Today would be my father’s birthday.

Apr 15 JDN 2458224

When this post goes live, it will be April 15, 2018. My father was born April 15, 1954 and died August 31, 2017, so this is the first time we will be celebrating his birthday without him.

I’m not sure that grief ever really goes away. The shock of the unexpected death fades eventually, and at last you can accept that this has really happened and make it a part of your life. But the sum total of all missed opportunities for life events you could have had together only continues to increase.

There are many cliches about this sort of thing: “Death is a part of life.” “Everything happens for a reason.” It’s all making excuses for the dragon. If we could find a way to make people stop dying, we ought to do it. The other consequences are things we could figure out later.

But, alas, we can’t, at least not in general. We have managed to cure or vaccinate against a wide variety of diseases, and as a result people do, on average, live longer than ever before in human history. But none of us live “on average”—and sometimes you get a very unlucky draw.

Yet somehow, we do learn to go on. I’m not sure how. I guess it’s a kind of desensitization: Right after my father’s death, any reminder of him was painful. But over time, that pain began to lessen. Each new reminder hurts a little less than the last, until eventually the pain is mild enough that it can mostly be ignored. It never really goes away, I think; but eventually it is below your just-noticeable-difference.

I had hoped to do more with this post. I had hoped that reflecting on the grief I’ve felt for the last several months would allow me to find some greater insight that I could share. Instead, I find myself re-writing the same sentences over and over again, trying in vain to express something that might help me, or help someone else who is going through similar grief. I keep looking for ways to distract myself, other things to think about—anything but this. Maybe there are no simple insights, no way for words to shorten the process that everyone must go through.

The extreme efficiency of environmental regulation—and the extreme inefficiency of war

Apr 8 JDN 2458217

Insofar as there has been any coherent policy strategy for the Trump administration, it has largely involved three things:

  1. Increase investment in military, incarceration, and immigration enforcement
  2. Redistribute wealth from the poor and middle class to the rich
  3. Remove regulations that affect business, particularly environmental regulations

The human cost of such a policy strategy is difficult to overstate. Literally millions of people will die around the world if such policies continue. This is almost the exact opposite of what our government should be doing.

This is because military is one of the most wasteful and destructive forms of government investment, while environmental regulation is one of the most efficient and beneficial. The magnitude of these differences is staggering.

First of all, it is not clear that the majority of US military spending provides any marginal benefit. It could quite literally be zero. The US spends more on military than the next ten countries combined.

I think it’s quite reasonable to say that the additional defense benefit becomes negligible once you exceed the sum of spending from all plausible enemies. China, Russia, and Saudi Arabia together add up to about $350 billion per year. Current US spending is $610 billion per year. (And this calculation, by the way, requires them all to band together, while simultaneously all our NATO allies completely abandon us.) That means we could probably cut $260 billion per year without losing anything.

What about the remaining $350 billion? I could be extremely generous here, and assume that nuclear weapons, alliances, economic ties, and diplomacy all have absolutely no effect, so that without our military spending we would be invaded and immediately lose, and that if we did lose a war with China or Russia it would be utterly catastrophic and result in the deaths of 10% of the US population. Since in this hypothetical scenario we are only preventing the war by the barest margin, each year of spending only adds 1 year to the lives of the war’s potential victims. That means we are paying some $350 billion per year to add 1 year to the lives of 32 million people. That is a cost of about $11,000 per QALY. If it really is saving us from being invaded, that doesn’t sound all that unreasonable. And indeed, I don’t favor eliminating all military spending.

Of course, the marginal benefit of additional spending is still negligible—and UN peacekeeping is about twice as cost-effective as US military action, even if we had to foot the entire bill ourselves.

Alternatively, I could consider only the actual, documented results of our recent military action, which has resulted in over 280,000 deaths in Iraq and 110,000 in Afghanistan, all for little or no apparent gain. Life expectancy in these countries is about 70 in Iraq and 60 in Afghanistan. Quality of life there is pretty awful, but people are also greatly harmed by war without actually dying in it, so I think a fair conversion factor is about 60 QALY per death. That’s a loss of 23.4 MQALY. The cost of the Iraq War was about $1.1 trillion, while the cost of the Afghanistan War was about a further $1.1 trillion. This means that we paid $94,000 per lost QALY. If this is right, we paid enormous amounts to destroy lives and accomplished nothing at all.

