This paper studies the equilibrium determination of the number of political jurisdictions […] . We focus on the trade off between the benefits of large jurisdictions in terms of economies of scale and the costs of heterogeneity of large and diverse populations.

The model they use is grievously unrealistic, but it’s a question I’d long been idly interested in.

Fifty-eight percent of those who think climate change is happening support a carbon tax, while 62 percent of those who do not accept that climate change is taking place oppose a carbon tax.

Support for a carbon tax is generally higher once told how the funds would be used.

Provides some extra context on Gas taxes for thee, but not for me.

Any time we charge a positive price for anything, the cost of paying that price is a higher burden on the poor than it is on the rich. It takes a special combination of myopia and tunnel vision to look at the prospect of congestion pricing anything other than a minor blip on a system of transportation finance that is systematically unfair to the poor and those who don’t own (or can’t afford) car.

Good rebuttal to a common objection to Pigouvian taxes as discussed here.

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• ## Priority decisions

Explanations and interactive tools demonstrating maximin, maximax and leximin decision rules.

##### Contents

Last time, we introduced the basic setup of decision theory and examined the dominance decision rule. We also emphasized that the dominance decision rule is “weak” because it applies in very general settings with limited information to go on.

This time, we’ll look at other decision rules that apply in that very general setting. They’re still decisions under ignorance—no probabilities associated with states of the world—and outcomes are still measured only on an ordinal scale.

### Maximin

The first such decision rule is maximin.

#### Prose

Maximin suggests that in any decision scenario, we look to the worst outcome that may come to pass under each plan of action. We should then pick the action which has the best such outcome. That is, we pick the action with the best worst case—maximize our minimum.

#### Example

You have the choice of two alternative routes to work. In good conditions, the first route takes 10 minutes and the second route 5 minutes. But the second route is prone to traffic and on bad days takes 20 minutes while the first route still takes 10 minutes.

With a scenario like this, the maximin rule demands that you take the first route since its worst case is only 10 minutes while the second route’s worst case is 20 minutes.

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• ## Dominated decisions

Preliminaries of decision theory and a basic interactive tool demonstrating the dominance decision rule.

### Decision matrices

(Peterson 2017) points out that we can represent decisions with decision matrices. For example, when considering the purchase of home insurance, we have:

Each row (after the first) represents a different action and each column (after the first) represents a different possible state of the world. Their intersections—the four cells that are the combination of an act and a world state—are called outcomes.

### Sets of settings

In decision theory (and social choice theory, game theory, mechanism design, etc.), when presented with a decision, it’s often useful to start by taking stock of what information we have available and what information we would like but don’t have. Depending on the result of this assessment, we will have better or worse strategies available. That is, we’d like to determine the best strategy or solution given the information available. Are there better strategies that we could execute with more information? What is that information? For example, we approach the question of “Should we buy home insurance?” very differently if we know the precise chance of our house catching on fire. Without key information like that, we have to resort to second-best strategies.

The decision matrix we depicted above reflects one of the simplest possible1 settings. In particular, we don’t have any probabilities associated with the different states of the world (“Fire” or “No fire”) which makes it a “decision under ignorance”. Another key limitation is that we don’t have a number representing how good or bad each outcome is—our outcomes have not been assigned cardinal utility. Instead, our outcomes are only on an ordinal scale.

Because this setting is so minimal, it both has wide applicability—it makes very few assumptions that can be contradicted by facts on the ground—and limited insight—the best you can do with minimal information still isn’t very good.

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You’ve probably heard claims about middle class American income stagnating over the past decades and most of the gains in productivity going to the top X%. The following table lists studies that looked at income trends in the US over time. Which of these methods of income analysis sounds like it’s closest to measuring something you’d actually be interested in knowing?

By presenting the analysis plans without the corresponding results, you can arrive at a judgment with a clean conscience—no need to fear that you’re simply approving the study with your favored result. When you’re ready to see what studies and results these descriptions correspond to, look at Table 1 in the linked PDF.

Together with various antelopes, baboons form multispecies groupings that take advantage of the great vision of the primates and the better smell and hearing of ungulates.

Housing prices in some cities in China have increased more than tenfold in the past decade. They appear to be rising too fast relative to the growth of income.

[…]

Due in part to the one-child policy, there were 120 Chinese men for every 100 Chinese women as of 2005—in some provinces this ratio is as high as 130 to 100. […] One of the most visible symbols of this status competition comes through housing. […] This places a lot of pressure on Chinese families with sons to demonstrate their value through homeownership.

[…]

We found that home prices are higher and home sizes are bigger in cities with more skewed sex ratios. Strikingly, the sex ratio imbalance explained between half and one-third of the increase in housing prices in 25 major cities between 2003 and 2009.
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• ## Anthropological enumerations

Key terms, categories and explanations from Ember’s Cultural Anthropology. The biggest omissions in this abridgment are the illustrative examples.

### Getting food

#### Foraging

Anatomically modern humans showed up to the party rather late—perhaps as late as only 130,000 years ago. This was right also right around the time Homo sapiens become cognitively modern and capable of symbolic thought. But looking good and thinking symbolically do not a good party make. Everyone would have been leaning against the wall and stealing furtive glances at each other for many millennia until they worked up the courage to finally invent language and talk to each other around 50,000 years ago.

So, regardless of what you use as your benchmark for modernity—anatomic, cognitive, or linguistic, the advent of food production in around 8,000 B.C.E. is relatively recent. That means, for most of human history, we’ve been foragers. “Foragers” are often called “hunter-gatherers” but this name is somewhat misleading. Hunting and gathering each typically contribute about 25-35% of caloric intake while fishing constituted the remaining 30-50%.

There is ongoing debate about just how sweet the pre-historic foraging life was. On one hand, per capita GDP between $90 and$200 for essentially forever doesn’t sound so hot (DeLong 1998) from the perspective of modernity. On the other hand, as we mentioned in a previous post, even modern foragers (who are typically thought to live in more marginal territory than early foragers) work fairly little. For example, the !Kung spend only about 42 hours on foraging, housework and tool-making all together. On the third hand, John “You’ve to break a few billion eggs to make an omelet”1 Zerzan is on the primitivist side so no thank you.

#### Food production

Like we said, the earliest food production arose in around 8,000 B.C.E. in the Near East.

Food production perhaps arose because it was the only way to support increased population. At some point, new bands of humans simply ran out of unoccupied territory to move into. Thereafter, population density would have increased beyond what foraging could support and horticulture, more productive per unit area than foraging, would have been the only option. This commonsensical theory of the rise of food production is called the Binford-Flannery model.

An alternative theory is that climate change reduced the availability of wild food supplies.

Regardless of the origin story, there are three major categories of food production:

Horticulture
“Plant cultivation carried out with relatively simple tools and methods”. Probably the earliest form of food production. Shifting cultivation, in which land is worked temporarily and then abandoned while soil nutrients naturally replenish, is a common form. 60 of 186 SCSS cultures are horticultural (variable 246) (Murdock and White 1969).
Intensive agriculture
“Food production characterized by the permanent cultivation of fields and made possible by the use of the plow, draft animals or machines, fertilizers, irrigation, water-storage techniques, and other complex agricultural techniques.” 57 of 186 SCSS cultures are intensively agricultural (variable 246) (Murdock and White 1969).
Pastoralism
“A form of subsistence technology in which food-getting is based directly or indirectly on the maintenance of domesticated animals.” 16 of 186 cultures SCSS are pastoral (variable 246) (Murdock and White 1969).
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