0: Recoverable Catastrophe

An apocalypse is an event that permanently damages the world. This scale is for scenarios that are much worse than any normal disaster. Even if 100 million people die in a war, the rest of the world can eventually rebuild and keep going.

1: Economic Apocalypse

The human carrying capacity of the planet depends on the world's systems of industry, shipping, agriculture, and organizations. If the planet's economic and infrastructural systems were destroyed, then we would have to rely on more local farming, and we could not support as high a population or standard of living. In addition, rebuilding the world economy could be very difficult if the Earth's mineral and fossil fuel resources are already depleted.

2: Communications Apocalypse

If large regions of the Earth become depopulated, or if sufficiently many humans die in the catastrophe, it's possible that regions and continents could be isolated from one another. In this scenario, globalization is reversed by obstacles to long-distance communication and travel. Telecommunications, the internet, and air travel are no longer common. Humans are reduced to multiple, isolated communities.

3: Knowledge Apocalypse

If the loss of human population and institutions is so extreme that a large portion of human cultural or technological knowledge is lost, it could reverse one of the most reliable trends in modern history. Some innovations and scientific models can take millennia to develop from scratch.

4: Human Apocalypse

Even if the human population were to be violently reduced by 90%, it's easy to imagine the survivors slowly resettling the planet, given the resources and opportunity. But a sufficiently extreme transformation of the Earth could drive the human species completely extinct. To many people, this is the worst possible outcome, and any further developments are irrelevant next to the end of human history.

 

5: Biosphere Apocalypse

In some scenarios (such as the physical destruction of the Earth), one can imagine the extinction not just of humans, but of all known life. Only astrophysical and geological phenomena would be left in this region of the universe. In this timeline we are unlikely to be succeeded by any familiar life forms.

6: Galactic Apocalypse

A rare few scenarios have the potential to wipe out not just Earth, but also all nearby space. This usually comes up in discussions of hostile artificial superintelligence, or very destructive chain reactions of exotic matter. However, the nature of cosmic inflation and extraterrestrial intelligence is still unknown, so it's possible that some phenomenon will ultimately interfere with the destruction.

7: Universal Apocalypse

This form of destruction is thankfully exotic. People discuss the loss of all of existence as an effect of topics like false vacuum bubbles, simulationist termination, solipsistic or anthropic observer effects, Boltzmann brain fluctuations, time travel, or religious eschatology.

The goal of this scale is to give a little more resolution to a speculative, unfamiliar space, in the same sense that the Kardashev Scale provides a little terminology to talk about the distant topic of interstellar civilizations. It can be important in x risk conversations to distinguish between disasters and truly worst-case scenarios. Even if some of these scenarios are unlikely or impossible, they are nevertheless discussed, and terminology can be useful to facilitate conversation.

In risk modeling, there is a well-known distinction between aleatory and epistemic uncertainty, which is sometimes referred to, or thought of, as irreducible versus reducible uncertainty. Epistemic uncertainty exists in our map; as Eliezer put it, “The Bayesian says, ‘Uncertainty exists in the map, not in the territory.’” Aleatory uncertainty, however, exists in the territory. (Well, at least according to our map that uses quantum mechanics, according to Bells Theorem – like, say, the time at which a radioactive atom decays.) This is what people call quantum uncertainty, indeterminism, true randomness, or recently (and somewhat confusingly to myself) ontological randomness – referring to the fact that our ontology allows randomness, not that the ontology itself is in any way random. It may be better, in Lesswrong terms, to think of uncertainty versus randomness – while being aware that the wider world refers to both as uncertainty. But does the distinction matter?

To clarify a key point, many facts are treated as random, such as dice rolls, are actually mostly uncertain – in that with enough physics modeling and inputs, we could predict them. On the other hand, in chaotic systems, there is the possibility that the “true” quantum randomness can propagate upwards into macro-level uncertainty. For example, a sphere of highly refined and shaped uranium that is *exactly* at the critical mass will set off a nuclear chain reaction, or not, based on the quantum physics of whether the neutrons from one of the first set of decays sets off a chain reaction – after enough of them decay, it will be reduced beyond the critical mass, and become increasingly unlikely to set off a nuclear chain reaction. Of course, the question of whether the nuclear sphere is above or below the critical mass (given its geometry, etc.) can be a difficult to measure uncertainty, but it’s not aleatory – though some part of the question of whether it kills the guy trying to measure whether it’s just above or just below the critical mass will be random – so maybe it’s not worth finding out. And that brings me to the key point.

