You're looking at Less Wrong's discussion board. This includes all posts, including those that haven't been promoted to the front page yet. For more information, see About Less Wrong.

jimrandomh comments on A Proposal for Defeating Moloch in the Prison Industrial Complex - Less Wrong Discussion

23 Post author: lululu 02 June 2015 10:03PM

You are viewing a comment permalink. View the original post to see all comments and the full post content.

Article Navigation

Comments (76)
Tags: qalys prison incentives ideas moloch

Comments (76)

You are viewing a single comment's thread. Show more comments above.

Comment author: VoiceOfRa 02 June 2015 10:32:59PM 1 point [-]

Politicians compete for electability. Convicts can’t vote, prisons make campaign contributions and jobs, and appearing “tough on crime” appeals to a large portion of the voter base.

You seem to have forgotten why appearing "tough on crime" appeals to current voters.

Breif history lesson: During the 1960's and early 1970's politicans competed for electability by appearing compasionate to criminals, who were after all only "victims of society". The result was the massive crime wave of the 1970's. As a result the generation which grew up during that time learned that politicans promissing being compasion for criminals leads to an increased chance of them being mugged. Now that generation is the one in charge and they like politicans who are "tough on crime".

Comment author: jimrandomh 02 June 2015 11:42:41PM 6 points [-]

"Our opinion-makers have gone too far in promoting the doctrine that when a law is broken, society, not the criminal is to blame." -- Richard Nixon, 1967 (about a year before he became President)

The data does not support the claim that "tough on crime" strategy was effective.

Comment author: knb 03 June 2015 09:41:50PM *  4 points [-]

The data does not support the claim that "tough on crime" strategy was effective.

Misleading. You cited a single quote from 1967 from a politician who held no elected office to try to imply that is when tough-on-crime legislation was adopted. To actually make the case, you would have to actually cite when actual tough-on-crime legislation was passed and implemented. Of course, harsher penalties were not implemented all at once, it happened over the course of decades, lasting into the 1980s and 1990s.

Comment author: jimrandomh 04 June 2015 12:27:37AM 0 points [-]

True; I quoted Nixon instead of pointing at legislation because checking the positions of presidents was easier than finding relevant legislation.

Comment author: shminux 02 June 2015 11:50:36PM 7 points [-]

The data does not support the claim that "tough on crime" strategy was effective.

But the "tough on lead" strategy likely was.

Comment author: anon85 03 June 2015 06:32:52PM 4 points [-]

Meh, probably not:

http://stevenpinker.com/files/pinker/files/pinker_comments_on_lead_removal_and_declining_crime.pdf

There are reasons to be skeptical of any claim based on correlations between such widely separated variables as lead exposure (the cause) and crime (the effect). Consuming lead does not instantly turn someone into a criminal in the way that consuming vitamin C cures scurvy. It affects the child’s developing brain, which makes the child duller and more impulsive, which, in some children, and under the right circumstances, leads them to grow up to make short-sighted and risky choices, which, in some children and under the right circumstances, leads them to commit crimes, which, if enough young people act in the same way and at the same time, affects the crime rate. The lead hypothesis correlates the first and last link in this chain, but it would be more convincing if there were evidence about the intervening links. Such correlations should be far stronger than the one they report: presumably most kids with lead are more impulsive, whereas only a minority of impulsive young adults commit crimes. If they are right we should see very strong changes in IQ, school achievement, impulsiveness, childhood aggressiveness, lack of conscientiousness (one of the “Big Five” personality traits) that mirror the trends in lead exposure, with a suitable time delay. Those trends should be much stronger than the time-lagged correlation of lead with crime itself, which is only indirectly related to impulsiveness, an effect that is necessarily diluted by other causes such as policing and incarceration. I am skeptical that such trends exist, though I may not be aware of such studies.

...

Also, the parallelism in curves for lead and time-shifted crime seem too good to be true, since the lead hypothesis assumes that the effects of lead exposure are greatest in childhood. But 23 years after the first lower-lead cohort, only a small fraction of the crime-prone cohort should be lead-free; there are still all those lead-laden young adults who have many years of crime ahead of them. Only gradually should the crime-prone demographic sector be increasingly populated by lead-free kids. The time-shifted curve for crime should be an attenuated, smeared version of the curve for lead, not a perfect copy of it. Also, the effects of age on crime are not sharply peaked, with a spike around the 23rd birthday, and a sharp falloff—it’s a very gentle bulge spread out over the 15-30 age range. So you would not expect such a perfect time-shifted overlap as you might, for example, for first-grade reading performance, where the measurement is so restricted in time.

Finally, the most general reason for skepticism about a causal hypothesis based on epidemiological correlations between a widely separated cause and effect is that across times and places, many things tend to go together. Neighborhoods next to smoggy freeways also tend to be poorer, more poorly policed, more poorly schooled, less stable, more dependent on contraband economies, and so on. It’s all too easy to find spurious correlations in this tangle – which is why so many epidemiological studies of the cause and prevention of disease (this gives you cancer; that prevents it) fail to replicate.

Comment author: Unnamed 03 June 2015 11:25:07PM 0 points [-]

It sounds like Pinker is unfamiliar with most of the research on the topic. He refers to this graph, but not any of the research on the various other predictions that you could derive from the hypothesis that lead caused much of the hump in crime over the past 60 years.

