A Post-Work Society

China had every technology necessary for an industrial revolution hundreds of years before the revolution actually happened in Europe. Why did it not start in China?

Industrialization is about efficiency – it’s about doing as much work as possible, using as few people as possible. But, people were China’s most abundant resource. They had no reason to conserve labor, to do work more efficiently, and so they did not industrialize.

To our modern sensibilities, this argument should make perfect sense. We are in the midst of a technological revolution of our own. Manufacturing jobs for humans are going away as factories realize a new tier of automation. Deliveries have just begun being made by drone. Several companies are developing self-driving cars, with surprising levels of success. Agents – who sell things like real estate and insurance – are being replaced by software.

As I write this post, politicians are scrambling to keep manufacturing jobs in this country. But, that scramble is futile. Those jobs aren’t leaving the country so much as they are leaving the world, never to return. Unlike China of the past, who didn’t have any reason to industrialize, modern companies have a strong motivation: profit. Automated manufacturing is far more efficient, to the extent that it is cheaper to manufacture goods in the United States using machines than it is to have people in third world companies do the work for third world wages.

Manufacturing may be moving back to the United States, but that doesn’t mean we are going to get back our precious manufacturing jobs. I call them precious because modern society places enormous importance on jobs. We judge the health of our economy on unemployment statistics. A person who cannot find a job is seen as tragic. A person who chooses not to aggressively seek a job is ostracized. There is a mantra throughout our culture, whether stated aloud or not: A job for every person, and every person at his job.

Of course we are not the first to face this problem. Nor were the people of every nation which has ever industrialized. There was a time when we – humans – knew how to farm, but not very well. We were an agrarian society, so called because almost every worker worked in agriculture. The better we got at farming, the fewer farmers we needed, and the more people didn’t have anything in particular to do.

What did the farmers forced into early retirement do? They invented new jobs. They found new ways to be productive, to increase our standard of living. Fewer farmers meant more craftsmen, stone masons, architects, entertainers, and makers of soap. We moved beyond the agrarian lifestyle.

And then we moved beyond the preindustrial lifestyle. Industrialization of agriculture meant even fewer farmers and more mathematicians, fewer craftsmen and more scientists, fewer teamsters and more operators of trains. More people doing the things that make our current way of life possible. Tesla invented radio, Fleming discovered penicillin, Tolkien wrote Lord of the Rings, Armstrong walked on the moon, and Elon Musk created the Tesla, all for the exact same reason: They were not too busy farming potatoes.

So don’t panic! Chanters of the mantra would have you believe the employment problems we are facing down are signs of a catastrophe. In time, we will come to think of them as nothing more than the growing pains of a new, presumably better future coming into being.

We do have some logistical questions to answer in the mean time, though. What should those displaced workers do while they wait for the future to get here? How can they be expected to put food over their heads and roofs in their mouths?

One possible solution goes all the way back to one of the founders of the United States – Thomas Paine. He wrote about the issue when it arose the first time, when the hot new science was Somewhat Better Farming.

In Agrarian Justice, he argues for land owners to pay a tax that would pay to support non land owners. He justified the tax as compensation for the use of the land. Before we cultivated the land, a person could support himself off of the resources he found there – resources which belonged to no one. But now that the land was cultivated, the only resources one might find there were someone else’s crops and livestock. The land owners were therefore responsible for ensuring the people could still survive, which Paine suggested they do monetarily.

The idea was never enacted, as best as I can tell, but the idea has persisted, and has now transformed into more modern concept: basic income. Although Paine’s justification doesn’t seem to apply, the concept is the same. If automation is set to put people out of work, why don’t we just pay those people anyway?

The first argument against this idea has a Red Scare flavor. Free money sounds like socialism. Free market capitalistic theory would say this basic income creates a disincentive to work. Why work when the government pays you not to?

First, because basic income is not meant to provide a lavish lifestyle. It’s a sustenance level of income. It’s enough to afford food and shelter, enough for a person to be reasonably assured not to starve or freeze to death.

