Great suggestion! That said, in light of your first paragraph, I’d like to point out a couple of issues. I came up with most of these by asking the questions “What exactly are you trying to encourage? What exactly are you incentivising? What differences are there between the two, and what would make those difference significant?”
You are trying to encourage prisons to rehabilitate their inmates. If, for a given prisoner, we use p to represent their propensity towards recidivism and a to represent their actual recidivism, rehabilitation is represented by p-a. Of course, we can’t actually measure these values, so we use proxies; anticipated recidivism according to your algorithm and re-conviction rate (we’ll call these p’ and a’, respectively).
With this incentive scheme, our prisons have three incentives: increasing p’-p, increasing p-a, and increasing a-a’. The first and last can lead to some problematic incentives.
To increase p’-p, prisons need to incarcerate prisoners which are less prone to recidivism than predicted. Given that past criminality is an excellent predictor of future criminality, this leads to a perverse incentive towards incarcerating those who were unfairly convicted (wrongly convicted innocents or over-convinced lesser offenders). If said prisons can influence the judges supplying their inmates, this may lead to judges being bribed to aggressively convict edge-cases or even outright innocents, and to convict lesser offenses of crimes more correlated with recidivism. (Counterpoint: We already have this problem, so this perverse incentive might not be making things much worse than they already are.)
To increase a-a’, prisons need to reduce the probability of re-conviction relative to recidivism. At the comically amoral end, this can lead to prisons teaching inmates “how not to get caught.” Even if that doesn’t happen, I can see prisons handing out their lawyer’s business cards to released inmates. “We are invested in making you a contributing member of society. If you are ever in trouble, let us know—we might be able to help you get back on track.” (Counterpoint: Some of these tactics are likely to be too expensive to be worthwhile, even ignoring morality issues.)
Also, since you are incentivising improvement but not disincentivizing regression, prisons who are below-average are encouraged to try high-volatility reforms even if they would yield negative expected improvement. For example, if a reform has a 20% chance of making things much better but a 80% chance of making things equally worse, it is still a good business decision (since the latter consequence does not carry any costs).
The incentive to try “high volatility” methods seems like an advantage; if many prisons try them, 20% of them would succeed, and we’d learn how to rehabilitate better.
Yep. Concretely, if you take one year to decide that each negative reform has been negative, the 20-80 trade that the OP posts is a net positive to society if you expect the improvement to stay around for 4 years.
Even if that doesn’t happen, I can see prisons handing out their lawyer’s business cards to released inmates. “We are invested in making you a contributing member of society. If you are ever in trouble, let us know—we might be able to help you get back on track.”
I don’t think that would be a problem. If more people get a good legal defense the system becomes more fair.
But even if you don’t like that, you can set up the rule that the prison doesn’t get the bonus if the prisoner is charged with a crime. You don’t need to tie the bonus to conviction.
I really, really wish policy makers considered possible perverse incentive exploits in this much detail. Though I’m not convinced there is any perfect policy that has zero exploits.
To increase p’-p, prisons need to incarcerate prisoners which are less prone to recidivism than predicted. Given that past criminality is an excellent predictor of future criminality, this leads to a perverse incentive towards incarcerating those who were unfairly convicted (wrongly convicted innocents or over-convinced lesser offenders).
If past criminality is a predictor of future criminality, then it should be included in the state’s predictive model of recidivism, which would fix the predictions. The actual perverse incentive here is for the prisons to reverse-engineer the predicted model, figure out where it’s consistently wrong, and then lobby to incarcerate (relatively) more of those people. Given that (a) data science is not the core competency of prison operators; (b) prisons will make it obvious when they find vulnerabilities in the model; and (c) the model can be re-trained faster than the prison lobbying cycle, it doesn’t seem like this perverse incentive is actually that bad.
(a) Prison operators are not currently incentivized to be experts in data science (b) Why? And will that fix things? There are plenty of examples of industries taking advantage of vulnerabilities, without those vulnerabilities being fixed. (c) How will it be retrained? Will there be a “We should retrain the model” lobby group, and will it act faster than the prison lobby?
Perhaps we should have a futures market in recidivism. When a prison gets a new prisoner, they buy the associated future at the market rate, and once the prisoner has been out of prison sufficiently long without committing further crimes, the prison can redeem the future. And, of course, there would be laws against prisons shorting their own prisoners.
If participants stop returning to jail at a rate of 10% or greater, Goldman will earn $2.1 million. If the recidivism rate rises above 10% over four years, Goldman stands to lose $2.4 million.
Your argument assumes that the algorithm and the prisons have access to the same data. This need not be the case—in particular, if a prison bribes a judge to over-convict, the algorithm will be (incorrectly) relying on said conviction as data, skewing the predicted recidivism measure.
