I echo the props; what a great way to live up to your name!
How do you intend to translate this analysis into practical change (if you think any change is warranted)?
In your opinion, how do the included forecasts affect grant-making decisions? My thought here is that when making a forecast about a grant, the person may not be purely playing an accuracy game—they may also be considering what the strategic/communicative impact of such forecasts are likely to be (this could be one source of the 90+% miscalibration—if I wanted to sell an idea or seem confident, I could inflate my forecast so that a particular outcome is 90+% likely).
We’re currently providing calibration and accuracy stats to our grant investigators through our Salesforce app in the hopes that they’ll find that feedback useful and actionable.
I’m not sure and I’d have to defer to decision-makers at OP. My model of them is that predictions are just one piece of evidence they look at.
I echo the props; what a great way to live up to your name!
How do you intend to translate this analysis into practical change (if you think any change is warranted)?
In your opinion, how do the included forecasts affect grant-making decisions?
My thought here is that when making a forecast about a grant, the person may not be purely playing an accuracy game—they may also be considering what the strategic/communicative impact of such forecasts are likely to be (this could be one source of the 90+% miscalibration—if I wanted to sell an idea or seem confident, I could inflate my forecast so that a particular outcome is 90+% likely).
Thanks!
We’re currently providing calibration and accuracy stats to our grant investigators through our Salesforce app in the hopes that they’ll find that feedback useful and actionable.
I’m not sure and I’d have to defer to decision-makers at OP. My model of them is that predictions are just one piece of evidence they look at.