Second, because basic income is not conditional. Work does not disqualify a worker, and so any income earned over basic income is simply supplemental. There is still plenty of motivation for people to work.

We may not have basic income yet, but we do have plenty examples of people with enough money they don’t have to work. What kind of lives do they lead when they don’t have a financial need to punch a clock?

Warren Buffet does not need to work in order to make the payments on his modest one-bedroom apartment or to be able to afford groceries next Friday. But he goes to work anyway. Robert Downey, Jr. made $80 million in 2015 and did not retire. Hillary Clinton and Donald Trump – say what you will about either of them – they are both wealthy enough they never need to work again, and yet in 2016 they competed to see which of them could get one of the most stressful jobs in the world.

When people say, they need incentive to work, which they are those people talking about?

Jobs in manufacturing, logistics, sales – even agriculture, where this all began – those are working class jobs. So when people reference this need for incentive, what they’re saying is working class people need to be properly motivated. Because, perhaps, they lack something better people possess.

Educated people, they don’t need to be motivated. No one questions why a partner at a law firm keeps working after making his or her first few millions. Or why entrepreneurs don’t sell off their companies as soon as they’ve made enough to comfortably retire.

Wealthy people, they also don’t need to be motivated. It would be easy to assume Julia Louis-Dreyfus got rich along with the rest of the cast of Seinfeld. In fact, she was a billionaire before the show began. She was born a billionaire. When people learn this piece of trivia, the typical response is to shrug. Rarely does anyone ask, Well then why does she bother acting?

Creative people somehow also don’t need to be motivated. Stephen King doesn’t have Julia Louis-Dreyfus money, but he is sitting pretty comfortable on his $400 million. And he continues to write, because he’s a writer and that’s what writers do. It’s just the kind of person he is.

But before Stephen King was the writer of The Shawshank Redemption – a story about a man incarcerated and forced to work, though he committed no crime – he worked in a textile mill. Dismal work for dismal pay. What kind of person was he then? Was he always the kind of person who would grow up to be Stephen King the Writer? Or did he only become that when he learned, while sitting in his kitchen trying to think of a way to afford penicillin for his daughter, that he had sold Carrie?

The hero of The Shawshank Redemption, like the writer, eventually found a way out of what seemed to be a hopeless situation, but both of them were rare exceptions. How many great thinkers – artists and scientists and poets and leaders – how many do we just not know about because they didn’t happen to beat the odds?

If working class people need to be spurred to work, it isn’t because they are a different type of people. Could it be that they need more motivation because they do a different class of work – the kind of work no one would do if they had a choice? We occasionally see an eccentric billionaire decide to go into acting or politics, but we don’t ever see them decide to lay asphalt or assemble machinery.

We have a sort myth of a working class hero – a man made hard by hard work. We might imagine a man laying brick for 30 years to send his daughter to college to be a lawyer or an astrophysicist. There is something romantic in the idea of a person enduring hardship in order to reach a better life for himself or his offspring.

Even in that myth, there is something aspirational. It acknowledges that there is a better possible life out there. We never bother to imagine a man laying brick for 30 years so that someday, maybe his grandson’s grandson’s grandson can go to brick laying school.

That better life, the working class hero has worked for – maybe that is what we are on the cusp of achieving. Maybe it isn’t just him, or his children, or theirs, who can now have that life. Maybe all of us can.


The Sleeping Beauty Problem

A friend of mine recently brought a logic problem to my attention. It’s called the Sleeping Beauty Problem, and it’s considered something of a paradox. From the Wikipedia page describing it <Sleeping Beauty Problem>:

Sleeping Beauty volunteers to undergo the following experiment and is told all of the following details: On Sunday she will be put to sleep. Once or twice, during the experiment, Beauty will be awakened, interviewed, and put back to sleep with an amnesia-inducing drug that makes her forget that awakening. A fair coin will be tossed to determine which experimental procedure to undertake: if the coin comes up heads, Beauty will be awakened and interviewed on Monday only. If the coin comes up tails, she will be awakened and interviewed on Monday and Tuesday. In either case, she will be awakened on Wednesday without interview and the experiment ends.

