Do arguments arising from probability convincingly argue a mass human extinction event in the near future? [closed]

Question

One such argument is the Doomsday argument which is taken seriously by a number of academics. But more simply, if we look at the modern population trajectory, it's something of an exponential curve. What would probability theory predict about the graph in the future?

It seems there are three basic possible trends:

1. continuation of exponential growth ad infinitum
2. plateau
3. a bell curve

If #1 were true, then we would never exist, since the future always has a greater probability density than the present and the past. If #2 were true, then we would also never exist, for the same reason as #1. Therefore, something like #3 is necessary to explain our existence.

To maximize the probability of our experience, we most likely are on or near the cusp of the bell curve. This means that a mass extinction event for humanity looms in the near future.

One might object and say we are at the top of the curve, but the downward slope could be gradual instead of abrupt. However, in that case the expected position would be already on the downward slope, and we wouldn't expect to see what we see today that we are on the upward slope.

So, what do you think about this reasoning? Does probability theory indicate a very large portion of humanity is about to die off?

Answer 1

If #1 were true, then we would never exist, since the future always has a greater probability density than the present and the past. If #2 were true, then we would also never exist, for the same reason as #1.

This is incorrect. 1 and 2 just suggest that we are unusual according to a particular parameter. Being unusual according to a particular parameter is the most usual thing there is, and you don't even need two parameters to make that be true. All you need to do is shift where you put the arbitrary origin of the parameter.

For example, suppose that in the distant future, the total number of humans that have ever existed is 10^100. If we parameterize humans by number of past humans, then we are part of a very unusual 100-gigahuman sample. If we parameterize humans by number of humans between you and the 0.33 x 10^100th human - exact same parameter, different origin - all of a sudden we're smack on top of the bell curve.

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The doomsday argument cited in a previous answer is also nonsense for a different reason: statistics only generates useful predictions about what's likely given the available evidence if you use the available evidence.

For instance, suppose Bob has a cat. Bob knows that cats have an average life expectancy of 14 years. If all Bob knows is that he has a cat and that cats live for 14 years, Bob should expect his cat to live another 7 years. On the other hand, if Bob has a sick 20-year-old cat whose siblings all died years ago, he should not expect his cat to live more than another year or two. If Bob has a healthy kitten from a long-lived breed and plans to take good care of it, he could reasonably expect his cat to live another 17 years. If Bob has just run over a cat in his truck, he should not think to himself, "No big deal, it's a cat. Statistics says it'll probably live another 7 years!"

The valid statistical inference is not "If Bob has a cat and cats live 14 years on average, then Bob's cat will probably live 7 more years."

It's "If the totality of known facts is: [Bob has a cat; cats live 14 years on average], then Bob's cat will probably live 7 more years."

Try another one. There is a raffle. You have 1 out of 100 tickets. There will be 10 winners. You start by predicting a 10/100 chance of winning.

The first winning ticket is identified. It is not yours. You now predict a 9/99 chance of winning. The second winner is read - 8/98. All of a sudden, a dozen angry badgers are set loose in the crowd. Maybe the raffle will close early and there won't be any more winners. Maybe the first two winners will be eaten by badgers and two new winners will be drawn. You just don't know. Adding more knowledge - first about the first two winners, then about a dozen angry badgers - at first decreased your probability of winning, then greatly increased the error bars on your estimates.

In this case, if literally all you know about humans is that you are one and that they die, then the cited argument holds: you are indeed 95% likely to be among the last 95% of humans. And 95% likely to be among any other group of 95% of humans. Including, for example, the 95% of humans whose circumstances are the least like the circumstances that we actually measure.

Answer 2

Doomsday Argument

Assume the total number of people who'll ever live = N Where are we among this N? We will call this our fractional position F. If the total number of people who've lived so far is n then, we see that F = n/N.

F is the probability of being born at a particular population value and is uniformly distributed on (0, 1). 0 ≤ Probability ≤ 1.

There is a 95% chance that we're in the interval (0.05, 1), as 1 − 0.05 = 0.95 = 95%.

So F> 0.05 ⇒ n/N > 0.05 ⇒ n/N > 1/20 ⇒ 20n > N or N < 20n.

We have a rough estimate of n = 60 billion people who've lived so far.

20 × 60 billion > N i.e. N < 1.2 trillion people.

The maximum number of people that'll ever live = N (as we assumed) = 1.2 trillion.

Currently we're at 8 billion. The rest 1,192,000,000 (1.192 trillion) is estimated to be born in the comin 9120 years.

The article on Wikipedia ends with a statement that it is unlikely that there'll live more than 1.2 trillion people. One reason why that might be is a mass extinction.

Answer 3

No. Any such argument of a mass die-off is purely speculative and not supported by theory or practice.

1. Probability does not indicate a mass die off, but first one has to understand what is meant by probability in this context. There are several meanings of the word probable because there are several theories. When talking about global population models, we have exactly 0 global models to use as predictors for own models, so it's tough to make frequentist claims without a prior history. That is to say, coin tosses through the frequentist lens are meaningful in the language of probability because of classical and frequentist definitions of probability and our experience with it. We simply have no experience regarding population growth models for planets because we are the only known planet.

2. But the problem with an argument for a mass die off is bigger than the abuse of the term probability in this picture. Human reason is defeasible. Any claims that we know enough to make such a prediction are undermined that in the topic of global population collapse, we simply do not know what we do not know. For instance, consider that as the population grows from 8 to 9 billion, there might be effects in terms of productivity of R&D that affect our current models about food production. In other words, maybe in the next couple of decades, a single food technology (converting cellulose to protein and carbohydrates) is unleashed and suddenly wood and grass can be fed into a machine that creates nutritional paste. Imagine 100 different technologies that could have a positive or negative impact.

3. Complex events are next to impossible to predict. Something as complex as a system of 8 billion people on a planet does not allow for accurate prediction across a variety of inputs to the model. In business, for instance, there is a class of phenomena known as black swan events. In the paragraph above, we talked about a single technology that disrupts our understanding, but there are thousands of factors that one might have to deal with. What are the effects of new IT? What about global warning? Diseases and disease fighting factors? Military conflicts? Even asteroid impacts could have a massive influence on events and populations, but such events are too complex to have any faith in reliable prediction.

4. Statisticians are pretty comfortable predicting a decline in world population given factors like health care, longevity, and birth rates. Population growth is not entirely a mystery. There are statisticians who do make a good effort, and they theorize declines, probably in our children's lives. One famous statistician who made such predictions is Hans Rosling (YT).

Now, this is not to say that the world won't suffer a crash. One sufficiently virulent disease, a cascading thermonuclear conflict, or a comet could bring the world crashing down around us. But conceivability is not the same as probability, and it's important in an argument to understand how probability works when people make probabilistic claims because the concept 'probable' is often abused for rhetorical purposes with people relying on a subjectivist interpretation. There are many fallacies that rely on abuses of statistics and probability.