Somewhere in between, we could assume that cutting the military budget greatly would result in the US being harmed in a manner similar to World War 2, which killed about 500,000 Americans. Paying $350 billion per year to gain 500,000 QALY per year is a price of $700,000 per QALY. I think this is about right; we are getting some benefit, but we are spending an enormous amount to get it.

Now let’s compare that to the cost-effectiveness of environmental regulation.

Since 1990, the total cost of implementing the regulations in the Clean Air Act was about $65 billion. That’s over 28 years, so less than $2.5 billion per year. Compare that to the $610 billion per year we spend on the military.

Yet the Clean Air Act saves over 160,000 lives every single year. And these aren’t lives extended one more year as they were in the hypothetical scenario where we are just barely preventing a catastrophic war; most of these people are old, but go on to live another 20 years or more. That means we are gaining 3.2 MQALY for a price of $2.5 billion. This is a price of only $800 per QALY.

From 1970 to 1990, the Clean Air Act cost more to implement: about $520 billion (so, you know, less than one year of military spending). But its estimated benefit was to save over 180,000 lives per year, and its estimated economic benefit was $22 trillion.

Look at those figures again, please. Even under very pessimistic assumptions where we would be on the verge of war if not for our enormous spending, we’re spending at least $11,000 and probably more like $700,000 on the military for each QALY gained. But environmental regulation only costs us about $800 per QALY. That’s a factor of at least 14 and more likely 1000. Environmental regulation is probably about one thousand times as cost-effective as military spending.

And I haven’t even included the fact that there is a direct substitution here: Climate change is predicted to trigger thousands if not millions of deaths due to military conflict. Even if national security were literally the only thing we cared about, it would probably still be more cost-effective to invest in carbon emission reduction rather than building yet another aircraft carrier. And if, like me, you think that a child who dies from asthma is just as important as one who gets bombed by China, then the cost-benefit analysis is absolutely overwhelming; every $60,000 spent on war instead of environmental protection is a statistical murder.

This is not even particularly controversial among economists. There is disagreement about specific environmental regulations, but the general benefits of fighting climate change and keeping air and water clean are universally acknowledged. There is disagreement about exactly how much military spending is necessary, but you’d be hard-pressed to find an economist who doesn’t think we could cut our military substantially with little or no risk to security.

Reasonableness and public goods games

Apr 1 JDN 2458210

There’s a very common economics experiment called a public goods game, often used to study cooperation and altruistic behavior. I’m actually planning on running a variant of such an experiment for my second-year paper.

The game is quite simple, which is part of why it is used so frequently: You are placed into a group of people (usually about four), and given a little bit of money (say $10). Then you are offered a choice: You can keep the money, or you can donate some of it to a group fund. Money in the group fund will be multiplied by some factor (usually about two) and then redistributed evenly to everyone in the group. So for example if you donate $5, that will become $10, split four ways, so you’ll get back $2.50.

Donating more to the group will benefit everyone else, but at a cost to yourself. The game is usually set up so that the best outcome for everyone is if everyone donates the maximum amount, but the best outcome for you, holding everyone else’s choices constant, is to donate nothing and keep it all.

Yet it is a very robust finding that most people do neither of those things. There’s still a good deal of uncertainty surrounding what motivates people to donate what they do, but certain patterns that have emerged:

  1. Most people donate something, but hardly anyone donates everything.
  2. Increasing the multiplier tends to smoothly increase how much people donate.
  3. The number of people in the group isn’t very important, though very small groups (e.g. 2) behave differently from very large groups (e.g. 50).
  4. Letting people talk to each other tends to increase the rate of donations.
  5. Repetition of the game, or experience from previous games, tends to result in decreasing donation over time.
  6. Economists donate less than other people.

Number 6 is unfortunate, but easy to explain: Indoctrination into game theory and neoclassical economics has taught economists that selfish behavior is efficient and optimal, so they behave selfishly.

Number 3 is also fairly easy to explain: Very small groups allow opportunities for punishment and coordination that don’t exist in large groups. Think about how you would respond when faced with 2 defectors in a group of 4 as opposed to 10 defectors in a group of 50. You could punish the 2 by giving less next round; but punishing the 10 would end up punishing 40 others who had contributed like they were supposed to.

