wouldn't it be worth contracting with actuaries to make important personal decisions?
Actuarial science is based on statistics and so is most useful when dealing with large populations.
Given that the probability distributions of these events are quite wide, and that personal tolerances of adverse outcomes, as well as the levels of appreciation for favorable outcomes also vary quite widely, I would expect the standard actuarial data to be of limited utility for personal decision making.
Check out The Longevity Project, an 80 year study of how various factors affected quality of life and longevity for 1500 people.
There's a blog, too.
We do sometimes use actuarial data here, like the Social Security Administration's tables, but I'm not sure how useful it is outside of very reliable data like mortality risks. All it is is a mass of correlations, and the most useful masses, lifestyle choices, may be commercially held.
That said, I could well be too pessimistic about the values.
Seeing as insurers have a commercial interest in their data being correct, the data they use should be of very high quality. Thus seeing what insights are really robust, like wearing a seatbelt is reducing the rate of death, should be useful.
I am an (almost qualified) actuary, working for a life insurance company.
I would love it if I had data of a very high quality. However, most insurance companies can't use population statistics because of differences with underwriting standards (we don't cover the very bad risks), target markets (we advertise in the Daily Slum, so only cover low socioeconomic classes, for example), and claim definitions (what is a disease in the population might not be a claim for the insurance company). So we use our own experience to modify the population stats. Very large companies might use entirely their own data.
Generally, there is not enough of it to be sure that it's totally credible, especially when it comes to fine differences such as how much you smoke or drink. And that's ignoring problems like non-disclosure. Age and Sex are easier, but there's not much you can do about changing those, so it doesn't help with the question at hand.
Of course, for some types of insurance, such as compulsory car insurance, there is more data to work with - I've never worked in general insurance, so I can't comment on that.
People in business are under some pressure to be accurate, but not that much (they don't actually have to be right, they just need to be more accurate than some of their competitors), and perhaps less so in businesses with high barriers to entry.
I'm trying to fact-check the rhetoric about older generations consuming Government resources due to demographic transitions and the aging population. Intergenerational equity concerns me as a young person. I've found a primer that models the general situation here and an article critising assumptions that people may make after introductory readings on the topic here. Does anyone have anything else to add? My working conclusion is that I have nothing to worry about. However, I am assuming that things like the 'trust fund' to pay for baby boomer retirement applies to Australia as much as in the US, and that the second article is more true than the first, since they are contradictory.
To echo what gwern said, AFAIK actuaries are only useful insofar as there's a large data set for them to work with. Often when evaluating evidence, you need to evaluate the merits of a bunch of studies and aggregate them along with any non-scientific evidence (e.g. not published in peer reviewed journals, not statistical, etc.) you may have, and this isn't what actuaries do. What you need is a statistician with specialist knowledge in the field(s) you're interested in. Or a specialist with some knowledge of statistics. These people are often called scientists.
This is not exactly an answer to your questions, but I do think actuarial science provides tools that many folks in this community should find useful, and for that reason it might be a discipline worth learning. Textbooks are usually the quickest and best way to learn new material; for actuarial science, a good choice is Fundamentals of Actuarial Mathematics, by David Promislow (free pdf)
I do think actuarial science provides tools that many folks in this community should find useful
Could you say more? Examples? I've downloaded your suggested textbook (presumably you've worked through it?), and read through the table of contents; while some of the overlap with economics and regular statistics looks mildly interesting, a lot of it looks heavily specialized to, well, insurance stuff like different premium payment schemes which I have no idea how I would ever find useful outside life insurance.
This old interview with Michael Vassar seems relevant (bold in answer not in original):
What kind of jobs you did when you were younger and what is the important things you learned about it?
I worked in labs doing biochemistry, microbiology, plant genetics, and soft lithography. Also waiting tables, teaching in inner city schools in the US, Peace Corps in Kazakhstan, and actuarial work. Currently a music licensing start-up, which has been the most educational of all, but all have taught me more than I have time to share.
It occurred to me this morning that, if it's actually valuable, generating true beliefs about the world must be someone's comparative advantage. If truth is instrumentally important, important people must be finding ways to pay to access it. I can think of several examples of this, but the one that caught my attention was actuarial science.
I know next to nothing about what actuaries actually do, but Wikipedia says:
"Actuaries mathematically evaluate the likelihood of events and quantify the contingent outcomes in order to minimize losses, both emotional and financial, associated with uncertain undesirable events."
Why, that sounds right up our alley.
So what I'm wondering is: for those who can afford it, wouldn't it be worth contracting with actuaries to make important personal decisions? Not merely with regards to business, but everything else as well? My preliminary ideas include: