansion and weak self-rescue. The decision-making changes of platforms are analyzed when adopting different operation strategies (independent or combined operation). This paper also examines the merger strategies of asymmetric platforms under the competitive conditions and the changes in total social welfare and anti-monopoly supervision. The results indicate that, first, if the platform chooses the strong expansion merger mode, it tends to pursue the strategy of merger operation to obtain greater profits, assuming there is no "merger paradox" phenomenon. Second, the merger strategy of the self-help of the weak platform is related to the difference in market size between platforms. Third, the platform's merger behavior under different strategies is likely to enhance overall social welfare. Finally, the market supervision department should focus on different aspects of anti-monopoly supervision under different cross-network externalities and post-merger operation strategies.

The ongoing progress in digital technology has simplified the collection, dissemination, interaction, and realization of multi-sourced and heterogeneous data, resulting in changes in the economic operation mechanisms. This paper measures the "data-driven ability" of enterprises from a technological innovation perspective and integrates data elements and an enterprise's "data-driven ability" into the production function. This provides fresh theoretical insights into the intrinsic mechanism by which data elements enable manufacturing companies to reduce costs and improve efficiency. We then get the following results: 1) The generation, sharing, and application of data can significantly enhance the production efficiency of manufacturing companies; 2) Further comparison of the processes and differences in the release of data value in companies of different technology intensities reveals that the current application of digital technology and the input of data elements can effectively reduce the manufacturing cost of low tech (LT) and medium tech (MT) manufacturing companies and the sales cost of high-tech (HT) companies; 3) Interestingly, when HT companies exhibit stronger data-driven abilities, their production costs tend to rise, indicating that China's HT manufacturing companies are yet to achieve a successful transition in production models. Furthermore, based on the industrial correlation theory and industrial agglomeration theory, this paper incorporates spatial factors into the analytical framework. The findings indicate that, from the industrial chain perspective, the integration and development utilization of data across the entire manufacturing process, industry chain, and product lifecycle can improve the collaborative efficiency of the regional industrial chain. This in turn can further enhance the production efficiency of companies. This research enriches the theory and mechanism analysis of digital economy and provides evidence support for further promoting the value release of data elements and optimizing the efficiency of resource allocation.

Developing the digital economy to boost common prosperity is a focus topic at the moment that has attracted wide attention. As an important measure of common prosperity, whether social intergenerational mobility can be improved in the digital economy era is rarely discussed in the literature. This paper seeks to systematically examine the role of the digital economy on intergenerational income mobility, both theoretically and empirically. The research found that: 1) On average, the digital economy significantly reduces intergenerational income autocorrelation, which reflects the improvement of intergenerational mobility. After various robustness tests, the conclusion still holds. 2) The mechanism is that the development of the digital economy promotes the accumulation of human capital among family offspring, stimulates urban entrepreneurial vitality, and optimizes the economic structure to improve intergenerational mobility. 3) In rural and inland areas, the effect of intergenerational mobility in the development of the digital economy is better. The research in this paper can provide new policy insights on how to smooth social mobility and achieve common prosperity in the digital age.

Based on the research sample of Chinese A-share listed enterprises from 2010 to 2021, this paper empirically tested the synergistic effect of bank digitalization and enterprise digitalization in promoting the green transformation of enterprises. The study found that both bank digitalization and enterprise digitalization contribute to promoting enterprise green transformation, and bank-enterprise digitalization has a synergistic effect on promoting enterprise green transformation. This conclusion is still valid after considering endogeneity problems and a series of robustness tests. The results of mechanism test show that the synergistic effect of bank-enterprise digitization in promoting enterprise green transformation is due to the "information synergistic effect", "credit allocation synergistic effect" and "internal governance synergistic effect". Based on the test of the enterprise characteristics, it is found that the synergistic effect of bank-enterprise digitalization is more effective for non-state-owned enterprises, small-scale enterprises, heavy polluting enterprises and high-tech enterprises. Based on the test of the external environment of enterprises, it is found that the synergistic effect of bank-enterprise digitalization is more effective for regions with stronger environmental regulations and more intense bank competition, as well as for competitive industries. The additional test of the effects of enterprise green transformation found that green transformation promoted the carbon emission reduction without sacrificing the development of enterprises. This study provides some policy implications for giving full play to the synergistic effect of bank digitalization and enterprise digitalization, so as to empower enterprises with green transformation through digitalization.

