Examples of Mitigating Assumption Risk

Keeping necessary materials and short-term inputs stored locally, to avoid exposure to supply chains or service outages.

Build and use applications that don't rely on always-on internet.

Download files, including music and videos, whenever possible, in addition to any cloud storage.

Locate resource sources and/or stored reserves close to you in physical space. Keep reserves of key materials, and of tradable resources.

Gather capital that cannot be confiscated or lost, especially human capital.

Avoid debt, especially debt that is not carefully bounded, is high interest or that could potentially balloon in size.

Stress test to trigger failure modes, so you know where those modes are, and also so you know what the consequences of failure are in context (and to remind yourself that they're not so bad).

Avoid or minimize prioritizing tasks by urgency or deadline when possible.

Avoid priortizing tasks by value beyond a certain threshold, so long as you are confident that you have sufficient resources to complete all tasks above that threshold.

Don't entirely hold off on engaging in high-value places even if you would prefer a higher-quality response. (e.g. the problem where you don't respond to an email because it deserves a good response, so it never gets one at all).

(This seems fun and useful, these were my first brainstorms)

The most commonsense example of making assumptions irrelevant I've heard of is from weapons safety: always act as if the gun is loaded.

See also

• Cut away from yourself
• Rule #2: Double tap
• Eating Mealsquares instead of Soylent

Don't deceive yourself even if it seems like a really really good idea.

Don't falsify data, frame people for bad things they didn't do, or hide bad things your allies are doing even if it seems like a really really good idea.

Prepare ahead of time for disasters. Learn first aid. Know what to do in the event of nuclear war. Keep essential first aid and disaster preparedness supplies on hand.

Assume every new sex partner is fertile and has HIV, and decide your safer-sex risk tolerance based on that.

Build slack. Have fuck-you money. Build extra time into your schedule in case something goes wrong.

Charge your phone and your laptop before leaving the house. Always take more books (or whatever your preferred form of entertainment is) than you think you'll need.

When hanging out privately with a stranger, tell a friend when you expect to return and when they should start freaking out.

Always have an exit plan for your job, relationship, and intentional community.

Adding a margin of safety when estimating how much load a bridge can bear

Yes, but after a certain point, "total load" will stop being your important metric and you need to think about other things like:

• Am I assuming a stable, evenly distributed load? (Or e.g. could an army marching across the bridge in step, or traffic only on one side, cause problems?)
• Is "the bridge falls down" the only material way the bridge could fail to hold up the traffic on it? (E.g. maybe you want good guardrails.)
• Are there other sources of stress other than load? (E.g. "ground is unstable" or "wind blows really fast".)
• Is the bridge likely to stay built as-designed? (Bridges mostly don't have people trying to take them apart, but a reasonably common source of reduced automobile performance is catalytic converter theft, since it's a fairly easily accessible part containing precious metals. There are also nonagentic problems like erosion.)

Other things equal, choose the reversible alternative.

Backup your stuff: My backup process is to scan or take pictures of items. Put files on an external hard drive. Backup that external drive to another external hard drive. Then I backup up everything to the cloud through Backblaze. That way you have the physical items themselves, the items on hard drive1 that you take with you, the items on hard drive2 that you store somewhere, AND everything is on the cloud. It may seem excessive but it's easy once you have everything set up. There are more risk mitigation tips in my post on my experiences prepping for a hurricane for those interested.

I love posts of examples! This one in particular I'm interested in, as I'm writing a post about making life-plans that involve mitigating assumption risk. I've moved this to the frontpage (currently there isn't functionality for you to prevent posts from being moved to the frontpage, so do move it back if that's your preference).

"Statistical models with fewer assumptions" is a tricky one, because the conditions under which your inferences work is not identical to the conditions you assume when deriving your inferences.

I mostly have in mind a historical controversy in the mathematical study of evolution. Joseph Felsenstein introduced maximum likelihood methods for inferring phylogenetic trees. He assumed a probabilistic model for how DNA sequences change over time, and from that he derived maximum likelihood estimates of phylogenetic trees of species based on their DNA sequences.

Felsenstein's maximum likelihood method was an alternative to another method, the "maximum parsimony" method. The maximum parsimony tree is the tree that requires you to assume the fewest possible sequence changes when explaining the data.

Some people criticized Felsenstein's maximum likelihood method, since it assumed a statistical model, whereas the maximum parsimony method did not. Felsenstein's response was to exhibit a phylogenetic tree and model of sequence change where maximum parsimony failed. Specifically, it was a tree connecting four species. And when you randomly generate DNA sequences using this tree and the specified probability model for sequence change, maximum parsimony gives the wrong result. When you generate short sequences, it may give the right result by chance, but as you generate longer seqences, maximum parsimony will, with probability 1, converge on the wrong tree. In statistical terms, maximum parsimony is inconsistent: it fails in the infinite-data limit, at least when that is the data-generating process.

What does this mean for the criticism that maximum likelihood makes assumptions? Well, it's true that maximum likelihood works when the data-generating process matches our assumptions, and may not work otherwise. But maximum parsimony also works for a limited set of data-generating processes. Can users of maximum parsimony, then, be accused of making the assumption that the data-generating process is one on which maximum parsimony is consistent?

The field of phylogenetic inference has since become very simulation-heavy. They assume a data-generating process, and test the output of maximum likelihood, maximum parsimony, and other methods. The conceern is, therefore, not so much on how many assumptions the statistical method makes, but on what range of data-generating processes it gives correct results.

This is an important distinction because, while we can assume that the maximum likelihood method works when its assumptions are true, it may also work when its assumptions are false. We have to explore with theory and simulations what is the set of data-generating processes on which it is effective, just like we do with "assumption-free" methods like maximum parsimony.

For more info, some of this story can be found in Felsenstein's book "Inferring Phylogenies", which also contains references to many of the original papers.