1. The Alsup Aftermath

A few weeks ago, I had an unusual — and challenging — assignment: providing a one-hour “tutorial” on the basic science of human-induced climate change to a Federal District Court in San Francisco. Judge William Alsup had requested this tutorial to bring him up to speed on the fundamental science before proceedings begin in earnest in a case brought by the cities of San Francisco and Oakland, on behalf of the people of California, against a group of major fossil fuel companies, addressing the costs of climate change caused, they argue, by products those companies have sold.

The most interesting part for me was learning that the basic story I’d heard about the greenhouse effect is so simplified as to be basically wrong. The actual mechanism of warming is examined in more detail here.

2. Mental Health

“[M]ental and substance use disorders account for around 7 percent of global disease burden in 2016, but this reaches up to 13-14 percent in several countries.”

3. Basic income and a public job offer: complementary policies to reduce poverty and unemployment

The paper’s a bit meandering, but I think the core idea—that a universal basic income and a job guarantee aren’t mutually exclusive or even particularly competitive—is valuable and true. The most import sources of conflict are probably finite supplies of political capital and the enormous complexity of implementing one of these policies, yet alone two.

4. Earmarks: Better Government through Honest Graft

Related to the early post here on the tyranny of the majority.

Under simple majority rule, a largely indifferent majority can approve a result that is intensely opposed by everyone in the minority. If out-and-out bribery were permitted, it would be relatively easy for the minority to bribe the majority faction to defeat the hated policy, and, since the minority does really hate the original proposal, both they and the majority would be, on balance, better off in the post-bribe situation. The original result of majority rule was not Pareto optimal. It was economically inefficient.

In the real world, it is not necessary to buy votes, either of individual voters or their representatives. What we actually see as a part of normal democratic practice is a process of vote trading, sometimes called “logrolling.” Voter 1 cares deeply about issue A but not about issue B. It is then rational for voter 1 to trade his vote on B for voter 2’s vote on A. As a result, everyone is better off. Dennis Mueller, Geoffrey Philpotts, and Jaroslav Vanek demonstrated in a 1972 paper in Public Choice that the analogy between the efficiency of ordinary markets and the efficiency of vote trading in the political sphere is almost perfect.1 In nearly all cases, a system of logrolling will take a society to a state very close to Pareto optimality.Earmarks are merely a special case of logrolling. They enable political minorities (even a minority of one) to have an impact on policy, despite an apathetic or even hostile majority.
5. Why is the replication crisis centered on social psychology?

Interestingly, most of the reasons advanced here aren’t about social psychology being ‘worse’ than other fields (e.g. more corrupt, less competent) but ‘better’ (e.g. more open with data, replications are easier). The one explanation offered contrary to that pattern is “psychology studies often (not always, but often) feature weak theory + weak measurement”.

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• ## Putting-out with smartphones II

There’s not a lot of high quality evidence, but it seems plausible to me that the putting-out system was better for workers than the factory system and that most gig workers would prefer more standard employment.

They have driven us away from our houses and gardens to work as prisoners in their factories and their seminaries of vice. Thomas Exell, 1838

### Intro

Last time, we reviewed the putting out system—a pre-industrial system of manufacture used in England where workers would take intermediate products home, refine them in some way, and return them to a merchant so that some other worker could perform the next step in the production process. Then, I suggested that this system sounds a lot like the modern gig economy.

Which left us with the question: Why is the gig economy worse than regular, full-time employment (for workers), but putting-out was better than factories (for workers)? We can resolve this tension by either finding that contemporary praise of the putting out system is wrong, finding that criticism of the gig economy is wrong, or (hint, hint) highlighting the disanalogies between the systems.

### Was putting-out actually preferred to factories?

#### Direct evidence

Obviously, we can’t answer this question definitively and our direct evidence1 is weak. One piece of direct evidence we do have is contemporary complaints about the factory system like that in the epigraph. Of course, these are only anecdotes and it’s hard to generalize or be sure they’re representative, but, try as I might, I couldn’t find any anecdotes on the other side of the issue—there were no early factory workers singing praises of child labor and 14 hour days.

Another piece of information which tends to reveal preferences is: “[W]here alternatives to factory employment were available, there is evidence that workers flocked to them. […] [D]espite the abysmally low level to which wages fell [in the non-factory cotton weaving industry], a force of domestic cotton weavers numbering some 250,000 survived well into the nineteenth century.” (Marglin 1974) It is, of course, impossible to be certain that worker aversion to factories is what drove this behavior.