As an important way of integrating digital economy with real economy, enterprise digital transformation has become an important way of enterprise strategic change in recent years. Using the sample of A-share listed companies in the Shanghai and Shenzhen Stock Exchange from 2007 to 2022, this paper empirically analyzes the influence of enterprise digital transformation on executives' opportunistic stock selling and its influencing mechanism. The results indicate that the enterprise digital transformation has significantly promoted the possibility of executives' opportunistic stock selling. This conclusion still holds after a series of endogenous and robustness tests such as instrumental variable estimation (IV) and propensity score matching (PSM). The mechanism analysis shows that the enterprise digital transformation affects executives' opportunistic stock selling by improving investors' optimistic sentiment and innovation investment. Further tests show that the positive relationship between enterprise digital transformation and opportunistic stock selling is more significant in companies with high divergence in analyst predictions, poor legal environment, and low audit quality. This paper combines digital transformation with the executives' opportunistic stock selling, enriching the relevant research in these two fields. The conclusion provides empirical evidence for controlling the chaos of opportunistic stock selling and promoting high-quality development of capital markets. At the same time, this paper provides a useful exploration for empowering the real economy with digital technology and promoting industrial digitization.

While the application of industrial robots enhances production efficiency, does it also impact the health of workers? This paper, leveraging data from the IFR (International Federation of Robotics), investigates the health effects of industrial robot application both from a broader perspective of workers' right to life and health and a detailed examination of individual mental and physical well-being. The research reveals that the application of industrial robots has created a health dividend for workers. At the macro level, it has significantly reduced the rate of occupational accidents and the number of casualties, safeguarding workers' right to life. A micro-level analysis indicates that the mental and physical health of workers, particularly those in lower-tier positions, has improved. The utilization of industrial robots has shortened their working hours and enhanced job safety and workplace satisfaction, thereby playing a crucial "safety-net" role. The findings of this paper contribute to a deeper understanding of the social benefits of the current application of industrial robots. They offer more precise policy recommendations for balancing safety with development, genuinely enhancing workers' sense of well-being and accomplishment, and achieving high-quality development.

There is wide consensus that social trust is a fundamental asset for economic development. A large literature suggests that trust crucially depends on social interaction and information. The use and popularity of mobile social networks (MSN) have profoundly changed the way of social interaction and information dissemination. Yet, evidences on whether and how they affect social trust are rare. This paper uses the establishing of community WeChat group as a quasi-natural experiment to estimate the impact of MSN on social trust. Using data from China household financial survey (CHFS) and taking the difference-in-differences (DID) and difference-in-difference-in-differences (DDD) as empirical strategy, we find that WeChat group can increase the level of social trust by 0.109 standard deviations during the sample coverage period (2013-2017), which slows down the decline of social trust by about 27%. Further mechanism analyses show that the enhancing effect of MSN on social trust does not arise through the channel of facilitating social interactions, but rather through the superimposed effect positive and negative information dissemination. This paper emphasizes that the content of information dissemination in MSN is the key to reshape social trust.

The digital infrastructure is an important foundation for the construction of a strong network and manufacturing power, and it is also a strategic public resource for the socio-economic development of contemporary China. In this context, this paper mainly focuses on the impact of China's digital infrastructure construction on urban labor employment and its operating mechanism. By treating the "Broadband China" pilot policy as a quasi-natural experiment, this paper is based on panel data from 285 prefecture-level and above cities in China from 2007 to 2019, and employs a multi-period difference-in-differences (DID) model to thoroughly examine the direct effects and underlying mechanisms of digital infrastructure construction on labor employment. The study finds that the use of digital infrastructure construction represented by the "Broadband China" pilot policy can significantly improve the level of labor employment in pilot cities, a conclusion that holds true even after multiple robustness and heterogeneity tests. Mechanism analysis reveals that digital infrastructure construction enhances labor employment levels through the effects of optimizing industrial structure, investing in human capital, and mitigating labor force distortions. The promotional effect of the "Broadband China" pilot policy on labor employment is more pronounced in cities with higher administrative ranks, better geographical locations, and among high-skilled labor. The research findings of this paper provide important policy implications for promoting the construction of "Digital China" and high-quality employment of labor.

In the current era of the digital economy, the transformation of digital technology has become a significant driving force for enterprises to achieve high-quality development. Taking advantage of the pilot program of "integration of informatization and industrialization" as a quasi-natural experiment and using Chinese listed manufacturing companies from 2007 to 2021 as the sample, this paper empirically investigates the impact of digital technology transformation on the stock price crash risk using a staggered difference-in-differences approach. The findings are as follows: 1) With the pilot program significantly promoting the digital technology transformation of enterprises, the risk of future stock price crash shows a notable decrease. This conclusion is further supported by a series of robustness tests. 2) Mechanism analysis reveals that the pilot program reduces the risk of future stock price crash through improving internal control quality, but not attracting more external monitoring. 3) The effects of the pilot program in reducing the risk of stock price crash are more pronounced when accounting conservatism is low, earnings aggressiveness is high, there is less analyst coverage, and media attention is low. These indicate the existence of complementary effects between emerging digital technologies and traditional governance mechanisms. Our research enriches the empirical evidence supporting the improvement of corporate governance in the digital economy. The conclusions provide decision-making references for enhancing corporate quality through digital governance for listed companies.