Any time Sleeping Beauty is awakened and interviewed she will not be able to tell which day it is or whether she has been awakened before. During the interview Beauty is asked: “What is your credence now for the proposition that the coin landed heads?”

First of all, you might wonder what credence is. It’s just probability. In this example, it is how sure Sleeping Beauty is that the coin landed heads. Of course she could be wrong. She could be 100% sure the coin landed on heads. But for this problem, we assume that she is perfectly logical, and that her credence is exactly the same as the correct probability. In short, credence is different from probability in that the people who coined the first term were apparently unaware that the second term already meant exactly the same thing.

But whatever.

So what’s the answer? Apparently there are two, which the same Wikipedia page names the Thirder Position and the Halfer Position. (There is a third position given but it’s wrong and even the guy who argued it knew it was wrong.) These arguments are paraphrased from the Wikipedia page:

The Thirder Position – If the coin comes up tails, there will be two interviews which SB could not distinguish between, so the probability that is both tails and Monday, P(tails and Monday), must be equal to the probability that it is both tails and Tuesday, P(tails and Tuesday). Similarly, if SB assumed the day of the interview was Monday, she would have no reason to believe heads or tails was more likely than the other. The procedure, she notes, does not require the coin to actually be thrown until Tuesday. So, Monday, the probability of the result being heads is equal to the probability of it being tails. Therefore P(tails and Monday) = P(tails and Tuesday).

P(tails and Monday) = P(tails and Tuesday) = P(heads and Monday)

As these are the only three possibilities, they add to 1, and so are each equal to 1/3. Only P(heads and Monday) has the coin flip landing heads, and so P(heads) equals 1/3.

The Halfer Position – The probability of a fair coin landing heads is 1/2, by definition. SB is awakened and interviewed. She knew at the outset of the experiment that this would happen, so she has gained no new information, so her credence cannot change. It must still be 1/2.

Let’s consider the Halfer Position. If we suppose P(heads) = 1/2 as it argues, we can deduce some odd findings. If P(heads) = 1/2, then P(heads and Monday) = 1/2, because if the coin lands on heads, there is no interview Tuesday.

We also find P(tails and Monday) = P(tails and Tuesday) for the exact same reason as it was true in the Thirder Position. So, they each equal 1/4

The term P(heads|Monday) means the probability that the coin flip landed on heads given that the day is Monday. It’s called conditional probability. It means, in this case, if SB somehow learns the day is Monday, what is her credence then that the coin landed heads?

We can calculate P(heads|Monday) and several other values as follows. By the definition of conditional probability:

P(heads and Monday) = P(heads|Monday)*P(Monday)
P(heads and Monday) = P(Monday|heads)*P(heads)
P(heads and Tuesday) = P(heads|Tuesday)*P(Tuesday)
P(heads and Tuesday) = P(Tuesday|heads)*P(heads)
P(tails and Tuesday) = P(tails|Tuesday)*P(Tuesday)
P(tails and Tuesday) = P(Tuesday|tails)*P(tails)

By solving these six equations simultaneously – which turns out to be a simpler algebra problem than it appears – We find the following:

P(heads|Monday) = 2/3.

This means if SB somehow learned during an interview that the day were Monday, she would have to answer that her credence that the coin landed on heads was 2/3. But, what would she have learned that would have changed her credence?

The Halfer Position is based on the assertion that waking and being interviewed doesn’t give SB any new information. After all, she knew she would be interviewed regardless of the coin flip. Being interviewed tells her only that there is an interview conducted, which is not new information, and therefore she cannot change her credence. That’s the argument.

