Department of Economics (IVW)Open OpportunitiesThis thesis aims to bridge the gap between human decision-making under uncertainty and artificial intelligence. Building upon recent neuroimaging research from our group on how the human brain processes probability and uncertainty of motivational events, this project will investigate whether meta-reinforcement learning (meta-RL) models can accurately replicate these complex neural computations and match human performance on a specific Pavlovian task. Ultimately, the goal is to understand the similarities and differences in how AI and biological intelligence handle learning and decision-making in uncertain environments. - Computer Perception, Memory and Attention, Neurocognitive Patterns and Neural Networks, Neurosciences, Simulation and Modelling
- Master Thesis
| Do you feel more in the mood to book a ski weekend on a sunny Monday or a rainy Monday? Our decisions can be influenced by many factors—including the environment around us. This study explores how non-social environmental factors, such as current and past weather and temperature, influence economic decision-making, including risk-taking, choice consistency, and rationality. We aim to develop a data analysis pipeline for processing large datasets with multiple features, using machine learning techniques like lasso and ridge regression to identify key predictors of economic behavior. The project involves parameter tuning, assumption checking, and feature selection to ensure robust, interpretable models. If time permits, weather data will be scraped from the web based on geolocation to further enhance the analysis of environmental conditions. By investigating how these contextual factors shape economic decisions, we aim to provide insights into the dynamic forces influencing individual choices, challenging the view of economic preferences as stable dispositions. - Behavioural and Cognitive Sciences, Economics
- Master Thesis, Semester Project
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