Covid-19 6/4: The Eye of the Storm

I attempted to produce a rough estimate of this here (excerpted below):

... One (BERI funded!) study suggested that banning large gatherings reduced r0 by around 28%.
Unfortunately, protests seem in many ways ideal for spreading the disease. They involve a large number of people in a relatively small area for an extended period of time. Even protests which were advertised as being socially distanced often do not end up that way. While many people wear masks, photos of protests make clear that many do not, and those that are are often using cloth masks that are significantly less effective than surgical or n95s in the face of repeated exposure. Additionally, protests often involve people shouting or chanting, which cause infectious droplets to be released from people's mouths. Exposure to tear gas can apparently also increase susceptibility, as well as cramped indoor conditions for those arrested.
It's hard to estimate how many new cases will be caused by the protests, because there doesn’t seem to be good statistics on the number of people at protests, so we can't model the physical dynamics easily. A simple method would be to assume we have lost the benefits of the ban on large gatherings over the last week or so. On the one hand, this may be an over-estimate, because fortunately most people continue to socially distance, and protests take place mainly outside. On the other hand, protesters are actively seeking out (encouraging others to seek out) boisterous large gatherings in a way they were not pre-March, which could make things even worse. On net I suspect it may under-estimate the incremental spread, but given the paucity of other statistics we will use it as our central scenario.
If the r0 was around 0.9 before, this suggests the protests might have temporarily increased it to around 1.25, and hopefully it will quickly return to 0.9 after the protests end. Even if we assume no chain infections during the protest - so no-one who has been infected at a protest goes on to infect another protester - this means the next step in disease prevalence would be a 25% increase instead of a 10% decrease. Unfortunately the exponential nature of infection means this will have a large impact. If you assume around 1% of the US was infected previously, had we stayed on the previous r0=0.9 we would end up with around 9% more of the population infected from here on before the disease was fully suppressed. In contrast, with this one-time step-up in r0, we will see around 12.5% of the population infected from here - an additional 3.5% of the population.
Assuming an IFR of around 0.66%, that's a change from around 190,000 deaths to more like 265,000. Protesters skew younger than average, suggesting that this IFR may be an over-estimate, but on the other hand, they are also disproportionately African American, who seem to be more susceptible to the disease, and the people they go on to infect will include older people.
Free Money at PredictIt?

I still found this helpful as it allowed me to exit my directional Yang and Buttigieg positions with negative transaction cost.

Honoring Petrov Day on LessWrong, in 2019

I would like to add that I think this is bad (and have the codes). We are trying to build social norms around not destroying the world; you are blithely defecting against that.

Power Buys You Distance From The Crime
This case is more complicated than the corporate cases because the powerful person (me) was getting merely the appearance of what she wanted (a genuine relationship with a compatible person), not the real thing. And because the exploited party was either me or Connor, not a third party like bank customers. No one thinks the Wells Fargo CEO was a victim the way I arguably was.

I think you have misunderstood the Wells Fargo case. These fake accounts generally didn't bring in any material revenue; they were just about internal 'number of new accounts targets'. It was directly a case of bank employees being incentivised to defraud management and investors, which they then did. If ordinary Wells employees had not behaved fraudulently, all the targets would have been missed, informing management/investors about their mis-calibration, and more appropriate targets would have been set. In this case power didn't buy distance from the crime, but only in the sense that it meant you couldn't tell you were being cheated.

For more on this I recommend the prolific Matt Levine:

There's a standard story in most bank scandals, in which small groups of highly paid traders gleefully and ungrammatically conspire to rip-off customers and make a lot of money for themselves and their bank. This isn't that. This looks more like a vast uprising of low-paid and ill-treated Wells Fargo employees against their bosses.
So that's about 2.1 million fake deposit and credit-card accounts, of which about 100,000 -- fewer than 5 percent -- brought in any fee income to Wells Fargo. The total fee income was $2.4 million, or about $1.14 per fake account. And that overstates the profitability: Wells Fargo also enrolled people for debit cards and online banking, but the CFPB doesn't bother to count those incidents, or suggest that any of them led to any fees. Which makes sense: You'd expect online banking and debit cards to be free, if you never use them or even know about them. Meanwhile, all this dumb stuff seems to have occupied huge amounts of employee time that could have been spent on more productive activities. If you divide the $2.4 million among the 5,300 employees fired for setting up fake accounts, you get about $450 per employee. Presumably it cost Wells Fargo way more than that just to replace them.
In the abstract, you can see why Wells Fargo would emphasize cross-selling of multiple "solutions" to customers. It is a good sales practice; it both indicates and encourages customer loyalty. If your customers have a checking account, and a savings account, and a credit card and online banking, all in one place, then they'll probably use each of those products more than if they had only one. And when they want a new, lucrative product -- a mortgage, say, or investment advice -- they're more likely to turn to the bank where they keep the rest of their financial life. 
But obviously no one in senior management wanted this. Signing customers up for online banking without telling them about it doesn't help Wells Fargo at all. No one feels extra loyalty because they have a banking product that they don't use or know about. Even signing them up for a credit card without telling them about it generally doesn't help Wells Fargo, because people don't use credit cards that they don't know about. Cards with an annual fee are a different story -- at least you can charge them the fee! -- but it seems like customers weren't signed up for many of those.  This isn't a case of management pushing for something profitable and getting what they asked for, albeit in a regrettable and illegal way. This is a case of management pushing for something profitable but difficult, and the workers pushing back with something worthless but easy.

Financial engineering for funding drug research

Is this very different from founding a pharmaceutical company?

Strategic implications of AIs' ability to coordinate at low cost, for example by merging

Critch wrote a related paper:

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs.Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine’s policy will prioritize each player’s interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player’s own beliefs in evaluating how well an action will serve that player’s utility function, and (2) shift the relative priority it assigns to each player’s expected utilities over time, by a factor proportional to how well that player’s beliefs predict the machine’s inputs. Observation (2) represents a substantial divergence from naive linear utility aggregation (as in Harsanyi’s utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.

Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making

Strategic implications of AIs' ability to coordinate at low cost, for example by merging

War only happens if two agents don’t have common knowledge about who would win (otherwise they’d agree to skip the costs of war).

They might also have poorly aligned incentives, like a war between two countries that allows both governments to gain power and prestige, at the cost of destruction that is borne by the ordinary people of both countries. But this sort of principle-agent problem also seems like something AIs should be better at dealing with.

Literature Review: Distributed Teams

In light of this:

Build over-communication into the process.
In particular, don’t let silence carry information. Silence can be interpreted a million different ways (Cramton 2001).

Thanks for writing this! I found it very interesting, and I like the style. I particularly hadn't properly appreciated how semi-distributed was worth than either extreme. It's disappointing to hear, but seemingly obvious in retrospect and good to know.

2018 AI Alignment Literature Review and Charity Comparison

Thanks for sharing, seems like a reasonable take to me.

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