E-commerce is an important application field in the digital economy industry. In the interaction sessions between users and goods on the e-commerce platform, the micro-operation behaviors can gain insight into the user's fine-grained purchase interests, and at the same time, the macro multi-period session sequence can reflect the dynamic evolution of user's interests. Therefore, it has become a hot and difficult problem that how to integrate the two to conduct accurate and comprehensive user interest modeling, and then carry out accurate recommendation. The deep recurrent neural network (RNN) has outstanding advantages in processing serial data with periodic and long-term dependencies, and is one of the core methods of AI. Based on this, a hierarchical RNN network: Session-level LSTM_{m ses}, block-level LSTM_{m blo} and user-level LSTM_{m usr}, is designed in the framework of the proposed model MpUIP. Firstly, the user's fine-grained interests are first learned from the micro behavior details in the session. Further, the evolution of user's short-term, medium-term and long-term interests are learned, and the multi-period interests are fused. Finally, on two real datasets, four experiments: model ablation, benchmark model comparison, sparsity evaluation, and real case analysis are conducted. On the two typical indicators of recommendation: Recall@K and MRR@K, the experimental results verify that the proposed model Mp-UIP has best performance than the existing classical models. This confirms that the model Mp-UIP can indeed build a more accurate and comprehensive user interest model by combining the user's fine-grained interests with the multi-period interest evolution representation, so as to serve for the accurate and personalized E-commerce recommendations.

The new generation of artificial intelligence has more significant cognitive, communicate, and creative abilities, and the attribute of general purpose technology is increasingly prominent. The new generation of artificial intelligence will enable high-quality social development through three mechanisms: Innovation enabling effect, promoting the cross-integration of emerging technologies; Promoting the transformation role, promoting the intelligent transformation of thousands of industries; System change potential, will promote the comprehensive innovation of social productive forces and production relations. The technological and social attributes of the new generation of artificial intelligence are highly integrated and unified, and currently, there are still technical problems in its development, such as inadequate infrastructure, imperfect open-source ecology, and insufficient technology implementation, At the same time, there are also social problems such as externality to society, difficulty in defining legal rights and responsibilities, and new challenges to employment reform. It is necessary to grasp the development law of the new generation of artificial intelligence and promote its high-quality development through strengthening system planning, improving ethical regulations, cultivating an innovative ecosystem, improving laws and regulations, innovating regulatory methods, and promoting employment.

Against the backdrop of economic globalization, the rapid advancement of cutting-edge digital technology has catalyzed a new wave of technological revolution. The AIGC technology, represented by ChatGPT, disrupts the technical landscape of traditional artificial intelligence. And it is widely embraced for its enhanced human-like functionalities, thus emerging as a pivotal milestone in the advancement of general artificial intelligence. Through the analysis of ChatGPT's impacts on the education and employment market, this research reveals that the implementation of AIGC technology can enhance social value exchange efficiency and invigorate the education and employment market. However, it also gives rise to legal and ethical concerns such as data privacy infringement. Therefore, management and supervision recommendations are proposed to address potential risks in order to ensure seamless operation of the economy and society.

With the decreasing cost of intelligent capital compared to the labor costs of medium-skilled workers, artificial intelligence (AI) is poised to replace jobs in the middle-skilled segment, leading to labor market polarization. This article constructs a dynamic multi-sector general equilibrium model to compare three strategies to cope with this phenomenon: 1) improving the labor productivity of medium-skilled workers, 2) transitioning medium-skilled workers into low-skilled roles, and 3) upskilling medium-skilled workers into high-skilled positions. Findings reveal that all three strategies can mitigate wage polarization, but transforming medium-skilled workers into high-skilled workers can enhance the overall labor force skill level, reduce income inequality, and promote quality employment and shared prosperity. Automation of high (low) skill tasks will decrease the wages and labor income share of high (low) skilled workers, while the creation of high (low) skill tasks will increase their wages and labor income share. The government increasing the proportion of investment in new infrastructure and reducing the proportion of investment in education can increase total social output, but it will intensify wage polarization. The government increasing the proportion of investment in education and reducing the proportion of investment in new infrastructure will help reduce income inequality, but the economic growth effect will not be as good as Invest in new infrastructure.

