> common in LP formulations?

If you want to consider the effects of war, inflation, something like the 2008 housing crash, economic depression, COVID, hurricanes, train derailments, etc., sure, if those hypothetical changes still yield an LP (linear program), can just solve the resulting LPs and display the resulting data in a way can visualize and get some intuitive insight.

But there is another way: Let insurance companies consider history and set insurance premiums for disasters, all disasters, ever seen before or not, and they will have quite good data, likely, sorry, better than get from RL (reinforcement learning) or, really, anything that can be obtained from anything like current analysis of data from Web crawling. A common assumption, understanding in business is that it is possible to get insurance on nearly anything against nearly anything. E.g., the whole thing about the book and movie The Big Short was that it was possible to get insurance on the BBB tranches of housing mortgage bonds, make a bet that the tranches would fail. For this would have to pay monthly premiums but when the tranches failed make huge returns on the premiums paid.

With their good data and history, an insurance company will be able to set insurance premiums that give you some good information about the seriousness of the disasters, on your list of disasters or not, you are concerned about. If seeing the premiums you are still highly concerned, then pay the premiums and relax.

As in The Big Short, call up Goldman-Sachs and see if they will write you some insurance on what you want protected.

Sure, if I were to invest, what for me are big bucks, in raising a flock of chickens, maybe I'd get some insurance against any disease, e.g., bird flu, attacks by rabid foxes, wolves, raccoons, fire, flood, tornado, etc. destroying the flock.

Sure, once I did some Monte Carlo work essentially to evaluate the odds of some survivors in a really complex, goofy situation -- with those odds, could have found premiums for writing insurance policies. The issue was, if there were global nuclear war limited to sea, how long would the US SSBN fleet survive? For input, I used some encounter rate probability derivations Koopman did as part of ocean search in WWII. Then I used some detection radii of Red-Blue pairs and battle result probabilities (one dies, the other dies, both die, neither dies). My work passed technical review by a world-class probability professor. My summary results were a graph over time of the expected number of SSBNs surviving. Also the Monte Carlo result could have also reported the probabilities of other aspects of the scenario, e.g., the probability that at least 10 survived for 30 days, enough to write an insurance policy!

The professor tried to object: He said how could I hope to "fathom" the huge state space? I responded, ..., using the strong law of large numbers ..., and he agreed with me. So, sometimes can analyze situations where some of the possibilities, actually nearly all, are never encountered or sampled. So, that would beat running LP many times one at a time on many disasters could describe.

Lesson: Sometimes it's possible to do Monte Carlo under some seemingly wildly complicated situations.

Net, I would advise pricing some insurance instead of some DIY (do it yourself) with either LP or RL.

More broadly and by analogy, a hammer and a screwdriver are both good tools, but no sense in asking which is better. Maybe the same for LP and RL. And for one more tool, insurance policies.