Bayesian Model Selection and Prior Calibration for Structural Models in Economic Experiments
"Bayesian estimates from experimental data can be influenced by highly diffuse or "uninformative" priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments of decision‑making under risk, and discuss some implications of the findings."