The lab focuses on understanding complex human behavior in a variety of contexts. This work frequently employs computational methods, both those typically labeled as machine learning and more conventional statistical inference. These methods are used to develop theories of behavior and to analyze data sets. A recent sampling of project includes:
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Transmission in Social Networks
This project investigates the social transmission of information. We are employing a two-pronged approach, employing a both behavioral experiments and agent-based simulations.
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Intertemporal Choice
We are interested in how people make decisions involving trade-offs between value and time and are particular interested in a) understanding the mechanisms underlying such choices and b) how to most accurately predict how individual will resolve such tradeoffs.
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Learning
We are interested in how people’s choices are shaped by past choices and their outcomes. Often we study learning and decision making together.
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Please see our list of publications for further information.
Mail Dr. Luhmann if you have more questions.