Being "too interconnected to fail" has made the risk correlation of financial institutions and its influencing factors a crucial issue in maintaining financial stability. Drawing inspiration from gene regulatory research using random forests, this paper proposes a method to construct a network that captures the relations between different indicators, for the purpose of exploring the influences between risk correlation and its related factors. It is achieved by integrating forest fusion and random permutation. The proposed method overcomes the limitations of traditional regression analysis, Granger causality test, and Bayesian networks, while the introduction of random permutation enhances the model's capability to handle variable heterogeneity. Empirical results based on 46 listed financial institutions in China from 2012 to 2022 demonstrate that the constructed network can identify the direct or indirect impact of different factors on risk correlation and reveal the influence paths of factors. This provides more comprehensive empirical evidence of complex relationships, highlighting the applicability of the proposed approach in addressing this issue and potentially offering a useful tool for financial regulation and risk management.

The generative AI technology represented by ChatGPT, considered as the second information revolution, have transformed the depth of data analysis, offering new perspectives for intelligent internal audits in enterprises. In response to the limitations in the existing audit risk warnings, such as the limited improvement in the generalization capability of traditional machine learning and the insufficient feature analysis dimensions, we propose a method based on the core technology of ChatGPT—A deep autoencoder network. This method aims to pre-determine risks in the critical accounting activity of incoming funds. First, based on influencing factors, audit features are selected and extracted from various perspectives including business matching, term structure, impairment loss, related transactions, individual statistics, and text information. Subsequently, considering the imbalance of risk samples and the temporal characteristics of financial indicators over the operating cycle, an unsupervised and deep learning-based approach is employed. This involves constructing a deep autoencoder (DAE) pre-training model with the addition of an attention mechanism and employing bidirectional long short-term memory (Bi-LSTM) as the network. Additionally, drawing from the concept of multi-task learning, an integrated framework with model transfer is utilized to quantify audit risk probabilities, ensuring the stability of warnings. Finally, real data from enterprise transactions and finances are collected by using big data technology for comprehensive comparative validation of the proposed method. Experimental results indicate that this method effectively and accurately extracts audit features under different warning time windows. In comparison to common practices like supervised learning and iterative clustering, it significantly enhances the precision and robustness of audit risk warnings. Moreover, it also identifies key factors leading to risk, enabling quickly swift localization of audit suspicions. Our study can provide intelligent decision support for enterprises to improve the quality and efficiency of internal audit.

With the explosive growth of investable assets and asset information, portfolio selection faces the dual challenges of high dimensionality in both assets and characteristics. This paper proposes a portfolio selection framework based on machine learning and asset characteristics. Leveraging the inherent advantages of machine learning, the framework utilizes asset characteristics to directly predict portfolio weights, bypassing return distribution prediction in the conventional two-step portfolio management paradigm. The framework is applied to asset allocation research in the Chinese stock market. The research results show that: 1) The proposed investment strategies capture incremental information within high-dimensional characteristics and uncover both linear and non-linear relationships between asset characteristics and portfolio weights, resulting in a significant enhancement of investment performance. 2) Trading friction-related characteristics are the most important indicators for predicting portfolio weights. 3) These strategies yield higher returns on stocks with stricter arbitrage restrictions while exhibiting lower sensitivity to changes in macroeconomic conditions. Under other economic constraints, these strategies remain robust. This paper expands the research framework of modern portfolio theory, contributing to the development of artificial intelligence and quantitative investment.

In recent years, driven by the commercialization of internet of things (IoT) platforms and support from governments at all levels, the process of intelligent and digital transformation in the manufacturing industry has accelerated. Manufacturers are utilizing IoT platforms to develop and produce smart products, providing consumers with smart products and services. Through game theory analysis, the study found that when the government does not provide subsidies, IoT platforms adjust commission rates and transfer payments to incentivize manufacturers to innovate and yield higher market returns. When the government provides subsidies, these subsidies have a similar effect on promoting smart product innovation for equipment manufacturers or consumers. However, when research and development costs are high, government subsidies may not be sufficient to fully incentivize manufacturers to expand smart product production, and platform transfer payments can strengthen the positive effect of government subsidies. Additionally, government subsidies have shown to bring more pronounced economic benefits in promoting manufacturer innovation and increasing market demand for smart products compared to platform transfer payments. This research provides constructive insights for enterprise digital transformation, platform transfer payments, and government policy formulation.

