“If many forecasts say the probability is 80% that A will win, 20% that B will win, why do they say the forecasts were wrong if B wins?”
Wrong implies bivalence, binary thinking, duality: it implies right. A probability cannot be binary, it’s infinite. My brain has a hard time understanding why it’s reasonable… Kind of Orwellian.
So to my point. Forecasts were only wrong if they say A will win, but B wins. Is this not correct? Stating 80% in hindsight is equal to stating 0%, and even before that it’s 0% or 100% or it’s void, nothing, of no substance...
You have a certain stable process that generates forecasts. You generate a forecast: 80% for A, 20% for B. B happens. You generate another forecast: 80% for C, 20% for D. D happens. You generate another forecast...
If events that you forecast at 20% keep happening and events you forecast at 80% keep not happening, how many forecasts do you need to recognize that your forecast-generating process is wrong?
Yes, you are right on the point. I wanted to ask:
“If many forecasts say the probability is 80% that A will win, 20% that B will win, why do they say the forecasts were wrong if B wins?”
Wrong implies bivalence, binary thinking, duality: it implies right. A probability cannot be binary, it’s infinite. My brain has a hard time understanding why it’s reasonable… Kind of Orwellian.
So to my point. Forecasts were only wrong if they say A will win, but B wins. Is this not correct? Stating 80% in hindsight is equal to stating 0%, and even before that it’s 0% or 100% or it’s void, nothing, of no substance...
Well, think about it this way.
You have a certain stable process that generates forecasts. You generate a forecast: 80% for A, 20% for B. B happens. You generate another forecast: 80% for C, 20% for D. D happens. You generate another forecast...
If events that you forecast at 20% keep happening and events you forecast at 80% keep not happening, how many forecasts do you need to recognize that your forecast-generating process is wrong?