Wonderful method by @sweiwang.bsky.social @cfcamerer.bsky.social et al on optimal design for pref parameter elicitation here: sites.pitt.edu/~swwang/pape...

New preprint with Lawrence Jin. We find that any effect of describing a prior to experimental participants is quickly crowded out by experience with that prior

Let’s ban Israeli students from attending Harvard in the name of squashing antisemitism, that’ll protect them

For those who are interested in digging deeper into this, Ryan has just posted a very thoughtful and thorough reply here: bit.ly/4bQk0P0

The student sample was conducted online too, though with more oversight via zoom. I think this pushes the debate from online vs. lab to online sample vs. student sample

EF and EFSS 2025, Call open - Society for Experimental Finance Maastricht University and the Society for Experimental Finance (SEF) invite you to the Experimental Finance 2025 (June 12-14) and the Summer School (June 10-11) in Maastricht, the Netherlands. The con...

Please join us this summer in Maastricht for the annual Experimental Finance Conference (June 12-14), and Summer School (June 10-11).

Some real interesting papers in this session “Neurofinance, Cognition” today, pushing on the idea that seeming departures from rational behavior wrt risk are actually just evidence of cognitive constraints, like limits on memory or attention

The models won’t be “complete” (every model is wrong etc), but they will be useful.

But this is a bet.

(Which is what makes the current period in BE so exciting)

Fin

Yes exactly, enter all the interesting work on description - experience gaps

Great thread. It’s possible that noise in cognitive representation of probabilities (or proportions) are driving some of these results. So to the extent that there’s more noise when probabilities aren’t explicitly presented in real world, perhaps you’d get broader scope of PT-like behavior

Your first point seems consistent with his conclusions…that these paradigms may have been measuring some information processing constraints rather than risk prefs all along?

2) all noisy coding models feature a prior which is first order for shaping choice, but irrelevant when perception isn’t noisy. To me, this is the key separating prediction and it’s testable at least experimentally

Agree that model fits are v useful but these new models do make some bold and testable predictions. (1) Enke & Graeber ‘23 show that empirical measures of cognitive uncertianty can explain choice bias and

This is great Dan—can you please add me? Thanks!