The integration of local aggregation and global integrity in the structure of intelligent collectives arouses the exploration of its underlying mechanisms in the academic community. The current mainstream explanation is the combination of structural embeddedness and structural holes based on social capital, and the discussion of homophily and heterophily based on individual attributes is relatively insufficient. In addition, related work often adopts the method of agent-based modeling, which is divorced from the real network to a certain extent and cannot predict the formation of relationships. Accordingly, this paper uses the method of data analysis and deep learning to explore the driving role of homophily and heterophily in the structural evolution of intelligent collectives. The results show that the interactive networks of two intelligent groups in two different fields investigated in this paper have experienced three stages (namely, loose clustering, chain structure, and small world state), showing good local aggregation and global integrity. During the network evolution, homophily promotes similar individuals to converge and form clusters; heterophily promotes the formation of connections between clusters. This study not only helps to further understand the underlying mechanisms of the structural evolution in intelligent collectives, but also provides a new idea for the analysis of evolutionary mechanisms in complex networks from the perspective of deep learning. Meanwhile, our work also has certain reference significance for the management practice of using social intelligence resources to implement open innovation.

Using natural language processing (NLP) techniques to gain key information from unstructured data, such as corporate texts, news coverage and self-media language, to do financial and economic research which has attracted extensive attention from numerous scholars in recent years and a wealth of research literature has existed. This paper summaries the latest research progress on the application of NLP in financial problems to expatiate text analysis methods using NLP techniques, and focuses on literature about how to use annual reports and news text to study issues in financial areas including corporate finance, asset pricing, risk management, macro-finance and green finance. We evaluate some rough edges in the existing research literature and provide certain research directions for further research in the end.

Based on the theoretical framework of multiple competing platforms, two forms of exclusive contracts are investigated: Traditional exclusive contracts and “pick one of two” contracts. We distinguish the differences in the incentives of the dominant platform to use these contracts, and analyze the impact of the different forms of exclusive contracts on market competition and the revenues of market participants. Our study shows that the degree of differentiation between platforms and between sellers are the core factors in determining the incentives for the dominant platform to use different forms of exclusive contracts and the impact on market participants. When the degree of differentiation between platforms and between sellers are both small, the dominant platform uses traditional exclusive contracts, while when the degree of differentiation between sellers is large, the dominant platform uses “pick one of two” contracts. Although both exclusive dealings reduce consumer surplus and social welfare, the impact on profits of other competing platforms and sellers is uncertain. Traditional exclusive contracts can reduce the profits of all competing platforms, whereas “pick one of two” contracts can reduce the profits of all sellers. This study provides a theoretical explanation for the use of different forms of exclusive dealing of platforms.

Based on the two-dimensional Hotelling model, this paper builds a game model that simultaneously considers the differentiation of platforms and merchants, to explore the adoption of different exclusive strategies by dominant platforms and the impacts of such strategy. The results show that, there are two types of exclusive strategies, namely monopoly-driven and differentiation-driven exclusivity. The monopoly-driven exclusivity can be promoted by low commission rates of strong platform, low horizontal differentiation between platforms, high vertical differentiation between platforms, and high horizontal differentiation between products; Otherwise, the differentiation-driven exclusivity will be strengthened. Second, the differentiation-driven exclusivity is also beneficial for weak platforms. To avoid the monopoly-driven exclusivity, weak platforms can increase horizontal differentiation between platforms and reduce vertical differentiation or commission rates. Finally, fierce competition among merchants can stimulate the differentiation-driven exclusivity implemented by dominant platforms, which in turn reduces competition among merchants and improves their profits. Overall, the research clarifies the drivers of exclusivity strategy of dominant platforms and distinguishes the influences of different exclusivity strategies on both platforms and merchants, providing strong policy implications for the regulation of dominant platforms and anti-monopoly in the platform industry.

Complex decision analyses are often faced with the high-level uncertainty beyond the normal range of common understanding, which is so-called “deep uncertainty”. Generally, the system characterized with deep uncertainty cannot been or has not been known well, and it usually has many components and mechanisms that would interact in a variety of ways and change over time. Deep uncertainty brings unexpected difficulties to system simulation and forecast. In recent years, the research on the simulation approach under deep uncertainty has been becoming one of the important directions in the field of system science. This paper firstly investigates the state of the art in the concept understanding and its cognition development from uncertainty to deep uncertainty, and then summarizes the key features and constraints for system simulation under deep uncertainty. The current mainstream simulation approaches including their modeling thoughts and implementations are also elaborated by systematically classifying the published literature and outlining main trends in modelling uncertain system. On this basis, a dynamic exploratory simulation approach based on data-and-model hybrid is proposed and applied into traffic simulation and forecast. Simulation experiment results show that this approach is a useful pathway to enhance computing system's adaptability to uncertainties and complex changes of real system.