However, the same argument can be applied to her being interviewed and knowing the day is Monday. This scenario would tell her only that there is an interview conducted on Monday. Again, she knew this would happen from the outset, so learning it here doesn’t give her any new information, and therefore she cannot change her credence. But she must, because for P(heads) to equal 1/2, P(heads|Monday) must equal 2/3.

Compare this experiment to a similar one, in which there is one interview performed on Monday if the coin flip lands on heads, but there are three interviews, performed Monday, Tuesday, and Wednesday if the flip lands on tails. Either way, she is released on Thursday.

In this experiment, SB’s credence that the coin flip is heads must be 3/4 if she somehow learned the day were Monday. Or, P(heads|Monday) = 3/4. The only difference between the two experiments, however is the number of interviews that have yet to be performed at the time this interview takes place.

What if she didn’t know which of these two experiments she was in? Consider a third experiment. Heads, one interview on Monday. Tails, one interview on Monday, followed by a random number of interviews on subsequent days.

In this experiment, the following chain of events might occur. SB is put to sleep. A fair coin is flipped. She is awakened, somehow learns that the day is Monday, and is interviewed. When asked for her credence, she can only reasonably answer that she does not know, that she would need to know how many interviews the experimenters are planning to perform in subsequent days.

Strangely, if the experiment isn’t disrupted, if she is awakened on Monday but did not know it was Monday, she could confidently answer that her credence was 1/2. If she then learned that it was Monday, she would have to quickly change her answer. She previously thought it was 1/2, but upon learning that it was Monday, she no longer has enough information to answer the question.

So the Halfer Position is wrong. But why is it wrong?

The Halfer Position asserts SB does not learn anything from being awakened and interviewed. Is that true?

Maybe. But her credence could change for another reason: the amnesia drug. Her change of credence stems not from something she knows but from something she doesn’t know.

Notice how trivial the interview becomes if there is a calendar in the room. Monday, she correctly answers 1/2. Tuesday, if there is an interview, she correctly answers 0.

Without the calendar, she knows what the probability is in the case that the day is Monday, and she knows what the probability is in the case that the day is Tuesday. What she does not know is which case she is in.

She doesn’t know. That is true. However – the point that the Halfer Position neglects – she does know the probability that the day is Monday. And she can use that information to adjust her credence.

It isn’t trivial to calculate, as P(Monday) is known only in terms of P(heads). There are numerous correct approaches to take to solve the problem this way, but the most straightforward and elegant is the one presented in the Thirder Position, above.

Here are a few of the key results:

P(heads) = 1/3
P(heads|Monday) = 1/2
P(heads|Tuesday) = 0

If it is Monday, the odds are 1/2, as expected. If it is Tuesday, the flip is guaranteed to be tails, also as expected.

Some have called this problem a paradox. A paradox occurs when the intuitive answer to a question is different from the correct one.

Strike the notion from your mind. Do not believe there is any such thing. If your intuition differs from reality, your intuition is deficient. If you allow ideas to persist in your mind under the heading of Paradoxes, you are allowing this deficiency to continue to be a part of your logical process.

Retrain yourself to intuitively see the world as it actually is.

In this case, a tool to keep in the toolbox is this: Information helps you make better judgments. Right?

Notice how in the Thirder Position, SB answers that her credence is 2/3, which is not the precise probability in either case. But upon learning the day, she can adjust her answer to either 1/2 or 0 (depending on what day she learns it is), which is the correct probability in each case.

However, in the Halfer Position, SB answers 1/2, but when she learns the day is Monday, her credence gets worse, moving to 2/3.

And remember the Halfer Position applied to the experiment with a random number of interviews. If SB doesn’t know the day, she answers 1/2, but upon learning that the day is Monday, no longer knows enough to answer the question.

Of course, we could contrive examples where a subject answers some question correctly just by dumb luck, and additional information causes the answer to deviate, but generally speaking, knowledge is helpful, and any case where the opposite appears true should make you suspicious.