It is very hard to come up with legit and solid advantages and drawbacks of the various approaches wich are trying to counteract parameter uncertainty in portfolio optimization procedures. In my opinion all bayesian approaches, i.e. the use of diffuse priors, asset pricing based priors and economic objective priors are somewhat similar and may or may not lead to favorable results. How would you assess the advantages and disadvantages of each approach in practice?
Use Mean Squared Forecast Error (or any other forecast evaluation metric).
Your question appears to complicate the problem: If your goal is to forecast a given parameter you can test the rolling forecast against the actual observed values. This will also give you a metric of uncertainty as you can then create confidence intervals around your forecasts based on past error (as opposed of using distributional estimation and it's assumptions).
If you want to test models you can check the forecast error of the model vs optimal with perfect information on a rolling standpoint.