Choice Prediction

Choice prediction aims to model and forecast human decision-making across various contexts, from simple consumer choices to complex strategic interactions. Current research focuses on improving prediction accuracy using advanced machine learning techniques, such as transformer neural networks and Gaussian processes, often incorporating response times to enhance model fidelity. These advancements are impacting fields like marketing, economics, and human-computer interaction by enabling more accurate modeling of human behavior and more effective design of interactive systems. The development of large language models is also significantly contributing to data generation and improved prediction in complex scenarios involving language and strategic communication.