Number 4 is a very interesting finding. Game theory says that communication shouldn’t matter, because there is a unique Nash equilibrium: Donate nothing. All the promises in the world can’t change what is the optimal response in the game. But in fact, human beings don’t like to break their promises, and so when you get a bunch of people together and they all agree to donate, most of them will carry through on that agreement most of the time.

Number 5 is on the frontier of research right now. There are various theoretical accounts for why it might occur, but none of the models proposed so far have much predictive power.

But my focus today will be on findings 1 and 2.

If you’re not familiar with the underlying game theory, finding 2 may seem obvious to you: Well, of course if you increase the payoff for donating, people will donate more! It’s precisely that sense of obviousness which I am going to appeal to in a moment.

In fact, the game theory makes a very sharp prediction: For N players, if the multiplier is less than N, you should always contribute nothing. Only if the multiplier becomes larger than N should you donate—and at that point you should donate everything. The game theory prediction is not a smooth increase; it’s all-or-nothing. The only time game theory predicts intermediate amounts is on the knife-edge at exactly equal to N, where each player would be indifferent between donating and not donating.

But it feels reasonable that increasing the multiplier should increase donation, doesn’t it? It’s a “safer bet” in some sense to donate $1 if the payoff to everyone is $3 and the payoff to yourself is $0.75 than if the payoff to everyone is $1.04 and the payoff to yourself is $0.26. The cost-benefit analysis comes out better: In the former case, you can gain up to $2 if everyone donates, but would only lose $0.25 if you donate alone; but in the latter case, you would only gain $0.04 if everyone donates, and would lose $0.74 if you donate alone.

I think this notion of “reasonableness” is a deep principle that underlies a great deal of human thought. This is something that is sorely lacking from artificial intelligence: The same AI that tells you the precise width of the English Channel to the nearest foot may also tell you that the Earth is 14 feet in diameter, because the former was in its database and the latter wasn’t. Yes, WATSON may have won on Jeopardy, but it (he?) also made a nonsensical response to the Final Jeopardy question.

Human beings like to “sanity-check” our results against prior knowledge, making sure that everything fits together. And, of particular note for public goods games, human beings like to “hedge our bets”; we don’t like to over-commit to a single belief in the face of uncertainty.

I think this is what best explains findings 1 and 2. We don’t donate everything, because that requires committing totally to the belief that contributing is always better. We also don’t donate nothing, because that requires committing totally to the belief that contributing is always worse.

And of course we donate more as the payoffs to donating more increase; that also just seems reasonable. If something is better, you do more of it!

These choices could be modeled formally by assigning some sort of probability distribution over other’s choices, but in a rather unconventional way. We can’t simply assume that other people will randomly choose some decision and then optimize accordingly—that just gives you back the game theory prediction. We have to assume that our behavior and the behavior of others is in some sense correlated; if we decide to donate, we reason that others are more likely to donate as well.

Stated like that, this sounds irrational; some economists have taken to calling it “magical thinking”. Yet, as I always like to point out to such economists: On average, people who do that make more money in the games. Economists playing other economists always make very little money in these games, because they turn on each other immediately. So who is “irrational” now?

Indeed, if you ask people to predict how others will behave in these games, they generally do better than the game theory prediction: They say, correctly, that some people will give nothing, most will give something, and hardly any will give everything. The same “reasonableness” that they use to motivate their own decisions, they also accurately apply to forecasting the decisions of others.

Of course, to say that something is “reasonable” may be ultimately to say that it conforms to our heuristics well. To really have a theory, I need to specify exactly what those heuristics are.

“Don’t put all your eggs in one basket” seems to be one, but it’s probably not the only one that matters; my guess is that there are circumstances in which people would actually choose all-or-nothing, like if we said that the multiplier was 0.5 (so everyone giving to the group would make everyone worse off) or 10 (so that giving to the group makes you and everyone else way better off).

“Higher payoffs are better” is probably one as well, but precisely formulating that is actually surprisingly difficult. Higher payoffs for you? For the group? Conditional on what? Do you hold others’ behavior constant, or assume it is somehow affected by your own choices?

And of course, the theory wouldn’t be much good if it only worked on public goods games (though even that would be a substantial advance at this point). We want a theory that explains a broad class of human behavior; we can start with simple economics experiments, but ultimately we want to extend it to real-world choices.