In a large class of risk problems, there are factors treated as aleatory – but they may be epistemic, just at a level where finding the “true” factors and outcomes is prohibitively expensive. Potentially, the timing of an earthquake that would happen at some point in the future could be determined exactly via a simulation of the relevant data. Why is it considered aleatory by most risk analysts? Well, doing it might require a destructive, currently technologically impossible deconstruction of the entire earth – making the earthquake irrelevant. We would start with measurement of the position, density, and stress of each relatively macroscopic structure, and the perform a very large physics simulation of the earth as it had existed beforehand. (We have lots of silicon from deconstructing the earth, so I’ll just assume we can now build a big enough computer to simulate this.) Of course, this is not worthwhile – but doing so would potentially show that the actual aleatory uncertainty involved is negligible. Or it could show that we need to model the macroscopically chaotic system to such a high fidelity that microscopic, fundamentally indeterminate factors actually matter – and it was truly aleatory uncertainty. (So we have epistemic uncertainty about whether it’s aleatory; if our map was of high enough fidelity, and was computable, we would know.)

It turns out that most of the time, for the types of problems being discussed, this distinction is irrelevant. If we know that the value of information to determine whether something is aleatory or epistemic is negative, we can treat the uncertainty as randomness. (And usually, we can figure this out via a quick order of magnitude calculation; Value of Perfect information is estimated to be worth $100 to figure out which side the dice lands on in this game, and building and testing / validating any model for predicting it would take me at least 10 hours, my time is worth at least $25/hour, it’s negative.) But sometimes, slightly improved models, and slightly better data, are feasible – and then worth checking whether there is some epistemic uncertainty that we can pay to reduce. In fact, for earthquakes, we’re doing that – we have monitoring systems that can give several minutes of warning, and geological models that can predict to some degree of accuracy the relative likelihood of different sized quakes.

So, in conclusion; most uncertainty is lack of resolution in our map, which we can call epistemic uncertainty. This is true even if lots of people call it “truly random” or irreducibly uncertain – or if they are fancy, aleatory uncertainty. Some of what we assume is uncertainty is really randomness. But lots of the epistemic uncertainty can be safely treated as aleatory randomness, and value of information is what actually makes a difference. And knowing the terminology used elsewhere can be helpful.

Hi guys,

So I've been curating this glossary over at https://www.reddit.com/r/Futurology/.  I want it to be sort of an introduction to future focused topics.  A list of words that the layman can read and be inspired by.  I try to stay away from household words (i.e. cyberspace), science fiction topics (i.e. dyson sphere), words that describe themselves (i.e. self driving cars), obscure and rarely used words (i.e. betelgeuse-brain), and words that can't be found in most dictionaries (i.e. Rocko's Basilisk (i've been meaning to remove that one)).  Most of the glossary is from words and phrases I find on the /r/Futurology forum.  I have a whole other list with potential words for the glossary that i collect just waiting for the day to be added (i.e particle accelerator, Aerogel, proactionary principle).  I find curating the glossary to be more of an art than a science.  I try to balance the list between science, technology, philosophy, ideology, and sociology.  I like to find related topics to expand the list (i.e. terraforming & geoengineering). Even though the glossary is in alphabetical order i want it to read somewhat like a story.    

Anders Sandberg of The Future of Humanities Institute, Oxford told me "I like the usefulness of your list..."

I'm interested to know what you guys think.

Glossary located below (the See /r/*.* is native to the reddit website.  See /r/*.* links the glossary to subreddits (other reddit pages) related to that word or phrase on the reddit website):

I feel that the term "rationalism", as opposed to "rationality", or "study of rationality", has undesirable connotations. My concerns are presented well by Eric Drexler in the article For Darwin’s sake, reject "Darwin-ism" (and other pernicious terms):

To call something an “ism” suggests that it is a matter ideology or faith, like Trotskyism or creationism. In the evolution wars, the term “evolutionism” is used to insinuate that the modern understanding of the principles, mechanisms, and pervasive consequences of evolution is no more than the dogma of a sect within science. It creates a false equivalence between a mountain of knowledge and the emptiness called “creationism”.

So, my suggestion is to use "rationality" consistently and to avoid using "rationalism". Via similarity to "scientist" and "physicist", "rationalist" doesn't seem to have the same problem. Discuss.

(Typical usage on Less Wrong is this way already, 3720 Google results for "rationality" and 1210 for "rationalist", against 251 for "rationalism". I've made this post as a reference for when someone uses "rationalism".)