Tag Archives: criminal justice

operations research for drug policy and addiction

I enjoyed listening to Jon Caulkins’ Omega Rho lecture at the INFORMS Annual Meeting. The abstract for the talk is:

Operations Research in Service of Drug and Addictions Policy: Lessons from and for the Discipline of Operations Research
Jonathan P.Caulkins
H.Guyford Stever Professorship of Operations Research and Public Policy
Carnegie Mellon University

I am an OR missionary. I have carried our tools and perspectives into the fields of drug policy and addiction. When traveling far afield, one often encounters opportunities to do good by applying what seem to be quite basic precepts back home, and one returns with a deeper understanding of one’s own culture and its strengths and weaknesses. That has certainly been true of my professional tour. I will share success stories – instances in which by virtue of being the only person thinking about an issue from the perspective of a math modeler, I was able to make fundamental contributions by doing analyses that anyone with training in OR would view as quite elementary. I will also try to share some insights into our disciplinary culture. Drug policy, like most policy domains, is inherently interdisciplinary. So I work with scholars from many disciplines. That experience has given me an appreciation of different disciplines’ strengths and limitations when grappling with messy unstructured problems. I firmly believe that diversity is essential to good decision making, including disciplinary diversity. But I am also interested in which disciplines’ graduates are leaders, not just members, of the teams that shape high-level and strategic decision making. I will close with some thoughts about how we might increase our discipline’s “market share” within t! hose leadership roles.

This was an interesting talk about being an OR practitioner and solving real problems. Jon talked about the general principles he uses to influence policies. This involves doing good work, but more importantly, it involves asking good research questions. Jon asks excellent research questions. Jon summarized the impact his answers to these questions have had on policy. Jon’s work modeled drug lifetimes and life cycles using Markov chain models, a feature common to all drug types that could be used to forecast when drugs would go out of favor. He talked about modeling types of users–heavy and light–and the insights one can obtain when considering different classes of users. I enjoyed the discussion on pricing and drug purity, two issues that are often overlooked by decision makers and therefore have impact.

A really great part of the talk was when Jon took on Big Data. He said that in his experience, being the first with any data at all is really important for influencing policy. Many times, public safety leaders make decisions with zero data points or one data point (an anecdote!). Going from 0 to 100 data points can change a policy, going from 100 data points to “Big Data,” not so much.

David Hutton blogged about Jon’s talk on the INFORMS2014 blog [Link].

Earlier posts about Jon Caulkins’ talks:


destroying drug cartels with mathematical modeling

The New Scientist has an article on using network analysis to destroy drug cartels. It’s worth reading [link]

They describe the structure of the network and why taking out the “hubs” can increase crime:

Complexity analysis depicts drugs cartels as a complex network with each member as a node and their interactions as lines between them. Algorithms compute the strength and importance of the connections. At first glance, taking out a central “hub” seems like a good idea. When Colombian drug lord Pablo Escobar was killed in 1993, for example, the Medellin cartel he was in charge of fell apart. But like a hydra, chopping off the head only caused the cartel to splinter into smaller networks. By 1996, 300 “baby cartels” had sprung up in Colombia, says Michael Lawrence of the Waterloo Institute for Complexity and Innovation in Canada, and they are still powerful today. Mexican officials are currently copying the top-down approach, says Lawrence, but he doubts it will work. “Network theory tells us how tenuous the current policy is,” he says.

The Vortex Foundation in Bogota, Columbia offers another approach for targeting anti-drug efforts:

Vortex uses network-analysis algorithms to construct diagrams for court cases that show the interactions between cartel members, governors and law enforcers. These reveal links that are not otherwise visible, what Salcedo-Albaran calls “betweeners” – people who are not well-connected, but serve as a bridge linking two groups. In Mexico and Colombia, these are often police or governors who are paid by the cartels.

“The betweener is the guy who connects the illegal with the legal,” says Salcedo-Albaran. Because many cartels depend on their close ties with the law to operate successfully, removing the betweeners could devastate their operations.

There is a rich history of applying OR to crime problems. Jon Caulkins has applied OR to drug. I like his paper “What Price Data Tell Us About Drug Markets” with Peter Reuter, where he touches on the drug network and hierarchy. The price of illicit drugs varies substantially in time and space. For example, illicit drug prices are lower in the supplier/hub cities as opposed to small cities. Here, the prices are not necessarily a function of the shortest path from supplier to market.

