It all began with this message from Christopher Bonnett:

I’m a observational cosmologist and I am writing you as I think the following paper + article might be of interest for your blog.

A fellow cosmologist, Fergus Simpson, has done a Bayesian analysis on the size of aliens, it has passed peer-review and has been published in Monthly Notices, a reputable astronomy journal. (just to make clear that it’s not some crackpot theory). Fergus has also written a blog-post with a less technical explanation of his research.

As the article is very heavy on bayesian analysis, I thought it might be an interesting topic for you and your co-bloggers to read and potentially blog about.

Indeed, this reminded me of the example on page 90 of this paper.

I see two problems with Simpson’s analysis, problems that are serious enough that it all just seems wrong to me. My first problem is the prior or distribution of aliens, and my second problem is the likelihood or model for what is observed. I don’t actually see any prior at all in the paper, I guess there must be some implicit prior, but otherwise it would seems for anyone to say much from a sample of size 1 with no prior. [No, I was wrong, there is a prior; see P.P.S. below. — AG] As for the likelihood, the assumption seems to be that we, or Fergus Simpson, in writing this paper, is being randomly sampled from all creatures in the universe, or I guess all creatures that have the ability to think these thoughts, or maybe all creatures that have the ability to get a paper published in an astronomy journal, or . . . whatever. It doesn’t make sense to me.

I forwarded the discussion to David Hogg, who wrote:

Amusing idea, but there is only so far you can go in these “we have one data point” studies. That said, we want to pick targets for spectroscopy and imaging carefully, since each imaged planet is likely to cost upwards of $50 million to image (see current plans for terrestrial planet imaging).

Also, why didn’t he also use the data point that Mars (which is also habitable-zone)

doesn’thave intelligent life on it? That might be more informative than the datum that Earth does have life.

That’s pretty much it for me, but Fergus Simpson did reply, so I’ll also include some of our back-and-forth discussion:

Simpson:

Andrew – you mentioned a concern about the “prior or distribution of aliens”, which I presume refers to the distribution of population sizes? It may sound surprising, but the secondary inferences (e.g. the sampling bias on planet size and body size) are insensitive to this function. For example, consider dividing all inhabited planets into large (above median) and small (sub-median) size. Now if we suppose that the mean population of the large planets are four times greater than the mean population of the small planets, then the odds of living on a large planet are 4:1. No matter how one chooses to distribute the aliens amongst the individual planets, the odds remain 4:1.

David – regarding your comment on Mars – it is certainly an interesting case. To make use of this datapoint one could decompose the size distribution of inhabited planets p(r) in terms of a product of the total number of planets n(r) and the habitation fraction i(r) such that

p(r) \propto n(r)*i(r)

However there are a couple of problems – the first is we don’t know n(r) very well below for r<1 Earth radii, and the second is that i(r) is probably very much less than unity. So to find a single example (Mars) devoid of intelligent life isn't very informative. So instead of using that formulation, the approach I adopted was to jump straight to the (very unknown) distribution of inhabited planets p(r), and marginalise over different means and standard deviations of that function with some reasonable sets of priors. Regarding the idea that "there is only so far you can go in these 'we have one data point' studies": This is something I've heard mentioned frequently, but have yet to see compelling evidence! If one performs a single roll of a fair die, then nothing is learned of what is written on the other faces. However if one rolls a loaded die - where the probability of rolling each face is weighted by the number shown on the face - it's a different story. If you roll a 10 with this loaded die, you can suddenly be confident that all the other faces are not much greater than 10. That's essentially what's happening here.

Me:

The prior distribution or base rate has to come into the calculation; it’s just basic probability theory. The other issue is that we are not a random sample of creatures in the universe, or even a random sample of sentient creatures. So I don’t think your analogy with countries of the world works out. In your analogy, one is picking a random person among all humans on earth. But in the planet example, we’re just us, we’re not randomly-selected critters.

Simpson:

In equation (5) I employ a reference prior as denoted by pi(mu, sigma^2). Also Figure 1 illustrates the different outcomes one reaches when adopting different prior distributions. If there’s a particular equation that’s troubling you, please let me know.

