The Good. The Bad. The Asinine.

I have an idea… Part 2

Regular readers of this blog will have recently attended their first actuarial lecture. Some of you may have learned something (see Note 1 below if you didn’t), but, at the very least, you all realised that actuarial lectures are awesome, and you have a strong desire to attend more.

Well, if there’s one thing I hate, it’s disappointing my regular reader. So here goes… Lecture 2 starts now.

Suppose you live on the planet Fluff. As it turns out, Fluff is very similar to Earth, and Fluffers, as Fluff’s inhabitants are known, are very similar to us humans. They breathe oxygen, eat chicken parmigiana, and mostly work in the porn industry. There are a few differences, however. For starters, they are lucky enough to have 11 Kardashians. They also have a different god to us, and life is a little different under the Fluffer god:

  1. Instead of being called ‘God’, their god insists on being called by his name, which is John.
  2. John’s pretty nice, and spends all his time preventing heart attacks, lung cancer, and lightning strikes, such that no one ever dies before the age of 80.
  3. As soon as someone turns 80, John sends a taxi to bring them to heaven. The really devout people get a stretch Hummer. Fckwits get a Camry.

As you might have guessed, Fluffers don’t really have a huge need for life insurance, given that everyone knows exactly when they’re going to die. And on the few occasions someone is actually stupid enough to take out a policy, the actuaries have it pretty easy – “Oh, you’re 30, and want $50,000 when you die? That’ll be 50 thousand divided by 50, please, whatever that is.” (Note 2)

One day John decides that life on Fluff is a little boring. So he determines that he’s not going to stop people having heart attacks any more. And while everyone was a little shocked when Betsy dropped dead at her 30th birthday party, (a) no one was surprised to see a Camry pull up out the front, and (b) people soon realised that life insurance might be a good idea.

That was a crazy day over at Mutual Fluffing, I can tell you. Thousands of people ringing up for life insurance, management in a panic, the call centre, whose name was Jarad, didn’t know how to work the phone… only the actuaries remained cool, which was the first time actuaries had been described with that particular adjective.

“Don’t panic!” they said. “We’ll just see how many people die of heart attacks over the next year. If we divide that by the total population, that will give us a rough idea of the probability of dying of a heart attack. Then if someone wants to insure their life for $100,000, we can just multiply that amount by the probability of dying, and that will give us the expected value of the policy. And that’s the premium we should charge.”

And John saw the way the actuaries were calculating premiums, and saw that it was good.

Things carried on in this way for a while, until one day the actuaries noticed something unusual. Looking at the characteristics of all the people dying, they noticed that men tended to have a lot more heart attacks than women – around twice as many, in fact. This created a few problems:

  1. While they were collecting the right amount of premium in total, if men were having twice as many heart attacks as women, they were also paying half as many premiums.
  2. If men were paying half the number of premiums, then, in order to keep everything fair, each premium should be twice as much. That is, men were getting their insurance at half price, and the women were making up the shortfall.
  3. If insurance was relatively cheap for men, and relatively expensive for women, women would soon stop buying it.

The actuaries realised that if they wanted to continue selling life insurance, they’d have to charge different premiums for men and women. So that’s what they did.

And John saw the way the actuaries were calculating premiums, and saw that it was good.

Eventually, however, John realised that he actually didn’t like Fluffers that much after all, so he decided to stop preventing lung cancer, too. Once again there was a mild panic at Mutual Fluffing, and, once again, the actuaries had the answer.

“You know what we’ve noticed about all these people dying from lung cancer? They’re all smokers. If we want premiums to remain fair, we need to charge smokers a higher premium than non-smokers.” So that’s what they did.

And John saw the way the actuaries were calculating premiums, and saw that it was good.

Ultimately, however, John accepted that he actually wasn’t very nice after all. In fact, he was a bit of a wanker, and couldn’t be bothered stopping anyone dying. And if you paid attention earlier, you’ve by now realised what that meant – people started getting zapped by lightning.

“Actuaries, actuaries! What do we do now?!” pleaded the managers at Mutual Fluffing.

“Hmm… Well, lightning doesn’t discriminate by sex or age. And people don’t choose to get struck by lightning. It’s just a completely random event that no one has any control over. So we should just increase everyone’s premium by the same amount.”

