Artificial intelligence (AI) automation challenges the assumption that careers are shaped mainly by individual choices, and directs attention to the interplay of individual, organisational and environmental conditions emphasised by the sustainable-career framework. This study formalises that framework as a complex adaptive system. Using the structure and loadings of a validated Sustainable Career Scale, we build an agent-based model in which employees, organisations and the environment interact through explicit feedback loops, and compare four scenarios that cross two trajectories of AI pressure with two institutional support regimes. Ablation and counterfactual experiments separate results that are emergent from those that follow from the model’s assumptions. Within the simulated system, career losses under AI pressure follow a threshold-like pattern whose form is robust but whose location depends on how career discontinuity is defined; it should not be read as an observable policy threshold. The rate of increase in pressure matters more than its level, a consequence of the assumption that adaptation accumulates over time. High institutional support prevents discontinuities through a redundant package: training transfer, individual investment, HR targets and management support are each sufficient on their own, and none is necessary. Payoff-biased peer learning does not generate the high-performance convergence seen under strong support, but under weak support it protects the most vulnerable agents. The study contributes a dynamic extension of a measurement model, a transparent separation of assumptions from emergent results, and a set of hypotheses for longitudinal research on how institutional configurations and social learning shape career resilience under technological change.
Environmental Non-Governmental Organizations (ENGO) play a crucial role in advancing environmental protection by using threats and boycotts to force enterprises to produce sustainably. ENGO interact with enterprises based on different motivations and benefit from psychological gains, media publicity and the reduction of negative environmental externalities, but ENGO don't always victory and achieve their objectives. We use a game-theoretic model to examine how different types of ENGO motivation affect strategic interactions between an ENGO and a firm, and use a contest process to elucidate the boycott of ENGO to the firm. We find that, the result of the interaction is solely contingent upon their relative status, and the contest is most vigorous when they have the same status. The strategy of ENGO depends on both the primary motivation behind the engagement and the specifics of the target firm,and we categorize engagement actions as Radical engagement,Self-improvement engagement and Self-transcendence engagement base on the different engagement strategy. Our findings contribute to strategic management research by developing new insights about the interaction between ENGO and firm and the categorization of engagement actions,and indicate that the government need to regulate ENGO' s action sometimes.
In global financial governance, interdependence is mainly manifested in the increasing interconnection between international financial organizations, the broadening of the agenda of financial globalization, and the rise of financial development issues. Based on the convolutional neural network, this paper designs and implements a financial risk control model that can be applied to the actual production environment. The entire model has the specificity of dual input and multi-modality and can use international economic law and international relations as an effective input source to perform sufficient feature extraction so that the entire financial risk control model is based on cross-perspective prediction results. Experiments show that the data processing method in this paper can significantly improve the evaluation indicators of multiple models by at least 2
In this paper, I construct a macroeconomic agent-based model to explore its potential as a tool to support macroeconomic policy decisions. Compared with existing models, I include several channels of the transmission mechanism in the model such as interest rate channel and expectation channel. I then conduct a series of experiments to understand the responses of key macroeconomic variables to major macroeconomic shocks. I find that the effects of the demand shock and the monetary policy shock are broadly consistent with those of standard models. On the other hand, supply shock generates additional effects that are not typical for other classes of models.
E-commerce growth has increased rapidly in recent years, and several people utilize this popular channel to purchase services and products through Internet resources. Shopping sites have become very important for customers to buy the best products, and sales have increased for this resource. Furthermore, people purchasing through internet resources face several problems and a lot of confusion due to the massive number of products. They find it very difficult to choose their favourite product. In the present market, various popular traditional algorithms named Collaborative Filtering (CF), Planned Behavioral Theory (PBT), Markov Hidden Model (MHM), Traditional Machine Learning (TMC) and Analysis of Component (AoC) were used in the E-commerce sites for the users to purchase and choose the products in a customized manner with high service and more loyalty. In the traditional methods, Customers face several difficulties in determining the statistical probability of the product due to bulk data information during shopping. In this research, the heuristic computational method (FHCM) has been integrated with e-commerce sites, which helps optimize product search during the purchase and customer authentication process in an effective manner. This proposed method has been experimentally analyzed at lab scale testbed software and found to be more helpful in solving the problems in data sparse to identify the best product on the site for the customers.