Regarding the sampling model, consider the following thought experiment – imagine humans had colonised other planets, as well as other continents. If one was uninformed which planet you lived on, it would still be reasonable to weight one’s probability in the same manner as countries – i.e. more likely to live on a higher population planet. Now, if those colonies on other planets had not travelled from earth, but evolved independently, would this reasoning change, and why?

Hogg:

Your conclusion about planet sizes is (as I see it) that you expect them to be near Earth size, and your conclusion about animal sizes (as I see it) is that they be near human sizes. Of course you get a distribution, but it is peaked near the Earth and its shape is set by your prior, really, not your likelihood. So the data are just saying “I expect things to be like the single data point I have seen, with some skew towards regions of higher prior probability”. That is interesting, but limited.

Another way to say it: The likelihood principle says that all the *new* information is coming from the likelihood. The likelihood, with one data point, is not very informative. The paper would have the same content if all you had plotted is the likelihood function, and you would have found that it is very broad, I expect.

Me (to Simpson):

I still don’t buy the random sampling assumption. And once you start thinking of random sampling, why sample creatures at random? Why not sample molecules, or cells, or, to go at it from the other way, families, or societies? To put it another way, in your countries example the key assumption is that a human is being selected at random.

Simpson:

So my perspective would be this – I’m experiencing a stream of consciousness originating from a neural network which is housed within a shell (my body). And I observe there to be many others such as yourself who are experiencing the same phenomenon. I have no idea how I ended up in this particular shell. It doesn’t appear to be special (people who are over 7 feet tall might decide otherwise!). As I see it the shell itself is irrelevant – what matters is the neural network which triggers consciousness. And our neural networks are all extremely similar. So my belief is that each shell which houses a conscious mind was equally probable. But if you happen to believe that you were more likely to inhabit some shells than others, that is your prerogative.

As I see it, Simpson is postulating andom sampling, and that’s just a model he has, I don’t see how it applies at all. He’s got an urn model but there ain’t no urn. Of course, as a practitioner of likelihood-based statistics, I work with such models all the time (you didn’t think those Poisson distributions and normal distributions and logistic regression models were *real*, did you?), but the point is that the model needs some justification, and in this case I don’t see any at all! What I see is an argument by analogy that doesn’t really make sense. I suppose that to really shoot it down I’d need to create my own model, some hypothetical distribution of planet sizes and populations of sentient creatures, and show how Simpson’s method could give wrong answers. Perhaps worth doing sometime, but in any case I thought I’d post our discussion here. I appreciate Simpson’s cordial engagement with skeptics.

**P.S.** See section 4 of this paper for a Bayesian discussion of a similar theory (the “doomsday argument”).

**P.P.S.** Regarding the prior distribution, Simpson writes:

– In equation (5) I employ a reference prior as denoted by pi(mu, sigma^2)

– Figure 2 (the main result of the paper) plots lines which are

labelled ‘narrow prior’ and broad prior’– Most of Section 3 is spent discussing the choice of prior, and

studying the results of modifying the prior.

Andrew, not only is there a prior in the calculation, but I pointed it out in the email you quote above:

“In equation (5) I employ a reference prior as denoted by pi(mu, sigma^2)”

There is also now a nice animated video from MinutePhysics which explains the argument with greater clarity:

https://www.youtube.com/watch?v=KRGca_Ya6OM

Question: I didn’t get deeply into this paper, but I wondered whether this model takes into account scaling of creature size, gravitation, thermodynamics, and the strength of materials?

I think this is the strongest information we have about creature size, and the size of planets and the relative populations of various types of organisms. It’s no accident that the Blue Whale, the largest organism ever to live on the planet, lives in the ocean, or that sauropod dinosaurs are pretty much a stomach on stumpy legs with a long thin hose that moves around to feed the stomach. It’s also no coincidence that there are far more insects per unit area than humans.

Also I have to believe that the largest sauropods would be probably closer to elephants if the gravitational acceleration at the surface of the earth were say 2x what it is now (presumably due to larger radius r)

This sort of cheap anthropic argument has been tried before. For instance: if population levels remain steady or continue to grow for the next few centuries, the number of people born in the future will dwarf the number born up to now. Under such circumstances, and under a random sampling model, the odds are vanishingly unlikely that I would have been born this early in history. Therefore, I conclude with a high level of certainty that the apocalypse must be nigh?

