Proceedings of the Fifth Workshop on Experimental Economics and Machine Learning at the National Research University Higher School of Economics co-located with the Seventh International Conference on Applied Research in Economics (iCare7)
Network embedding has become a very promising technique in analysis of complex networks. It is a method to project nodes of a network into a low-dimensional vector space while retaining the structure of the network based on vector similarity. There are many methods of network embedding developed for traditional single layer networks. On the other hand, multilayer networks can provide more information about relationships between nodes. In this paper, we present our random walk based multilayer network embedding and compare it with single layer and multilayer network embeddings. For this purpose, we used several classic datasets usually used in network embedding experiments and also collected our own dataset of papers and authors indexed in Scopus.
In this work, we study deep reinforcement algorithms for partially observable Markov decision processes (POMDP) combined with Deep Q-Networks. To our knowledge, we are the first to apply standard Markov decision process architectures to POMDP scenarios. We propose an extension of DQN with Dueling Networks and several other model-free policies to training agent using deep reinforcement learning in VizDoom environment, which is replication of Doom first-person shooter. We develop several agents for the following scenarios in VizDoom first-person shooter (FPS): Basic, Defend The Center, Health Gathering. We compare our agent with Recurrent DQN with Prioritized Experience Replay and Snapshot Ensembling agent and get approximately triple increase in per episode reward. It is important to say that POMDP scenario close the gap between human and computer player scenarios thus providing more meaningful justification for Deep RL agent performance.
Nowadays there is a large amount of demographic data which should be analyzed and interpreted. From accumulated demographic data, more useful information can be extracted by applying modern methods of data mining. Two kinds of experiments are considered in this work: 1) generation of additional secondary features from events and evaluation of its influence on accuracy; 2) exploration of features influence on classification result using SHAP (SHapley Additive exPlanations). An algorithm for creating secondary features is proposed and applied to the dataset. The classifications were made by two methods, SVM and neural networks, and the results were evaluated. The impact of events and features on the classification results was evaluated using SHAP; it was demonstrated how to tune model for improving accuracy based on the obtained values. Applying convolutional neural network for sequences of events allowed improve classification accuracy and surpass the previous best result on the studied demographic dataset.
Russian Federation and European Union are fighting against fake news together with other countries in various topics. The disinformation affected British referendum of existing EU, the US election and Catalonia’s referendum are broadly studied. A need for automated factchecking increases, European Commission’s Action Plan 8 is an evidence. In this work, we develop a model for detecting disinformation in Russian language in online media. We use reliable and unreliable sources to compare named entities and verbs extracted using DeepPavlov library. Our method shows four time greater recall compared to chosen baseline.