We have already alluded to the fact that there is systematic variation in wholesale prices
between cities, implying that there are poor information flows and/or significant transaction costs
associated with lateral market transactions. Examining spatial variation in retail prices also yields
insights about these markets. Caulkins (1995) found that illicit drug prices within the United
States increase as one moves away from the drug sources and that prices are lower in larger
markets. For cocaine in particular, the data support the notion that cocaine is distributed through
an “urban hierarchy,” in which large cities tend to be “leaders,” with drugs diffusing down through
layers of successively smaller surrounding communities. Points of import, such as New York City,
are at the top of the hierarchy. Large, cosmopolitan cities such as Philadelphia occupy the first tier
below points of import; more regionally oriented cities such as Pittsburgh the second; and smaller
cities the third. Of course drug distribution networks do not always follow such a regimented
pattern; some cocaine is shipped directly to smaller cities from more distant points of import such
as Miami and Houston. Nevertheless, prices show the general pattern of an urban hierarchy. This
is consistent with anecdotal observations but stands in marked contrast to common depictions of
trafficking paths which suggest that drugs more or less follow the shortest path from place of
import to point of retail sale.

There even seems to be systematic variation in prices between different neighborhoods
within one city. As Kleiman (1992) observed, heroin prices are consistently lower in Harlem than
in the Lower East Side, just half an hour away by subway. For example, in data from the 1993
domestic monitor program (DEA, 1994), the mean price per pure gram in East Harlem was
$0.358/mg vs. a mean price of $0.471/mg on the Lower East Side, a difference that is statistically
significant at the 0.05 level.

In his paper “Domestic Geographic Variation in Illicit Drug Prices” in the Journal of Urban Economics, he attributes some of the price variations to incomplete information and economies of scale (ares that produce/process large amounts of drugs can sell it more cheaply).

Related post:


a gun offender registry: everyone wants one but the costs and benefits are a mystery

Prince George County in the Washington DC is weighing a bill requiring a gun offender registry, and a vote is expected in early June. If it passes (and it is expected to do so), the county would allow/require police to monitor gun offenders. I think this is akin to the sex offender registries but for those convicted for gun-related violence.  Prince George County is not the first to pursue a gun registry. Gun registries are a trend in local governments across the nation.

Supposedly, gun-offender registry have had “an impact.” I am not surprised. But they surely come with a cost. The cost in this case is a decreased manpower of already-understaffed police force. No one wants to appear soft on crime, but no one wants to pay for it either. I was surprised that the NRA representative quoted in the article appeared to be the most sensible in his assessment of the tradeoffs:

Andrew Arulanandam, the National Rifle Association’s director of public affairs, said his group advocates vigorous enforcement of existing gun laws rather than the creation of new databases. Running a gun-offender registry, he said, requires taxpayers to foot the bill for computer equipment and IT professionals, and it could take officers off the streets and place them in administrative jobs.

Gun registries could be effective despite the costs. If those on the gun registries are “dangerous,” then monitoring those individuals more closely could be an effective use of police manpower, since it focuses police attention on those most likely to commit dangerous crimes. That could more than make up for the policing that is foregone due to the new responsibilities associated with the registry. But I need to see some research that honestly shows the level of impact that registries could have. The police chief in Prince George does indeed intend to use some of their existing 1400 police officers to monitor the gun registry.

[Deputy Police Chief Craig] Howard said police would initially assign a sergeant and four or five detectives to handle the gun registry, pulling them from other jobs in the department. He said the creation of the computer database could be handled by those who run the department’s sex-offender registry and do other IT work.

The 5-6 officers needed for the registry are less than 1% of the police force. But the current registry level is zero and the system is not in steady-state. The registry will likely grow in time as gun offenders are added, necessitating an increasing number of police officers to monitor the registry. I’m not sure that monitoring the registry in the future will be sustainable.

I am agnostic on gun registries. I am not agnostic about honesty regarding the true cost of gun registries and their impacts. Certainly, different municipalities and counties need to weigh the costs with the rewards and make a decision that they can live with. Previous attempts to curb crime—such as the Three Strikes Laws and minimum sentencing—have lowered crime at an enormous cost. The costs here are associated with an exploding prison population. I suspect that the same might be true for gun registries.


the price of justice

I was enthralled by a New York Times article about how the state of Missouri systematically provides judges cost information when they are deciding how to sentence someone convicted of a serious crime. “For someone convicted of endangering the welfare of a child, for instance, a judge might now learn that a three-year prison sentence would run more than $37,000 while probation would cost $6,770.”

All of these costs and guidelines are the result of the Missouri Sentencing Advisory Commission, which aggregates and summarizes the cost of justice.  It also provides other useful information, such as the expected time served as the recidivism rate (see this newsletter for some examples).  The system doesn’t capture indirect costs, such as the societal costs when someone on probation commits another crime.  But overall, this system makes excellent use of math and statistics aimed at making better decisions.  I could pore over the numbers all day, if I had the time.

With more transparent information, judges can make better information.  It’s win-win, isn’t it?

