6 ms·

This makes sense, thanks. However, it doesn't mean that the 3% estimate of the chance of impact was wrong at the time of initial observation given data availabl

by apatil 3y ago

This makes sense, thanks. However, it doesn't mean that the 3% estimate of the chance of impact was wrong at the time of initial observation given data available, and that still makes it a huge deal at that time. At minimum, it would seem to justify using the best available instruments to characterize the asteroid more precisely as soon as possible. If these large numbers happen so often that asteroids with initial impact probabilities of 3% are known to actually impact much less frequently than that, then the model is poorly calibrated, no? In other words, the reported probabilities aren't really probabilities and that is what has caused the confusion and anxiety in these comments.

abathur 3y ago
I don't know, but I suspect this is more about the limits of the observations (which I imagine are mostly from terrestrial observatories) needed to obtain much certainty about the object's size, course, speed, density, etc.
cfraenkel 3y ago
It's not a model that is poorly calibrated - you seem to be taking a software-centric concept far away from where it's useful. The uncertainty at initial observation is because when you first observe an object, you only have observations covering a tiny bit of the orbit, resulting in very wide error bars. The "model" (Newtonian orbital dynamics) is one of the most precise models we have. Doesn't help when the observations are noisy.
- MauranKilom 3y ago
  Unless one in every 33 asteroids that have 3% impact probability at some point in time actually impacts earth, there is clearly some unwarranted assumption in the error bar/distribution calculation. "The measurement data has noise" does not explain why the noise has a bias towards "the asteroid will hit earth" whereas reality so far has been biased towards "the asteroid will not hit earth". (This assumes that significantly more than 33 asteroids have had >= 3% impact probability predicted at some point. The opposite would not be less concerning.)
  - noduerme 3y ago
    One explanation would be the Anthropic Principle. In 3% of universes you were killed today, you're just not living in one of those.
    - jacquesm 3y ago
      In 97% of the universes dinosaur descendants rule planet earth. But on this one they got unlucky.
    - MauranKilom 3y ago
      This only works if there is nothing between "no impact" and "you die the same day as the impact". But we know that's not the case.
      - noduerme 3y ago
        Why "same day" and not same week or month? If it's not the same instant, then you're hypothesizing some kind of back-propagation (where alternate futures in which you die influence the likelihood of current events)[0]. Under that hypothesis, it would only matter whether some event would cause you to die sooner than you otherwise would. [0] https://arxiv.org/abs/0707.1919 https://arxiv.org/abs/0707.1919 [edit] FWIW, I actually corresponded with one of the authors of this paper back in 2007, and from what I could tell, this wasn't an attempt at parody, although now it might be dismissed as one. Personally I'm not willing to declare my (non)commitment to the theory either way.
  - jacquesm 3y ago
    That's because Earth has gravity, and an asteroid that comes close enough can get deflected onto the planet even if right now it seems to be on a trajectory to miss it entirely. The closer they get and the lower the relative speeds the larger the chance that they will collide and that's not a linear relationship. Beyond a certain boundary impact is certain, then the question is what the time of the impact is and how precise the observations up to that point are in order to figure out where and when exactly it will come down. That won't happen very long before the impact itself happens even if you could say some time in advance roughly in which hemisphere and roughly when. But not precise enough to be very useful.
- deleted 3y ago
  [deleted]
- snewman 3y ago
  If you have very wide error bars, shouldn't your estimate of impact probability be much lower than 3%? Most trajectories within your error bars will not intersect the Earth.
  - jacquesm 3y ago
    The 3% is likely the median of the probability range. You need more observations (and more accurate ones to narrow it further down, but for a first estimate it will do).
aaron695 3y ago
> This makes sense, thanks. It makes no fucking sense. There is 3% chance it'll be revised to 100% chance and 97% chance it'll be revised to 0% chance. Are you Yogi Berra? It'll be reported like possible hurricanes hitting landfall. If this has happed ~33 times then one will hit us. If it's 400m it will kill 200,000 people assuming Vox is reporting correctly - https://www.vox.com/future-perfect/2019/7/26/8931776/near-earth-asteroid-tracking https://www.vox.com/future-perfect/2019/7/26/8931776/near-ea... That will be 9 in 10 it hit's boring ocean and looks cool on satellites and one in 10 kills 2 million people and there will be some cool live streams. Or the 3% in the title is a lie.
- jryb 3y ago
  > If this has happed ~33 times then one will hit us. There would be a ~63.4% chance that at least one would hit us if this happened 33 times. To compute this, take 1-(0.97^33). But I agree with your broader intuition that these predictions must be getting inflated.
apatil 3y ago
In hindsight this comment was too short. Clarifying some points: By "This makes sense", I meant that this kind of thing can happen; as more data are gathered, the Bayesian probability of a candidate value can increase and then suddenly decrease. Here's a Colab notebook demonstrating the general phenomenon: https://colab.research.google.com/drive/1Eb1_humiGPdKb0c3qr_0k1yYDWBVODKm?usp=sharing https://colab.research.google.com/drive/1Eb1_humiGPdKb0c3qr_... "Calibration" in this context means "statistical consistency between distributional forecasts and observations" in the words of https://sites.stat.washington.edu/raftery/Research/PDF/Gneiting2007jrssb.pdf https://sites.stat.washington.edu/raftery/Research/PDF/Gneit... . If the model's early forecasts predict impact with probability >3% for a class of objects that end up impacting with frequency much less than 3%, then the model is not well calibrated with respect to its early forecasts for those objects. Based on the GP, it sounds like these early impact "probabilities" are no one's subjective (Bayesian) probability of impact because people who are closely familiar with this model know it is not well calibrated. The reported probabilities may still be useful to them as indicators or flags. However, those of us who are _not_ closely familiar with the model have found it confusing to see things that are not really probabilities reported as probabilities.