Competitive action and its attack-response is one of the important research branches of competitive dynamics theory. This study takes Chinese listed real estate companies as the research sample, puts inter-firm competitive interaction into a multi-market contact situation which is more in line with the competition reality, and at the same time expands the scope of action to the non-market area, and expands the scope of action to the non-market area to explore the impact of multimarket contact on the rent seeking actions of enterprises. The results show that: Firstly, multimarket contact has an inverted U-shape influence on rent seeking, with mutual forbearance effects occurring in the non-market field when the indicator of multimarket exceeds a critical value; secondly, there is a negative correlation between rent seeking and firm performance, and rent seeking does not necessarily lead to improved firm performance; thirdly, rent seeking will play a mediating role between multimarket contact and performance. When faced with the pressure of multimarket competition, firms seek to enhance their performance by engaging in rent seeking behaviors in non-market domains; fourthly, institutional environment will weaken the influence of multimarket contact on rent seeking. The above conclusions indicate that when faced with multiple market contacts, firms often adopt a strategy of “Do one thing under cover of another”. While mutual forbearance may be observed among firms in the market domain, they covertly seek to launch attacks through non-market competitive actions, ultimately resulting in competition spillover into non-market domains. The research findings effectively expand the current theoretical research on multi-market competition and provide valuable insights for multimarket firms to better grasp inter-firm competitive interactions.

Based on the realistic background of the increasing popularity of robot adoption and the vigorous implementation of innovation and development strategy in China, this paper discusses the impact of robot adoption on the technological innovation of manufacturing enterprises. It is found that robot adoption can significantly promote enterprise technological innovation, and its mechanism is that robot adoption improves enterprise technological innovation by governance effect and human effect. For non-high-tech enterprises, and enterprises located in areas with imperfect institutional environment, robot adoption plays a more significant role in improving enterprise technological innovation. In-depth research on the mechanism of human effect shows that robot adoption, labor demand and labor structure help enterprises to help enterprises improve technological innovation by playing a “dual complementary role”. Finally, based on the heterogeneity test of enterprise technological innovation, it is found that robot adoption plays a more significant role in promoting exploratory technological innovation than explorative technological innovation. This study provides a decision-making reference for China to promote the robot industry to better serve the high-quality economic development.

Based on the registration reform of China's stock market, this paper examines the changes in different market segments before and after the introduction of the reforms. We examine the market reactions of each board after the introduction of the registration system, from the perspective of financial distress risk and ownership structure of the control shareholders. The research finds that after the implementation of registration-based IPOs, the average time of listing in the STAR market is the shortest; meanwhile, the reforms have improved the listing efficiency of both the ChiNext board and the main board. We also find that: 1) In the event of the registration system reform of the ChiNext board, the listed companies of the STAR market showed negative short-term responses and positive long-term responses. 2) In both registration system reform events, those listed companies on ChiNext board have produced a positive response in both short-term and long-term. 3) On the whole, the market of companies with less severe financial risks performed better in the two registration system reform events. 4) Compared with non-SOEs, the response of SOEs to the two reform events is more negative. 5) Increase on the informativeness of stock prices has been detected across markets after the reform. 6) After the reform, the ChiNext Board has seen a rise in stock liquidity. Our research shows that there are significant differences in the impact of the introduction of the registration systems in different sectors in China's stock market, which could provide some reference for the policy application and path selection of the comprehensive implementation of the registration reform in the stock market in the future.

This paper selects the panel data of cities above prefecture level in China from 2008 to 2020, and measures the innovation efficiency of cities using the FP (Fare-Primont) index method based on data envelopment analysis. Using the panel double fixed effects model and instrumental variable method, this paper empirically tests the impact effect of urban innovation efficiency on real estate investment structure; By constructing a nonlinear dynamic system model for residential and non-residential real estate investment, the stability of urban real estate investment structures with different innovation efficiencies was analyzed. The test results indicate that the innovation efficiency of both the current and previous periods in the city will significantly promote the proportion of non-residential real estate investment, and exhibit a non-linear effect pattern. In different innovation efficiency cities, the real estate investment structure has stable values, and the stable value of high innovation efficiency cities is higher than that of medium innovation efficiency cities and lower innovation efficiency cities. Therefore, local governments should combine the positioning of urban innovation and development, reasonably apply regulatory policies, and promote the healthy development of the real estate market; At the same time, it is also necessary to adjust the real estate investment structure according to changes in urban innovation efficiency levels and differences in innovation efficiency between cities, and implement policies tailored to the times and cities.

