Aggregation of information and beliefs on prediction markets with non-bayesian traders

Prediction markets are specific financial markets designed to produce forecasts of future events, such as political election outcomes or economic policy decisions. Empirical studies have exhibited over the years the significant accuracy of these anticipations, which tends to give credit to the efficicent market hypothesis advocated by the literature. However the latter relies theoretically on rational behaviors, in sharp contrast with traders' actions observed on most prediction markets. Indeed, an important fraction of participants are subject to several judgement bias. Based on Ottaviani and Sorensen's (2010) approach, we develop a framework that allows to introduce these biased traders and to study the consequences on the equilibrium properties

Dynamics of Wetting Fronts in Porous Media

We propose a new phenomenological approach for describing the dynamics of wetting front propagation in porous media. Unlike traditional models, the proposed approach is based on dynamic nature of the relation between capillary pressure and medium saturation. We choose a modified phase-field model of solidification as a particular case of such dynamic relation. We show that in the traveling wave regime the results obtained from our approach reproduce those derived from the standard model of flow in porous media. In more general case, the proposed approach reveals the dependence of front dynamics upon the flow regime.Comment: 4 pages, 2 figures, revte

Deep Learning for Inertial Sensor Alignment

Accurate alignment of a fixed mobile device equipped with inertial sensors inside a moving vehicle is important for navigation, activity recognition, and other applications. Accurate estimation of the device mounting angle is required to rotate the inertial measurement from the sensor frame to the moving platform frame to standardize measurements and improve the performance of the target task. In this work, a data-driven approach using deep neural networks (DNNs) is proposed to learn the yaw mounting angle of a smartphone equipped with an inertial measurement unit (IMU) and strapped to a car. The proposed model uses only the accelerometer and gyroscope readings from an IMU as input and, in contrast to existing solutions, does not require global position inputs from global navigation satellite systems (GNSS). To train the model in a supervised manner, IMU data is collected for training and validation with the sensor mounted at a known yaw mounting angle, and a range of ground truth labels is generated by applying a random rotation in a bounded range to the measurements. The trained model is tested on data with real rotations showing similar performance as with synthetic rotations. The trained model is deployed on an Android device and evaluated in real-time to test the accuracy of the estimated yaw mounting angle. The model is shown to find the mounting angle at an accuracy of 8 degrees within 5 seconds, and 4 degrees within 27 seconds. An experiment is conducted to compare the proposed model with an existing off-the-shelf solution.Comment: 9 Pages, Preprint. Accepted IEE

Determining The Efficiency Of Online Learning Models

The study proposes a method for objectively determining the efficiency of online learning based on quantitative data generated by users while studying at online courses. Classifications of online learning models are researched and the efficiency of 4 common models in Ukraine is evaluated