That said, the perverse incentive you mentioned is absolutely in play as well.
Yes, I glossed over the possibility of prisons bribing judges to screw up the data set. That’s because the extremely small influence of marginal data points and the cost of bribing judges would make such a strategy incredibly expensive.
Great suggestion! That said, in light of your first paragraph, I’d like to point out a couple of issues. I came up with most of these by asking the questions “What exactly are you trying to encourage? What exactly are you incentivising? What differences are there between the two, and what would make those difference significant?”
You are trying to encourage prisons to rehabilitate their inmates. If, for a given prisoner, we use p to represent their propensity towards recidivism and a to represent their actual recidivism, rehabilitation is represented by p-a. Of course, we can’t actually measure these values, so we use proxies; anticipated recidivism according to your algorithm and re-conviction rate (we’ll call these p’ and a’, respectively).
With this incentive scheme, our prisons have three incentives: increasing p’-p, increasing p-a, and increasing a-a’. The first and last can lead to some problematic incentives.
To increase p’-p, prisons need to incarcerate prisoners which are less prone to recidivism than predicted. Given that past criminality is an excellent predictor of future criminality, this leads to a perverse incentive towards incarcerating those who were unfairly convicted (wrongly convicted innocents or over-convinced lesser offenders). If said prisons can influence the judges supplying their inmates, this may lead to judges being bribed to aggressively convict edge-cases or even outright innocents, and to convict lesser offenses of crimes more correlated with recidivism. (Counterpoint: We already have this problem, so this perverse incentive might not be making things much worse than they already are.)
To increase a-a’, prisons need to reduce the probability of re-conviction relative to recidivism. At the comically amoral end, this can lead to prisons teaching inmates “how not to get caught.” Even if that doesn’t happen, I can see prisons handing out their lawyer’s business cards to released inmates. “We are invested in making you a contributing member of society. If you are ever in trouble, let us know—we might be able to help you get back on track.” (Counterpoint: Some of these tactics are likely to be too expensive to be worthwhile, even ignoring morality issues.)
Also, since you are incentivising improvement but not disincentivizing regression, prisons who are below-average are encouraged to try high-volatility reforms even if they would yield negative expected improvement. For example, if a reform has a 20% chance of making things much better but a 80% chance of making things equally worse, it is still a good business decision (since the latter consequence does not carry any costs).
The incentive to try “high volatility” methods seems like an advantage; if many prisons try them, 20% of them would succeed, and we’d learn how to rehabilitate better.
Yep. Concretely, if you take one year to decide that each negative reform has been negative, the 20-80 trade that the OP posts is a net positive to society if you expect the improvement to stay around for 4 years.
Or if they will be replicated by another 20 prisons if they work...
I don’t think that would be a problem. If more people get a good legal defense the system becomes more fair.
But even if you don’t like that, you can set up the rule that the prison doesn’t get the bonus if the prisoner is charged with a crime. You don’t need to tie the bonus to conviction.
I really, really wish policy makers considered possible perverse incentive exploits in this much detail. Though I’m not convinced there is any perfect policy that has zero exploits.
If past criminality is a predictor of future criminality, then it should be included in the state’s predictive model of recidivism, which would fix the predictions. The actual perverse incentive here is for the prisons to reverse-engineer the predicted model, figure out where it’s consistently wrong, and then lobby to incarcerate (relatively) more of those people. Given that (a) data science is not the core competency of prison operators; (b) prisons will make it obvious when they find vulnerabilities in the model; and (c) the model can be re-trained faster than the prison lobbying cycle, it doesn’t seem like this perverse incentive is actually that bad.
(a) Prison operators are not currently incentivized to be experts in data science (b) Why? And will that fix things? There are plenty of examples of industries taking advantage of vulnerabilities, without those vulnerabilities being fixed. (c) How will it be retrained? Will there be a “We should retrain the model” lobby group, and will it act faster than the prison lobby?
Perhaps we should have a futures market in recidivism. When a prison gets a new prisoner, they buy the associated future at the market rate, and once the prisoner has been out of prison sufficiently long without committing further crimes, the prison can redeem the future. And, of course, there would be laws against prisons shorting their own prisoners.
re: futures market in recidivism—http://freakonomics.com/2014/01/24/reducing-recidivism-through-incentives/
Your argument assumes that the algorithm and the prisons have access to the same data. This need not be the case—in particular, if a prison bribes a judge to over-convict, the algorithm will be (incorrectly) relying on said conviction as data, skewing the predicted recidivism measure.
That said, the perverse incentive you mentioned is absolutely in play as well.
Yes, I glossed over the possibility of prisons bribing judges to screw up the data set. That’s because the extremely small influence of marginal data points and the cost of bribing judges would make such a strategy incredibly expensive.