by apatil 3y ago

abathur 3y ago

I don't know, but I suspect this is more about the limits of the observations (which I imagine are mostly from terrestrial observatories) needed to obtain much certainty about the object's size, course, speed, density, etc.

cfraenkel 3y ago

It's not a model that is poorly calibrated - you seem to be taking a software-centric concept far away from where it's useful. The uncertainty at initial observation is because when you first observe an object, you only have observations covering a tiny bit of the orbit, resulting in very wide error bars. The "model" (Newtonian orbital dynamics) is one of the most precise models we have. Doesn't help when the observations are noisy.

MauranKilom 3y ago
Unless one in every 33 asteroids that have 3% impact probability at some point in time actually impacts earth, there is clearly some unwarranted assumption in the error bar/distribution calculation. "The measurement data has noise" does not explain why the noise has a bias towards "the asteroid will hit earth" whereas reality so far has been biased towards "the asteroid will not hit earth". (This assumes that significantly more than 33 asteroids have had >= 3% impact probability predicted at some point. The opposite would not be less concerning.)
- noduerme 3y ago
  One explanation would be the Anthropic Principle. In 3% of universes you were killed today, you're just not living in one of those.
  - jacquesm 3y ago
    In 97% of the universes dinosaur descendants rule planet earth. But on this one they got unlucky.
  - MauranKilom 3y ago
    This only works if there is nothing between "no impact" and "you die the same day as the impact". But we know that's not the case.
    - noduerme 3y ago
      Why "same day" and not same week or month? If it's not the same instant, then you're hypothesizing some kind of back-propagation (where alternate futures in which you die influence the likelihood of current events)[0]. Under that hypothesis, it would only matter whether some event would cause you to die sooner than you otherwise would. [0] https://arxiv.org/abs/0707.1919 https://arxiv.org/abs/0707.1919 [edit] FWIW, I actually corresponded with one of the authors of this paper back in 2007, and from what I could tell, this wasn't an attempt at parody, although now it might be dismissed as one. Personally I'm not willing to declare my (non)commitment to the theory either way.
- jacquesm 3y ago
  That's because Earth has gravity, and an asteroid that comes close enough can get deflected onto the planet even if right now it seems to be on a trajectory to miss it entirely. The closer they get and the lower the relative speeds the larger the chance that they will collide and that's not a linear relationship. Beyond a certain boundary impact is certain, then the question is what the time of the impact is and how precise the observations up to that point are in order to figure out where and when exactly it will come down. That won't happen very long before the impact itself happens even if you could say some time in advance roughly in which hemisphere and roughly when. But not precise enough to be very useful.
deleted 3y ago
[deleted]
snewman 3y ago
If you have very wide error bars, shouldn't your estimate of impact probability be much lower than 3%? Most trajectories within your error bars will not intersect the Earth.
- jacquesm 3y ago
  The 3% is likely the median of the probability range. You need more observations (and more accurate ones to narrow it further down, but for a first estimate it will do).

aaron695 3y ago

> This makes sense, thanks. It makes no fucking sense. There is 3% chance it'll be revised to 100% chance and 97% chance it'll be revised to 0% chance. Are you Yogi Berra? It'll be reported like possible hurricanes hitting landfall. If this has happed ~33 times then one will hit us. If it's 400m it will kill 200,000 people assuming Vox is reporting correctly - https://www.vox.com/future-perfect/2019/7/26/8931776/near-earth-asteroid-tracking https://www.vox.com/future-perfect/2019/7/26/8931776/near-ea... That will be 9 in 10 it hit's boring ocean and looks cool on satellites and one in 10 kills 2 million people and there will be some cool live streams. Or the 3% in the title is a lie.

jryb 3y ago
> If this has happed ~33 times then one will hit us. There would be a ~63.4% chance that at least one would hit us if this happened 33 times. To compute this, take 1-(0.97^33). But I agree with your broader intuition that these predictions must be getting inflated.

apatil 3y ago

In hindsight this comment was too short. Clarifying some points: By "This makes sense", I meant that this kind of thing can happen; as more data are gathered, the Bayesian probability of a candidate value can increase and then suddenly decrease. Here's a Colab notebook demonstrating the general phenomenon: https://colab.research.google.com/drive/1Eb1_humiGPdKb0c3qr_0k1yYDWBVODKm?usp=sharing https://colab.research.google.com/drive/1Eb1_humiGPdKb0c3qr_... "Calibration" in this context means "statistical consistency between distributional forecasts and observations" in the words of https://sites.stat.washington.edu/raftery/Research/PDF/Gneiting2007jrssb.pdf https://sites.stat.washington.edu/raftery/Research/PDF/Gneit... . If the model's early forecasts predict impact with probability >3% for a class of objects that end up impacting with frequency much less than 3%, then the model is not well calibrated with respect to its early forecasts for those objects. Based on the GP, it sounds like these early impact "probabilities" are no one's subjective (Bayesian) probability of impact because people who are closely familiar with this model know it is not well calibrated. The reported probabilities may still be useful to them as indicators or flags. However, those of us who are _not_ closely familiar with the model have found it confusing to see things that are not really probabilities reported as probabilities.