And John saw the way the actuaries were calculating premiums, and realised something important – something that would come in handy if he ever stumbled across an awesome blog on Earth. Each premium was comprised of three components:

  1. Things that increased the risk, but over which the life insured had no control (e.g. sex);
  2. Things that increased the risk, and which the life insured could control (e.g. smoking);
  3. Risks that no one could predict, or control.

Or, if he needed a catchy phrase that was easy to remember – “People are things, people do things, and shit happens”.

He also realised that it was probably time that the Son of John left home. In a Camry.

But that’s another story.


  1. You learned that an expected value is an average outcome for a wide range of probabilities and consequences, and we should be indifferent between two sets of events that have the same expected value. Or you would have if you’d paid attention. (back)
  2. Premium payable annually in advance, and assumes John’s monetary policy is so effective that it keeps interest rates at 0% at all times. Also assumes that John’s bans on expenses, regulatory capital, profit, and coveting your neighbour’s ox remain in place. (back)

Block & Roll #3 – Dishing it Out

Block & Roll 3

Stop the Boats

As a political debate, the discussion around the arrival of asylum seekers by boat is roughly comparable to a cream pie fight between blind, deaf and mentally retarded circus clowns. In a pit full of jelly. So much venomous half-truth and blatant un-truth is flung gauchely, clumsily and senselessly round as soon as the topic is raised that any possibility of sane discussion is extinguished in a flurry of bleeding hearts and sub-human, barely veiled racism.

I however, think that there are powerful reasons for stopping the boats. None of them, however, have very much to do with the reasons I see being bruited about in the popular press.

Let us first deal with the xenophobes, who believe that our culture and way of life is threatened by the unchecked arrival of asylum seekers.

Dear Xenophobes,

Less than 2% of all arrivals in this country are by boat. If you think that this tiny drop in the ocean is likely to extinguish your culture and way of life, you must have a population comparable to that of a pristine, Amazonian Rainforest Tribe.

As we know very well that this is not the case, please drop the pretence immediately and just admit that you’re racists. Once this is done, we can shut you the fuck up and put you back in your holes, as is right and proper.

Then, of course, there are the people that argue that taking asylum seekers is ruinous to the economy. Firstly, leaving aside the fact that the portion arriving by boat is actually negligible, I would like to point out that the entire refugee spend for the last quarter of 2012 was 4% of the welfare budget. That is, 4% of the 20% of public spending that was allocated to welfare. Now, I’m no mathematician, but I think that comes to 0.8% of the budget. I am open to correction on the exact figure, but stick staunchly to the point that it is diddly squat of fuck all.

I was able to find this out because it is a matter of public record and I know how to use Google. So why do these people remain in a constant state of fiscal ferment? Well, I’d suggest that it is because these are the same sort of people who will declaim virulently about the size of the budget deficit without being able to explain what a budget deficit actually is.

So, Mr It’s Too Expensive, you can shut the fuck up, too.

Then there are my favourites: Those who say that they welcome immigrants, but are not willing to welcome illegal immigrants. For these people, my instructions are as follows:

Directly after school you are to go to your rooms, take a clean sheet of paper and write, in your best cursive:


one hundred times.

As a supplementary step, you may also wish to discover the definitions of the following terms:

Asylum Seeker


Illegal Immigrant

Until this is done, please, for the love of sanity, shut the fuck up.

So, none of these reasons stand up as rational motivation to stop the boats. Some of these reasons, in fact, are being dealt a lavish courtesy by being referred to as ‘reasons’. So why, then, should we stop the boats?

Two reasons, and two reasons only.

  1. Coming to Australia by boat in wooden craft with shitty engines and less than half a foot of freeboard is dangerous. Not for us, but for the asylum seekers. Ever since this subject became a press circus, confirmed losses of asylum seeker boats have been bruited all over the news. I, however, have sat there on the line, listening to MARPAT reporting boats that never materialised. Nobody else gave a shit then, but then I guess it wasn’t an election year.
  2. The practice of coming to Australia on these craft funds an illegal and morally reprehensible industry. People smugglers, in general, are the scum of the Earth, and we should try to put a stop to anything that puts money in their pockets.

There. It’s that simple. So, xenophobes and amateur economists – you are supporting a policy position that has its only logical bases in concern for foreigners, and in the desire to choke an industry.