Based on the perspective of ecological efficiency, this paper divides the innovation process of industrial innovation ecosystem into three stages: Assimilation, growth and utilization, and constructs an evaluation index system of industrial innovation ecosystem efficiency. The Super-SBM model and Malmquist index method are used to measure the innovation ecosystem efficiency of 15 high-tech industries in China, and relevant efficiency improvement paths are proposed. The research results show that the efficiency of China's high-tech industry innovation ecosystem is at a medium level, with an overall trend of continuous growth. The differences and polarization among industries are significant, and further coordinated development is needed. There are significant differences in the efficiency of the three stages, with the highest utilization efficiency, the second highest growth efficiency and the lowest assimilation efficiency. In the evolutionary trend, the assimilation efficiency has the fastest growth rate, utilization efficiency is the second, and growth efficiency is the slowest (negative growth). Combined with the difference of industrial efficiency in different stages, it is found that the “double three-high” efficiency industry has not yet formed, which cannot play the leading and promoting role of “innovation ecological effect”. The efficiency of China's high-tech industry innovation ecosystem can be characterized by four types: high efficiency - fast growth, low efficiency - fast growth, low efficiency - slow growth, and high efficiency - slow growth. Different types of industries can improve the efficiency of innovation ecosystem through process optimization, mechanism improvement, and bilateral breakthrough.

This paper investigates an optimal reinsurance contract formulation problem under the competition between n cooperative insurers and one reinsurer. The reinsurance contract consists of claim risk sharing strategy and reinsurance price. n insurers fully believe in their respective surplus process, while the reinsurer believes that each insurer's surplus process is uncertain. Each insurer's goal is to find an optimal claim risk sharing strategy so as to maximize the expected terminal wealth while minimizing the variance of the terminal wealth. The reinsurer's goal is to find an optimal reinsurance price so as to maximize the expected terminal wealth while minimizing the variance of the terminal wealth under the worst-case scenario of surplus process. Under the Stackelberg game framework, by using the stochastic control and stochastic dynamic programming techniques, we obtain the explicit solutions for the optimal claim risk sharing strategy and reinsurance price, furthermore derive the explicit solution for the optimal reinsurance contract. Finally, the influence of model parameter on the theoretical results is illustrated by numerical experiment, and some new reinsurance inspirations are obtained.

The rapid development of internet has facilitated the wide application of blockchain technology in supply chains. Blockchain adoption not only incurs high cost, but also has important impacts operational decisions and performance of supply chains. This paper develops a dynamic game model between a manufacturer and a retailer under proportional cost-sharing and Bargaining cost-sharing mechanisms based on the context of supply chain members cooperating in investing blockchain technology, and analyzes how the cost sharing of blockchain technology adoption affects equilibrium decisions, profits, consumer surplus, and social welfare. Results show that, compared with the no-sharing scenario, cost-sharing can improve manufacturer and supply chain system profits and increase consumer surplus and social welfare. Although the Bargaining cost-sharing mechanism can achieve optimal system efficiency, the profit of the retailer under this sharing mechanism is lower than the reservation profit under the no-sharing scenario. In addition, a revenue sharing contract is designed to achieve coordination of the supply chain system based on blockchain adoption. These findings can be used in enterprises' blockchain technology adoption decisions and improving the efficiency of blockchain technology cooperation and supply chain operations.

Agri-food supply chain with wholesale market as the core is the main circulation channel of agricultural products, which plays an important role in ensuring urban supply and maintaining agri-food supply. Some wholesale market closure and supply disruptions have seriously affected the normal operation of wholesalers. How to design the most profitable supply chain against supply chain disruption risks is getting more attention. However, there are few researches based on the agri-food wholesale market, and the research about the general supply chain design is difficult to reflect the purchasing characteristics of wholesalers and disruption risks. This paper speculates there are some strategies that can be used to mitigate the disruption risks, namely multiple-sourcing, backup supplier, supplier reinforcement, and backup demand, which are suitable for characteristics of wholesalers and designs a resilient wholesaler supply chain network model which considers demand and supply disruption, product sorting, discount pricing and perishability. The model designs the most profitable network to determine the selection of suppliers, DCs and the order allocation. In order to solve the problem, the piecewise model is transformed into a mixed-integer programming model through two steps. And then, typical disruption scenarios are obtained by Monte Carlo sampling and k-medoids clustering. Finally, we make a sensitivity analysis on the design of agri-food supply chain under different pricing schemes, different demand changes, different resilient strategies, different facility disruption and different disruption intensity, which can provide managerial insights for similar business pattern of wholesalers.