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• ## Ethics as well-order

There is an analogy to be found between choice functions and ethical theories. Both select a distinguished element/action from a collection of sets/actions. If we continue along this line of thought, certain assumptions permit us to make a further analogy between well-orders and ethical theories.

### Intro

Ethics is fundamentally about ‘ought’. What ought we to do? Which actions are proscribed and which prescribed? Among all available actions, which should we actually pursue? I think we can formalize this basic understanding and draw some interesting conclusions.

For any given ethical decision, we have some nonempty set $$\mathcal{A}$$ (depending on how we individuate, possibly infinite) of possible actions. But, alas, we cannot perform all these actions; only one. So an ethical theory is something that for every possible $$\mathcal{A}$$ selects a distinguished element $$a \in \mathcal{A}$$. That is, an ethical theory is a choice function over nonempty sets of actions.

In the rest of the post, we’ll discuss what properties such an ethical choice function might have and build up an understanding of how the tools of order theory might be applied to metaethical reasoning.

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• ## Standalone–Build your own von Neumann–Morgenstern utility function

Which things would you like to make a utility function out of?

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• ## Build your own von Neumann–Morgenstern utility function

The von Neumann–Morgenstern utility theorem lets us turn an ordinal utility function into a cardinal utility function. Here, we have an interactive widget that actually constructs a utility function from a series of questions using the theorem.

### von Neumann–Morgenstern utility theorem

The von Neumann–Morgenstern utility theorem says that, “under certain axioms of rational behavior, a decision-maker faced with risky (probabilistic) outcomes of different choices will behave as if he or she is maximizing the expected value of some function defined over the potential outcomes at some specified point in the future”. But the somewhat sloppy way I like to think of it is this: If a person has merely ordinal preferences (e.g. I prefer an apple to a banana but can’t or won’t quantify the magnitude of that preference. The preceding information alone isn’t enough to conclude how I’d feel about one apple vs. two bananas.) and reasons well under uncertainty, we can transform those ordinal preferences into a cardinal utility function (e.g. I like apples exactly twice as much as bananas and would be indifferent between an apple and two bananas (ignoring diminishing marginal utility for the same of exposition).).

This transformation is often useful because a cardinal utility function is much richer and more informative than an ordinal utility function. The extra information is useful, for example, in sidestepping Arrow’s impossibility theorem (which says that it’s impossible to have a good voting system if you only ask people for their ordering of candidates).

### Interactive VNM

The standard descriptions of the mechanism of the VNM utility theorem may be a little opaque. But because the theorem is constructive, we can actually give people a feel for it by putting them ‘inside’ the mechanism and showing them the result. That’s what we attempt here.

In the first text area, enter a list of goods (each on a separate line) for which you’d like to generate a utility function. It starts with a few sample goods, but you’re free to add, remove or otherwise alter these.

Once you’ve decided upon the goods you’re interested in, you can proceed to the next step. Here, you’ll be presented with a series of lotteries. In each lottery, you have to decide whether you prefer $$x$$ lottery tickets for one good over $$y$$ lottery tickets for the other good, or if you’re indifferent. If your lottery ticket is drawn, you win whatever good is on the ticket. You can register your answer as to which set of tickets you prefer by clicking on one of the three blue boxes.

For example, if you mildly prefer bananas to carrots, you’d click on the banana box when presented with one lottery ticket for each. A $$\frac{1}{n}$$ chance of a banana is better than a $$\frac{1}{n}$$ chance of a carrot, by your lights ($$n \geq 2$$). On the other hand (because your preference was only mild), you’d click on the carrot box if offered 100 carrot tickets vs. 1 banana ticket. A $$\frac{100}{n}$$ chance of a carrot is better than a $$\frac{1}{n}$$ chance of a banana ($$n \geq 101$$).

After you’ve repeated this process enough, we can deduce what your favorite good of all the listed goods is. With this as a numéraire, we can start to visualize your utility function and do so with a chart that appears at the bottom. But, of course, we still have uncertainty about the relative value of these goods. Based on the questions you answer, we know upper and lower bounds for your value (a carrot is better than $$\frac{1}{100}$$ banana but worse than $$\frac{1}{1}$$ banana). Over time, by answering more questions, we can refine these intervals until they’re arbitrarily small.

Try it out!

Which things would you like to make a utility function out of?

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