There’s a reason these sorts of arguments haven’t taken hold. I mean, if I had absolutely no information about the course of human history, and I absolutely had to take a guess as to whether my birthdate is or is not in the bottom 5th percentile of the birthdates of all humans ever born, I would certainly have to guess it is not. But I would be very eager to revise or reject this model; I could be hand-waved into accepting a very different prior based on even the slightest new evidence. Which is to say my confidence in this guess is almost nil (to say nothing of the problems potentially hidden in the term “humans”).

At root, the approach doesn’t really make sense. “I” could only have been born now (or at least, it is vanishingly unlikely that a person just like me would be born in the year 3000). To talk about the probability that “I” would be born under different circumstances is meaningless or trivial. If I believed that my soul was created independently and was assigned to my body at random, then maybe we could talk about the probability of landing here or there, but I don’t believe in that. Simpson seems to be proposing the same thing in scientific terms: “neural networks” are assigned randomly to “shells.” Of course this is literally untrue as Simpson would be the first to acknowledge: he’s saying that IT IS AS IF this were true. But on inspection I don’t see any strong argument as to WHY it should be as if this were true.

I think this is one of the big reasons that Bayesian approaches are valuable. You have to be explicit about everything that goes into the soup, and it’s easy to see how a somewhat silly idea translates into somewhat silly results. How can you possibly learn much about the size of all species in the universe when you are only looking at one species? This approach wouldn’t even tell you anything informative about the distribution of sizes of Earth species, let alone alien ones!

I’m bothered by the whole notion of “intelligent life”.

Until humans arrived on the scene, there was no concept of “intelligence” in the sense we mean it when we talk of intelligent life. So, (a) what’s to say intelligence in the human sense has much to do with life? (e.g. dinosaur “intelligence” seems to have been capable of dominating the planet for a long time), and (b) what’s to say there aren’t other concepts out there that future species on earth will identify?

[of course, now that we have the notion of intelligence, we can see that other species show signs of intelligence, although they themselves wouldn’t recognize that word. Similarly, whatever new concept comes up from some future species might exist in nascent form in us, even though we don’t yet recognize the concept]

Net: there’s so much uncertainty out there that this will likely remain the domain of science fiction writers, not statisticians, for my lifetime at least.

I wrote a paper in high school in 1975 about how the Drake Equation

https://en.wikipedia.org/wiki/Drake_equation

pretty much proved that the new giant radio telescopes would pick up evidence of intelligent aliens any day now.

That did happen, right?

There are well known problems with assuming your experiences (consciousness whatever) are sampled from some space of possible experiences. The best known (and argued about at length in philsoophical circles) example is the sleeping beauty problem:

You are told on Sunday you are about to be anestitized and placed in a room with no calendar. At which point a coin will be flipped. Regardless of the outcome of the coin flip you will be woken up on monday, asked if you would like to wager $1 on the coin landing heads with even odds put back to sleep and have your memory of monday wiped. If the coin landed heads you are woken again on tuesday offered the same wager and then put back to sleep. Either way you are woken up at the end of the experiment on wednesday.

The question is upon waking up what probability should you assign to the coin being heads.

On the one hand you knew for certain you would have the experience of waking up during the experiment at least once so your conditional probability should remain P(H)=.5.

On the other hand if you take your experiences of waking up during the experiment (with sunday as your last memory) to be drawn uniformly from the set of all experiences of that kind then, as there are two such experiences if the coin lands heads, you should say P(H)=2/3. To further motivate this answer note that if you adopt the policy of betting the coin lands heads your expected return on your bets is positive.

A great deal of philosophy has been written about the issue…and in my opinion makes the mistake of assuming there is the one correct answer. Quite possibly this demonstrates an ambiguity in our understanding of the concept (not the mathematical space but it’s scientific meaning) where different related concepts come apart.

