Not a philosopher, but I have overlapping interests.
I’m not sure what you mean here. What’s RDM? Robust decision making? So you’d want to formalise decision making in terms of the Bayesian or frequentist interpretation of probability?
Again, I’m not sure what “maximising ambition” means? Could you expand on this?
How would you approach this? Surveys? Simulations? From a probability perspective I’m not sure that there’s anything to say here. You choose a prior based on symmetry/maximum-entropy/invariance arguments, then if observations give you more information you update, otherwise you don’t.
I suspect a better way to approach topic selection is to find a paper you get excited about, and ask “how can I improve on this research by 10%?” This stops you from straying wildly off of the path of “respectable and achievable academic research”.
2. I think formalizing this properly would be part of the task, but if we take the Impact, Neglectedness, Tractability framework, I’m roughly thinking of a decision-making framework that boosts the weight given to impact and lowers the weight given to tractability.
3. I was roughly thinking of an analysis of the approach used by exceptional participants in forecasting tournaments like Tetlock’s. Most of them seem to be doing something Bayesian in flavor, if not strictly Bayesian updating, and with impressive results. I suspect that could have interesting implications for how we understand (the relation of subjectivity to) a Bayesian interpretation of probability.
Not a philosopher, but I have overlapping interests.
I’m not sure what you mean here. What’s RDM? Robust decision making? So you’d want to formalise decision making in terms of the Bayesian or frequentist interpretation of probability?
Again, I’m not sure what “maximising ambition” means? Could you expand on this?
How would you approach this? Surveys? Simulations? From a probability perspective I’m not sure that there’s anything to say here. You choose a prior based on symmetry/maximum-entropy/invariance arguments, then if observations give you more information you update, otherwise you don’t.
I suspect a better way to approach topic selection is to find a paper you get excited about, and ask “how can I improve on this research by 10%?” This stops you from straying wildly off of the path of “respectable and achievable academic research”.
Thanks for your suggestions! Some answers:
1. Robust decision making. And yes, pretty much, I was thinking of the interpretations covered here: https://plato.stanford.edu/entries/probability-interpret.
2. I think formalizing this properly would be part of the task, but if we take the Impact, Neglectedness, Tractability framework, I’m roughly thinking of a decision-making framework that boosts the weight given to impact and lowers the weight given to tractability.
3. I was roughly thinking of an analysis of the approach used by exceptional participants in forecasting tournaments like Tetlock’s. Most of them seem to be doing something Bayesian in flavor, if not strictly Bayesian updating, and with impressive results. I suspect that could have interesting implications for how we understand (the relation of subjectivity to) a Bayesian interpretation of probability.