. More precise performance evaluation reduces current agency costs but promotes learning, which can increase future agency …

Learning. Specifically, our study is framed in the context where a reference discrete optimization problem is given and there …

literature, reinforcement learning (RL) has shown potential to quickly evaluate the reward- and value function of the Bellman …

-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for …

extend our model to investigate the benefits of Bayesian learning. Furthermore, we compare the principal's returns in general … and learning models and find that the learning contract can bring more profit to the principal. …

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm …

Real-time Trajectory Optimization (RTOP) is a machine learning-based flight path optimization framework that … algorithms: (1) a supervised learning algorithm to predict winds, (2) an unsupervised learning algorithm to forecast areas of … proposes a methodology that uses an unsupervised learning-based clustering algorithm with the concept of Hausdorff distance to …

Strategic learning self-efficacy reflects the confidence of managers in their ability to learn from the outcomes of … strategic learning self-efficacy and a firm's industry-adjusted sales growth rate and specifies strategic decision-making style … hypothesized relationships. Results indicate that strategic learning self-efficacy is not significantly related to firm growth as a …