Author Archives: Arnold Kling

Robin Hanson on epistemology

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Robin Hanson writes,

Just as our distant ancestors were too gullible about their sources of knowledge on the physical world around them, we today are too gullible on how much we can trust the many experts on which we rely. Oh we are quite capable of skepticism about our rivals, such as rival governments and their laws and officials. Or rival professions and their experts. Or rival suppliers within our profession. But without such rivalry, we revert to gullibility, at least regarding “our” prestigious experts who follow proper procedures.

On a recommendation from the redoubtable John Alcorn, I am reading Hugo Mercier’s Not Born Yesterday. Mercier claims that we have evolved not to be gullible. Otherwise, we would be taken advantage of and not survive.

Incidentally, if I tell you that you are not gullible, how gullible do you have to be to believe me? To not believe me?

I think Mercier relies quite a bit on a distinction between cheap talk and actionable beliefs (he terms these “reflective beliefs” and “intuitive beliefs,” respectively, which I find unhelpful). He says that the implausible beliefs that we hold, which make us seem gullible, are in the cheap talk category–we don’t act as if we deeply believe them. When we need to act, we make the effort to sort out truth. Libertarian economics would predict that political choices are based on cheap talk and consumer choices are based on actionable beliefs.

Accounting for stimulus checks

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Scott Sumner writes,

April saw by far the largest increase in personal income ever seen in America. That’s not normal for a month that is likely to end up being the absolute trough of the 2020 depression. And saying it’s “not normal” during a depression is an epic understatement.

In freshman macroeconomics, the letter Y often is used to stand for GDP and for national income, interchangeably. In the national income accounts, they are arrived at separately. Nominal GDP is measured as the purchases of goods and services at market prices. Nominal income is the payments received by workers and investors. Any difference between these two measured is labeled as a statistical discrepancy.

Personal income includes transfer payments, which are checks written by the government–Social Security, or unemployment compensation, or this year’s stimulus checks. Transfer payments are not part of national income, because they are not earned from the sale of goods and services. If you counted transfer payments in national income, the “statistical discrepancy” would get out of hand.

All of these flows are measured at annual rates. If you get a $1000 stimulus check in April, then at an annual rate that is $12,000. And some of us did not even get our checks until May. So the second quarter (April, May, and June) is going to see a whopping increase in personal income.

Some households will spend their stimulus checks right away, but many households will not. There’s only so much spending you can do with all the stores closed. In the national income accounts, there will be a big increase in personal savings. This will not be matched by investment; instead it will be matched by government dis-saving, a larger government deficit.

Suppose that households were to spend all of their income in the second quarter. Meanwhile, 20 percent are unemployed, so output should be down by a lot. Nominal spending up and real output down means that prices have to rise.

As I see it, the price rise was delayed by the fact that households did a lot of saving. Eventually, as they spend their stimulus checks, we will see the impact on prices.

Consider an extreme case. Suppose that textbook Y is $1. That is, we produce $1 of output and receive $1 in income. Next, the government writes a total of $99 in stimulus checks to households. Now households have $100 in personal income, and the government has a $99 deficit. When households spend their $100 on the output, the price of output will go up. Income will also go up, which means people can spend still more. The process only stops when the government engages in saving by running a surplus. That surplus cuts into personal income, reducing spending.

The agony of the conservative intellectual, 2016

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I review a Never Trump, by Robert P. Saldin and Steven M. Teles. The book does well in capturing conservative intellectuals’ thought process in 2016.

Before November, opposition to Mr. Trump could be thought of as having little cost. Because no one expected him to win, the question for intellectuals was how best to position themselves for the aftermath of his defeat.

In 1965, Robert Novak wrote a book called The Agony of the GOP, 1964. It was about the Goldwater movement’s takeover of the Republican Party that year. That resulted in an immediate electoral disaster, but without long-term damage to the Republicans. 2016 may have been the reverse.

Some outlandish long-term virus predictions

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I don’t want to post much about the virus. Maybe once a month, taking a more long-term perspective. Take these predictions not as “I believe these are highly probable” but instead as “I find these less improbable than others are currently seeing them.”

1. We will find that regional differences in the impact of the virus depend very little on differences in government interventions. We will down-rate the importance of lockdowns or track-and-trace. Instead, we will up-rate genetic differences and lifestyle differences that affect the immune system in general (take this WSJ essay as a portent). The significance of vitamin D will receive more attention. In addition, we may find that someone’s previous exposure to other viruses affects the immune response to this virus, so that the history of other viruses in a population matters. Sunlight and/or temperature may prove to be important factors affecting the severity of the virus. Finally, we may find that some of the regional variation is due to different mixes of virus strains that prevail in different areas.

2. We will quietly give up on a vaccine. Instead, the focus will shift toward general enhancement of our immune systems. Also, there will be strong social shaming of people who fail to self-isolate when they have fever or other symptoms of illness. The common cold will be as unwelcome in public as leprosy or measles.

3. In years to come, tourism will be highest in the summer months of the country visited. The conventional wisdom will be that you visit Brazil only in January-February, and you visit Italy only in July-August. Even if this virus is no longer salient, people will carry with them a generic perception that you incur health risk if you visit a place during the “unsafe season.”

4. Ventilators will be mothballed. Instead, the treatment of choice for severe cases of the virus will be antiviral cocktails.

