Simulated trial and error experiments on productivity

Abstract

Trial and error experiments in socioeconomics were proved to be beneficial by Nobel prize laureates. However, replication is challenging and costly in term of time and money. The approach required interventions on human society, and moral issues have to be carefully considered in research designs. This work tried to make the approach more feasible by developing virtual economic environment to allow simulated trial and error experiments to take place. This research demonstrated the framework using 19 macroeconomic indicators in 6 interested categories to study the effect on productivity if each indicator value grew by 5 percent for each of 65 countries. Seven predictive models including some machine learning (ML) models were compared. Neural network dominated in accurateness and was selected as the core of the simulator. Experimented results are in full of surprises, and the framework acted as expected to be a data-driven guide toward country-specific policy making.

Multi-channel microseismic signals classification with convolutional neural networks

“…Earlier studies have investigated several strategies, such as conventional signal processing methods and machine learning algorithms [12], [13], to identify and classify microseismic signals [14]. However, these techniques have limitations regarding their efficiency and accuracy.…”