The development of rural industry enabled by blockchain technology is of great significance to the promotion of rural revitalization. Based on the consumer preference for “blockchain-tracked” agricultural products, this study established a game-theoretical model of a typical contract-farming supply chain composed of a farmer, a firm, and consumers, and investigates the optimal strategy of blockchain traceability technology introduction. Through model analysis, this study compares the optimal decisions of blockchain node investment, planting area, and price under different blockchain introduction modes, as well as their impacts on the firm's profit, consumer surplus, and social welfare. The results show that, first, when the cost effect of blockchain investment is relatively small, only the firm (or downstream of the supply chain) introduces blockchain. When the cost effect of blockchain investment is relatively large, both farmer and firm (or the whole supply chain) will introduce blockchain. Second, the firm is more willing to introduce blockchain than the farmer, and the farmer has the incentive to “free-ride” and not introduce blockchain under certain conditions. Last, in equilibrium, the introduction strategy of blockchain is Pareto effective. However, when only the firm (or the downstream of the supply chain) introduces blockchain as an equilibrium strategy, the total consumer surplus and social welfare will suffer a certain loss. At this point, the firm or government can consider subsidizing farmers to avoid such losses.

A system with two rival electric vehicle manufacturers (EVMs) is considered, where one EVM has battery technology (BT) and the other hasn't BT. Each EVM recycles its own retired batteries. The game-theoretic models are constructed and solved under competition model, patent co-opetition model and wholesale co-opetition model, respectively. And then, the EVMs' strategy choices between competition and co-opetition, and the decisions for retired battery recycling are explored. It is shown that the choice of the optimal strategy (Competition, Patent co-opetition or Wholesale co-opetition) depends on the bargaining power of EVMs, the degree of substitution between electric vehicles produced by the two EVMs, the battery cost difference between the two EVMs and the recycling value of retired batteries. It is also shown that the Pareto improvement in co-opetition strategies (Patent co-opetition or Wholesale co-opetition) leads to increased profits and decreased vehicle prices, which is beneficial to both EVMs and consumers; in addition, the Pareto improvement in the co-opetition strategies leads to decreased (or increased) recycling rate of EVM with (without) BT. Furthermore, it is shown that the recycling rate of retired batteries and profit for EVM with BT is increasing in its recycling value regardless of competition or co-opetition, while the impact of the recycling value of retired batteries on the recycling rate and profit for EVM without BT depends on EVMs' competitive/coopetitive relationship and the battery cost difference between the two EVMs.

When users go to the station for bicycle rental or return, if there are no bicycles or parking spaces at the station, it will make the user's rental or return needs unable to be met, resulting in losses to the company. Facing this challenge, this study develops a nonlinear static repositioning optimization model for BSS, which considers two periods, namely operation and shutdown periods. The bike repositioning occurs during shutdown period. In the model, the objective functions include the repositioning costs and unmet service cost, the decision variables are truck activation, travel routes, and vehicle repositioning between stations, and the state variable includes the number of bikes at each station during the operation period. Analyses are conducted on the dynamic evolution process of the state variable, which is caused by repositioning strategy and the interaction of rental and return demands between stations. The internal logic of the repositioning is also analyzed, and a linearization method is employed to linearize the model. Then, an artificial bee colony-greedy algorithm is designed to solve large-scale problems based on the problem characteristics. Finally, numerical examples are used to analyze the problem properties and algorithm's performance. The results show that the unit unmet service cost and repositioning capacity have significant impacts on repositioning optimization. The advantages of the artificial bee colony-greedy algorithm in solving large-scale problems have been verified. This research can provide decision support for the repositioning of BSS.

The high penetration of wind and PV increases the demand for flexible resources in new power systems. As a special power plant, virtual power plant integrates all kinds of resources such as controllable distributed power supply, new energy, energy storage, carbon treatment and load. “Internal collaboration” can realize collaborative regulation of internal resources, and “external unity” can participate in the external electric carbon market for profit. Based on this, this paper innovatively proposes a distributionally robust optimization model for virtual power plants to participate in multi-class electric carbon markets. In order to describe the uncertainty of wind power and PV, the distributed fuzzy set based on Wasserstein distance and the error uncertainty set based on data-driven are constructed. In order to consider both economy and robustness, a two-stage robust optimization model with maximum expected revenue under the worst-case distribution is constructed by considering the internal operating costs and the benefits of external participation in multi-category markets, and the solution method of the model is proposed. In order to ensure the dynamic balance of the alliance, a multi-layer benefit sharing method is proposed. Finally, the example analysis shows that under the operation mode of “internal coordination and external unification”, the potential of various resources in the virtual power plant is effectively stimulated, and the shared benefits are obtained after participating in multiple markets, and the mutual benefit and win-win situation of various parties is realized. The proposed model has the advantages of data-driven, fast solution, flexible and controllable, economical and practical. The multi-layer benefit sharing method can transmit the shared benefit to each subject simply and effectively.