Physicians’ Online Popularity and Price Premiums for Online Health Consultations: A Combined Signaling Theory and Online Feedback Mechanisms Explanation

Online health consultation communities (OHCCs) provide a digital channel for physicians to signal their professional competence (i.e., credibility) and compassionate care (i.e., benevolence), and for patients to spread word-of-mouth reviews. The valence, volume, and variance of patient reviews may shape the effectiveness of signals transmitted by physicians in OHCCs. We investigate the interactions between the signaling mechanism and the online feedback mechanism through which OHCCs help physicians build online popularity and achieve price premiums for online health consultations. We are using web scraping to collect weekly data for 12 months from a large OHCC in China. Applying mixed effects models on the data collected to date, we find online popularity and price premiums to be two benefits that physicians can derive from OHCCs. Importantly, in the presence of benevolence actions, an absence of consistently favorable online feedback slows down physicians’ online popularity and reduces price premiums for online health consultations

Temporal Difference Learning for High-Dimensional PIDEs with Jumps

In this paper, we propose a deep learning framework for solving high-dimensional partial integro-differential equations (PIDEs) based on the temporal difference learning. We introduce a set of Levy processes and construct a corresponding reinforcement learning model. To simulate the entire process, we use deep neural networks to represent the solutions and non-local terms of the equations. Subsequently, we train the networks using the temporal difference error, termination condition, and properties of the non-local terms as the loss function. The relative error of the method reaches O(10^{-3}) in 100-dimensional experiments and O(10^{-4}) in one-dimensional pure jump problems. Additionally, our method demonstrates the advantages of low computational cost and robustness, making it well-suited for addressing problems with different forms and intensities of jumps