The UBS Center Scholarship made such a difference allowing me to fully focus on my research and investigate questions in behavioral economics and decision-making.
I'm deeply grateful for the opportunity and for being part of such a supportive community.
@ubscenter.uzh.ch

Thank you so much to the UBS Center for Economics in Society for this wonderful post and for the incredible support throughout my PhD journey!

(4/4): Their integrity and the way they truly care about their students has meant the world to me and my colleagues. Thank you again for everything!

(3/4): I could bring them any idea, no matter how far from their own expertise, and always get thoughtful, insightful feedback.
Their curiosity is infectious, and they somehow manage to be both genuinely kind and constructively critical – not an easy balance to strike!

(2/3): I have to give huge thanks to my advisors @robertoweber.bsky.social, @sandroecon.bsky.social, and Lorenzo Casaburi.
They've been amazing mentors who made all the difference. They never let disciplinary boundaries limit our conversations.

(1/3) Thank you so much! I'm absolutely thrilled and still processing that this journey is complete. These years in Zurich have been incredible, and I feel so grateful for the experience.

🚨 Job Market Paper 🚨

If 6 million individuals *voluntarily* put themself in a life-and-death situation, what drove them to make that choice?

My job market paper examines how perceived fairness affected the decision to voluntarily enlist in the US during WW2 #EconJMP #EconJMC #EconTwitter

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Find the paper here: sites.google.com/view/maitede...
Time to go learn something new! (But don't get discouraged if it takes longer than expected 😉)

(6/7) Implications? People struggle to extract accurate lessons from learning experiences, leading to underinvestment in skills. Better understanding these biases could help improve how we support learning and skill development

(5/7) Limitations: My experimental design strips away features of real-world learning - social interactions, motivation, complex skills - to get precise control over learning parameters. This helps identify core mechanisms but leaves open questions about richer learning settings!

(4/7) Context: Real-effort task requiring learning new info. Design lets me compare behavior against Bayesian benchmarks. Why experiment? Real-world learning experiences are confounded by pre-existing knowledge & self-selection into tasks

(3/7) Findings: #1: People mispredict learning trajectories - overestimate short-term but underestimate long-term progress. #2: Errors persist and often worsen with experience. #3: Result? They choose easier tasks with lower rewards despite ability to succeed at harder ones

(2/7) What I do: Novel experiment controlling learning environments to study how past learning shapes future investment decisions. I vary how much people need to learn & how fast they progress

How do people learn about their ability to learn? In my JMP, I show they struggle. Past learning experiences could help predict future learning and guide better skill investments. But do people use this information effectively?