Modeling human cognition and polarization using reinforcement Learning

Abstract Some people polarize due to heated arguments but in reality and research studies says that they even polarize with similar information also. People think irrationally and confirms their pre-existing beliefs and strive to look into that information only. With these they avoid such data that contradicts them. Polarization occurs through the bias contribute to the reinforcement of existing beliefs. The people manipulate the new evidence based on the previous beliefs and bias confirmation suggestions. In order to form beliefs, people depend on least samples rather than gathering huge data and processing is also costly. A hypothesis is generated based on the least samples collected and then belief polarization model is used to test our hypothesis. A new belief formation model is developed based on reinforcement learning. In accordance to this a new evaluation metrics is used for polarization and simulations are learned how least size affects polarization. In this work basic assumptions are done with the generated hypothesis results, such that with these least samples polarization may increase. Practical suggestions are done for obtaining polarization based on our findings.