About
I am a PhD student in neuroeconomics in Christian Ruff’s group at the University of Zurich. My broad research interests are in understanding how a resource-constrained brain exploits the structure of the world to compute efficiently according to the goals of the agent, and how this approach yields systematic biases and variability patterns seen in human behavior.
For example, I study how noisy inference of bounded quantities like probabilities, with either Bayes-optimal decoding or efficient encoding, can independently produce the classic probability distortion pattern from prospect theory (see preprint). Related work uses the same resource-rational lens to explain experience based probability distortions, to show how task goals modulate computations in multi-attribute decisions, and to model valuation as a higher-order sequential (resource-limited) inference process. Across these projects I pair formal theory with behavioral experiments to test model predictions.
Interests
While my work so far has focused on simple, single-trial decisions, I am getting interested in sequential decision making—again in how resource-constrained computations, informed by both environmental structure and task goals, can explain behavior. This opens a way to understand more complex phenomena such as planning and learning under the same principles. I think this is a useful and exciting direction.
As a side interest, I also enjoy thinking about how these abstract cognitive computations might emerge as properties of statistical physical systems (e.g., the Ising model). While orthogonal to my main research, these loosely formed connections often inspire my thinking about how such computations could be implemented.
If you'd like to chat or know more, please feel free to reach out!