The things that he says about priors, and about the sorts of research that he'd like to see, sound plausible, but I wouldn't put much stock in what he says about the state of the research.

Comment author: anon85 03 June 2015 11:30:32PM 0 points [-]

He refers to this graph, but not any of the research on the various other predictions that you could derive from the hypothesis that lead caused much of the hump in crime over the past 60 years.

Can you name some of these predictions? Can you link to some of the research? What exactly are you referring to?

Comment author: Unnamed 03 June 2015 11:53:45PM 1 point [-]

3 predictions that I came up with, when I heard about the hypothesis:

  1. The lead hypothesis predicts a cohort effect: the crime rate for 35-year-olds should drop 10 years after the crime rate drops for 25-year-olds. Many competing hypotheses (like new policing tactics) predict a cross-sectional effect: the crime rate for 35-year-olds drops at the same time that the crime rate drops for 25-year-olds. (Although there may be feedback effects which cause some smudging of this sharp distinction, e.g. more crime among a subgroup means that police are spread thinner which makes crime more attractive for everyone.)
  2. The lead hypothesis makes pretty specific predictions about differences in the timing of the crime drop across different regions. If one jurisdiction removes lead 8 years after another jurisdiction, then their crime rate should drop 8 years later.
  3. The lead hypothesis predicts differences in the size of the crime increase & drop across different regions. If one region had more environmental lead than another, then it should have both a larger increase in crime during the "increasing crime rates" time period and a larger drop in crime during the "declining crime rates" time period.

I looked at one of Nevin's papers shortly after the Drum piece originally came out and it had some evidence for all 3 predictions, though not with as much rigor/precision/detail as I would've liked. For example, on prediction #3 it compared larger cities (which had more driving per unit area, and thus more environmental lead due to gasoline) to smaller cities and showed that their crime rates matched this pattern.

There is also research on other steps in the long chain (e.g., measuring blood levels of lead), and on other outcomes attributed to lead (e.g., teen pregnancy rates), some of which is mentioned in Drum's original piece. I haven't looked into that research beyond what I've seen in the popular press articles.

Comment author: anon85 04 June 2015 01:15:43AM 0 points [-]

Do you think you could link to that paper?

A Larger vs. smaller cities comparison sounds like it has ample room for confounding factors, no?

The other outcomes attributed to lead sound like they correlate with crime rates, so this isn't independent evidence for the lead hypothesis.

Comment author: Unnamed 04 June 2015 04:22:22AM 0 points [-]

I think the paper that I looked at was The Answer is Lead Poisoning. Mainly just looking at the graphs & tables.

The city size pattern is not a unique prediction of the lead hypothesis (there are various other differences between large & small cities which could account for it, though nothing that strikes me as overwhelmingly obvious), but it is a relatively unambiguous prediction (especially if there's high quality data on city size vs. environmental lead levels - I'm not sure how good those data are). If large vs. small cities turned out not to have this difference in crime trends then that would be pretty strong evidence against the lead hypothesis, so the fact that the comparison did come out this way must be at least some evidence in favor of the lead hypothesis.

Comment author: OrphanWilde 03 June 2015 07:27:39PM 0 points [-]

Also, the parallelism in curves for lead and time-shifted crime seem too good to be true, since the lead hypothesis assumes that the effects of lead exposure are greatest in childhood. But 23 years after the first lower-lead cohort, only a small fraction of the crime-prone cohort should be lead-free; there are still all those lead-laden young adults who have many years of crime ahead of them. Only gradually should the crime-prone demographic sector be increasingly populated by lead-free kids. The time-shifted curve for crime should be an attenuated, smeared version of the curve for lead, not a perfect copy of it. Also, the effects of age on crime are not sharply peaked, with a spike around the 23rd birthday, and a sharp falloff—it’s a very gentle bulge spread out over the 15-30 age range. So you would not expect such a perfect time-shifted overlap as you might, for example, for first-grade reading performance, where the measurement is so restricted in time.

Am I misreading this, or is this suggesting that the fact that the time-shifted correlation is unusually strong should be taken as evidence -against- the correlation?

Comment author: anon85 03 June 2015 10:59:33PM 1 point [-]

Suppose you're Bayesian, and you're calculating

P(lead causes crime | data) = P(data | lead causes crime) * P(lead causes crime) / P(data).

What Pinker is saying is that P(data | lead causes crime) is not as high as you'd think, because if lead really does cause crime, we should not expect the crime curve to be a time-shifted version of the lead curve. It's probably still true that P(data | lead causes crime) > P(data), so that you should update in the direction of lead causes crime, but this update should probably be smaller than you thought before reading that paragraph.

Comment author: Unnamed 04 June 2015 04:31:22AM 0 points [-]

Has anyone figured out what crime curve you would expect based on the lead curve (presumably a version that is shifted & smeared out based on the age distribution of criminals), and checked how well it fits the actual crime data? It's not obvious to me, from looking at the pictures that I've seen with the shifted curves, that adding the smearing would make the fit worse. For instance, the graph I linked earlier shows that the recent drop in crime is more gradual than the drop in lead that happened 20-30 years ago, which seems to fit the more rigorous "time-shifted and smeared out" prediction better than it fits the simplistic time-shifted curve approach that Nevin used.

Powered by Reddit Powered by Reddit