In any case I think the argument you mentioned makes the mistake of reasoning in the way that argues P(H)=2/3 and this is clearly inappropriate if you intend to use probability as a measure of what is plausibly true (as opposed to say rational betting odds). To illustrate consider the following super extreme argument:

I assign some small, non-zero, prior that in addition to this universe there are infinitely many duplicates of it which, up until this point, are exactly the same. By the reasoning above (as there are infinitely many more conscionesses I could be in the infinite duplicate case) I should infer that my posterior probability (conditional on having this experience/memory) of there being infinitely many duplicates is 1.

One can (and people have) analyzed this in much greater detail and I am glossing over subtlties but it emphasizes how misleading this kind of argument can be.

The argument is flawed. Simpson fails to take into account that the bets (on inhabiting a populous world) are correlated for every planet. Thus the wager that I am on a populous planet has the same probability as my planet does, which is 0.5 in the absence of any further information.

A far worse problem with Simpson’s “model” is something else. Simpson draws a plausibly correct conclusion for wrong reasons. He offers Bayesian evidence for the prevalence of waterworlds, which predicts that most habitable planets are dominated by oceans spanning over 90 per cent of their surface area (95 per cent credible interval). This scenario, in which the Earth has a much greater land area than most habitable planets, offers up one of my own top ten hypotheses to explain the Fermi Paradox. Nevertheless, I have comments:

1- By “habitable “ you of course exclude by far the most prevalent domains of liquid water, under ice roofs – worlds like Europa – which can occer anywhere, not just in a CHZ.

2- Earth appears to skate the very inner edge of our sun’s CHZ. This offers a possible cause for the high continental area, one that does not rely on anthropic coincidence. Water worlds that orbit farther out (and our sun’s CHZ extends beyond Mars, according to Kasting et al., would reach a balance with more CO2 in the atmosphere and likely also wetter.

3- A proper Gaia balance relies on volcanoes to replenish CO2 in the atmosphere and weathering of continental rocks to let oceans remove CO2. Life can use the weathered material but life is not essential. A world with too little continental area will not weather enough minerals and thus CO2 will build up till the heat causes some loss of water.

#2 and #3 are factors that need to be considered in Simpson’s model. Mind you, I do believe it plausible that Earth is dryer than average for water worlds that may be a “fermi” of some relevance. It may indeed be the most optimistic theory of them all. But I do not believe the Bayesian derivation of this outcome.

With cordial regards,

David Brin, PHD, author of EARTH, EXISTENCE and The Postman

http://www.davidbrin.com

David:

Thanks for commenting here! How cool is that???

My expertise on this matter is limited to the application of probability theory and statistical modeling, and I don’t know enough to even attempt to assess your arguments regarding earth-like planets (I’d never thought about the volcano thing before), but I very much appreciate your participation in this public discussion.

I agree with the general point from @Brin (and it somehow maps onto my comments in the original blog post): These models are models for life that is very much Earth-life-like. Life on Enceladus might be hella different.

Hi David, thanks for your comments! Some brief thoughts on the points you’ve raised:

1 – That’s correct, I was only considering the ensemble of bodies with liquid water on the surface. As mentioned in the article (arXiv:1607.03095), Europa and Titan are helpful examples of how ‘oceans’ need not be finely balanced to match their basin capacity.

2 – Perhaps I misunderstood this point, but let’s look at an even bigger coincidence. There are over 100,000 contiguous pieces of land scattered across the Earth’s surface. Yet you live on one of the ten largest. Is that a coincidence? Or could we interpret that by assigning unequal probability to each one?

3 – Geological and astrophysical factors aren’t explicitly required by the model, as I simply propose that some distribution of oceanic volumes exist across the ensemble of habitable planets. The particular mechanism responsible for this variability isn’t important. And yet, I strongly agree that the underlying physics is very important if we wish to arrive at a complete understanding of the problem. For example, in answering the question “why is your bike red?”, one could rely on physics and chemistry, by talking about the pigment of the paintwork. Alternatively one could point out that you chose a red bike because you like the colour red (i.e. the anthropic explanation). I believe both answers are equally valid, and provide their own unique insights. So when it comes to habitable planets, I think there is scope for a statistical interpretation alongside a conventional physical explanation, such as the one you’re considering.