All that remains now is to deliver my message to those people who made of this issue a banner with the strange device ‘stop the boats’, behind which political troglodytes, racists and various other bigots could loudly rally behind.

Dear Sensationalist Fucktards,

I am so angry with you that I would not piss on you if you were on fire. If you arrived at my house on a boat seeking asylum, I would have you towed back.



I have an idea…

And, even better than that, it’s an actuarial idea. Now I know what you’re thinking. Actuaries have the most amazing spreadsheets, the funniest jokes, and the best estimates (that’s a funny joke right there, trust me), but they tend not to have that many new ideas.

Ah, but we do.

For example, did you know that it was an actuary that invented the telephone? That’s right, Herbert J Smigglepants invented it over lunch one day in 1822, because he was sick of talking to people face to face. History gives all the credit to Alexander Graham Bell, of course, but history is full of lies, isn’t it. Like the moon landing.

These days actuaries are still having ideas. Although actuarial techniques were developed primarily in the field of insurance, today those techniques are applied to a whole range of industries and issues. From managing the distribution of electricity, to climate change and weather forecasting, it often helps to think about things actuarially.

And that’s what I’m going to do.

Before we get to the actual idea, however, I have to explain a few concepts. So prepare yourself – you’re about to attend your first actuarial lecture. If it’s anything like my first actuarial lecture, you’ll trip on the way in, ask a stupid question, and then fall asleep. Yours won’t be quite that fun, but pretty close.

All actuarial science is built on three distinct but related concepts. The first two are chance and consequence. That is, “What is the probability that a particular event will occur?” and “What are the consequences if it does occur?”. Throw in an allowance for the time-value of money, and boom, that’s the essence of anything actuarial. Normally the goal is to estimate these probabilities and consequences, and use them to work out the expected cost of a particular set of events, over a given period of time. This cost is called the “expected value”, and is essentially the distillation of a wide range of possible scenarios into a single, average outcome.

As with most new concepts, it helps to look at an example. Suppose you find yourself in the world’s most boring casino, a not-for-profit Mormon church hall with beige corduroy couches that only serves light beer. In this casino, there is only one game you can play, called Satan’s Evil Coin Toss Game. A man in a Satan suit flips a coin, and if you guess right, you win a dollar, which you can then use to buy some magic underpants in the gift shop. The question is, how much should the casino charge people to play, if they want to break even in the long run?

As it turns out, we can work this out by calculating the average outcome, or expected value. In this example, it’s calculated as:

Expected Value = 50% x $1 + 50% x $0 = $0.50

That is, if the casino charges everyone 50 cents to play Satan’s Evil Coin Toss Game, they can expect to break even in the long run.

Easy, right?

Where expected values really come in handy is comparing two different sets of events, with two ranges of possible outcomes. Once again, it helps to look at an example. Suppose you go to the slightly more exciting casino up the road. This one has full strength beer, leather couches, and two games to choose from. In the first game, you have to pick a number between 1 and 1,000, and if you guess correctly, you win $1,000,000. In the second game, they just give you $1,000. If both games are free to play, which game should you choose if you’re being completely rational?

At this point, you’re probably thinking one of three things:

  1. Oh man, a 1 in a 1,000 chance at a million, sign me up!
  2. A guaranteed $1,000? Lock it in, Eddie.
  3. Why the fuck am I still reading this? The Bachelorette is on.

The answer, however, is “either”. That is, a rational person should be completely indifferent between the two games. That may sound odd, but don’t be alarmed, it just means you’re reckless. Or boring. Or irrational.

The answer lies in the expected values, which, as you’ve now probably guessed, are the same for each game:

Game 1
Expected Value = 0.1% x $1,000,000 + 99.9% x $0 = $1,000

Game 2
Expected Value = 100% x $1,000 = $1,000

As I said, all of this is just groundwork for my grand, actuarial idea. I still need to explain a few more things, which I will do over the next few posts, but the main thing to remember from this post is that:

  1. An expected value is essentially an average outcome for a wide range of probabilities and consequences; and
  2. We should be indifferent between two sets of events that have the same expected value.

OK, I think I’ve bored you enough for now. Who wants to play Satan’s Evil Coin Toss Game? $1 a throw.