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Overcoming Bias Commenter's avatar

Wow, I missed this conversation by a year. Good comments, and I think those who defended "no one can know" got part of what I was saying.

The other part was a simple reminder that "peak oil" cannot be directly measured. All we have as measurable data are price and current production data. The next step is always an extrapolation based upon an assumption. One starts, for instance, with the assumption that Hubbert's method will hold for world production, and that a calculation done today will yield an accurate "high production" and "high production date."

How do you put error bars on that assumption, that Hubbert's method, a heuristic, will hold?

(And I might also comment that in the year since this post, the "Hubbert's date" for Peak oil has moved and argued again and again.)

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Overcoming Bias Commenter's avatar

EliezerI agree, I was only using the example as something we can all agree we can't know. If you would like an unarguable example where the distribution as well as the expected value is unknowable, how-about the number of intelligent life forms in a galaxy outside our light cone? My point was really that there are a range from things that we can know well to things that we can't know at all. But when we get a distribution from someone how do we know how well or how much it is underpinned by real knowledge?

If we look at, say, the global warming predictions, we get a range in possible rises in average temperatures - I have heard from 3 to 6 deg C. But how much faith should we put in this distribution? Clearly it is of worse quality than if the same distribution was provided for the temperatures in New York tomorrow. How could we "measure" or otherwise agree on this quality factor? Could the measure include whether the model that produced the distribution can be tuned by real feedback or not.

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