5. Student life at colleges this fall will be heavily regulated, to the point where the on-campus experience feels hardly more interesting than staying at home. Among students, deaths from the virus will be much rarer than deaths of despair (suicide will be higher than normal), but where virus deaths do occur the institutions will be forced to close temporarily, and in some instances permanently.

6. In the early fall, the media will be filled with stories, including many false alarms, about a second wave of the virus, particularly in Red states. A realistic picture will emerge only after the election.

7. Florida may do worse in the summer than in the winter, because summer is the season where Floridians spend all day inside.

Epistemological Wisdom

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1. Michael Huemer writes,

I started thinking about other very important, general epistemological lessons. Lessons that most human beings have not gotten, which has led to lots of other errors. So here’s one; this probably wouldn’t be a good single sentence to leave to the future (since it requires further explanation), but it’s still one of the most important facts of epistemology: Your priors are too high.

Equivalently, he writes

Almost all beliefs require evidence, and they require a lot of it. Way more than you’re thinking.

One consequence of “your priors are too high” is that your mind is too hard to change.

2. Edward R. Dougherty writes,

Four conditions must be satisfied to have a valid scientific theory: (1) There is a mathematical model expressing the theory. (2) Precise relationships, known as “operational definitions,” are specified between terms in the theory and measurements of corresponding physical events. (3) There are validating data: there is a set of future quantitative predictions derived from the theory and measurements of corresponding physical events. (4) There is a statistical analysis that supports acceptance of the theory, that is, supports the concordance of the predictions with the physical measurements—including the mathematical theory justifying the application of the statistical methods.

The theory must be expressed in mathematics because science involves relations between measurable quantities and mathematics concerns such relations. There must also be precise relationships specified between a theory and corresponding observations; otherwise, the theory would not be rigorously connected to physical phenomena. Third, observations must confirm predictions made from the theory. Lastly, owing to randomness, concordance of theory and observation must be characterized statistically.

…Practically speaking, a leader need not know the mathematical particulars of a theory, but he must understand the validation process: what predictions are derived from the theory and to what extent have those predictions agreed with observations?

This is not to argue that leadership be confined to scientists and engineers, only that education include serious scientific, mathematical, and statistical courses. Certainly, one cannot expect good political leadership from someone ignorant of political philosophy, history, or economics, or from someone lacking the political skill to work productively amid differing opinions. The basic point is that good decision-making in a technical civilization requires fundamental knowledge of scientific epistemology.

…To validate a deterministic model, one can align the model and experiment with various initial states and check to see if predictions and observations agree. There might be some experimental variation, but in principle this can be reduced arbitrarily and slight disagreements ignored.

The situation with stochastic models is completely different. For a single initial condition, there are many destination states and these are described via the model by a probability distribution giving the likelihoods of ending up in different states. An experiment consists of many observation trajectories from a single initial state and the construction of a histogram giving the distribution of the experimental outcomes relative to that state. Validation concerns the degree of agreement between the theoretical, model-derived probability distribution and the data-derived histogram. Acceptance or rejection of the theory depends on some statistical test measuring the agreement between the two curves—and here it should be recognized that there is no universally agreed upon test.

…Confronting the problems of complexity, validation, and model uncertainty, I have previously identified four options for moving ahead: (1) dispense with modeling complex systems that cannot be validated; (2) model complex systems and pretend they are validated; (3) model complex systems, admit that the models are not validated, use them pragmatically where possible, and be extremely cautious when interpreting them; (4) strive to develop a new and perhaps weaker scientific epistemology.14

The first option would entail not dealing with key problems facing humanity, and the second, which seems popular, at least implicitly, is a road to mindless and potentially dangerous tinkering. Option three is risky because it requires operating in the context of scientific ignorance; but used conservatively with serious thought, it may allow us to deal with critical problems. Moreover, option three may facilitate productive thinking in the direction of option four, a new epistemology that maintains a rigorous formal relationship between theory and phenomena.

3. https://slatestarcodex.com/2020/05/18/coronalinks-5-18-20-when-all-you-have-is-a-hammer-everything-starts-looking-like-a-dance/

Milanovic on capitalism and its effects

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Fascinating conversation between Russ Roberts and Branko Milanovic. Milanovic on assortive mating:

I looked at the cohorts of young, first, American males, men between the ages of 20 and 35 in 1970. And I looked at people who were in the top wage group. They were as likely to marry another woman from a top wage group as to marry somebody from the bottom. So the ratio is one to one.

Well, nowadays, for males, it’s three to one. So, they are three times as likely to marry a woman who is also from the same top income group as a woman who is from the bottom. But, for women, it’s even more dramatic. The change is five to one. It used to be one to one and it’s now five to one.

On who attends top universities:

percentage of people who go to the top schools and where the parents come from, of course, you have–I think actually I mentioned that in my book; that’s not my own number; I actually took it from somebody else–is the ratio is 60 to 1 between the top 1% and the middle class.

But some of the more interesting discussion comes later in the podcast. Especially around minute 80 when they talk about how when many things can be outsourced the role of the family changes, and this reduces family formation.

Martin Gurri on social epistemology

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In a new essay, Martin Gurri writes,

Post-truth, as I define it, signifies a moment of sharply divergent perspectives on every subject or event, without a trusted authority in the room to settle the matter. A telling symptom is that we no longer care to persuade. We aim to impose our facts and annihilate theirs, a process closer to intellectual holy war than to critical thinking.

He and I are going to try to do a podcast on the topic, since I have also been interested in it.