There are some downsides:

  • Some critics “say that the cost of punishment is an irrelevant consideration when deciding a criminal’s fate and that there is a risk of overlooking the larger social costs of crime.”  I agree that you can’t put a price on being a victim, but at the end of the day, costs and budgets do matter.  Someone has to pay for putting someone behind bars–it doesn’t come cheap.
  • Some judges do not really look at all the cost information.  I suppose we can’t expect everyone to enjoy studying numbers, but on the upside, it does sound like many judges do look at the numbers.
  • “To some, the concept sounds crass, and carries the prospect of unwanted consequences. Might a decision between life in prison and a death sentence be decided some day by price comparison? … Could the costs of various sentences become so widely known as to affect the decisions of jurors?”
  • “Some voiced concern about the ramifications, the methodology — even the price tag of calculating sentencing price tags.”  This is a sticky issue.  Transparency can uncover inequities in the system, and such a system aimed at cutting costs could lead to additional costs to fix any inequities.

Despite the downsides, I am always a fan of using analytical techniques to make better decisions.  Prison reform is one of my favorite topics, although I am the first to admit that I don’t have any good answers.  I am always looking for better numbers to help me shape my views, and I am glad to hear that the Missouri Sentencing Advisory Commission is helping to provide some of those numbers.  But this story is also a cautionary tale to nerds like me:  it is hard for politicians to acknowledge that we live in a world with limited resources, and they might not get so excited about better numbers and transparency.  Sad, but true.

If you were a juror, would you like to have access to sentencing numbers and statistics?

Related posts:


How many people get arrested?

I recently wrote about how OR can be used to determine how employer criminal background checks could be conducted. This is a follow up post.

An excellent article by Carl Bialik in the WSJ summarizes arrest patterns in the United States. It reports that according to a report from the President’s Commission on Law Enforcement and Administration of Justice, 52% of American men will be arrested in their lifetime. This is consistent with the number that Al Blumstein reported in the 1960s.

Both then and now, the researchers who came up with this number were surprised that so many men get arrested. For a law-abiding citizen, it is hard to understand how so many people are arrested. The image from Bialik’s article (displayed below) helps in that regard. Note that the proportion of men that have been arrested (approximately 40%) is lower, since some men have not yet been arrested for the first time yet. The proportion of men who will be arrested in four times higher than the proportion of women that will be arrested.

Bialik explores the 52% claim in greater detail in his blog.


personal choices and crime

In January, Ralph Keeney’s paper called “Personal Decisions are the Leading Cause of Death” made quite a splash. As the title suggests, the article tackled the issue of how our choices affect our health. The paper asserts that “over one million of the 2.4 million deaths in 2000 can be attributed to personal decisions and could have been avoided if readily available alternative choices were made.”

In the same vein, a Marginal Revolution post summarized violent crime statistics. It seems that our personal choices affect our likelihood of being a victim of violent crime. MR reports the rate of victimization for violent crimes (per 1,000 persons aged 12 and over), summarized below. Married men and women have drastically lower rates of victimization than their unmarried counterparts. Interestingly, divorced men and women have drastically higher rates of victimization than their married counterparts. Even more interesting is that these trends appear to be much stronger for women than men. I’m not sure what to make of all of this. The general trends are not so surprising to me, the the strength of these trends (particularly for the divorced) is troubling. Now I know that correlation doesn’t imply causation, but these overwhelming statistics seem to suggest personal choices play a role.

The statistics:

Never Married Males: 45.0
Married Males: 12.3
Divorced or Separated Males: 44.2

Never Married Females: 38.4
Married Females: 10.3
Divorced or Separated Females: 49.4

What do you make of this?

Links:


fighting crime with analytics

Dr. Colleen McCue, president and CEO of MC2 Solutions, gave a talk at VCU entitled “What can Amazon and Wal-Mart teach us about fighting crime in a recession?” She talked about using data mining and analytics tools to not just fight crime, but prevent it.

I really enjoyed McCue’s talk, since she  had some really, really cool examples of how she used analytical tools (data mining, statistics, and a little optimization) to fight and prevent crime.  (I am also a CSI jumkie).

For example, McCue used data mining to determine which armed robberies escalated to an aggravated assault. One challenge was that these events are rare (3% escalate) and data is unreliable, since it is mostly provided by eyewitnesses (If you’ve seen CSI or Law & Order once or twice, this should be no surprise). Most of the useful data is in a narrative, which is hard to process in an automated way. Once this challenge was met, the data were used to proactively located police officers in the riskiest areas (areas that were most likely for a robbery to escalate to an aggravated assault), rather than locate police areas with the highest rate of (non-escalating) robberies.  It turned out that one of the riskiest areas was where suburbanites drive into the city to buy drugs (as one police officer put it), where they were then used as “walking ATMs.”  Some resisted being robbed, and were subsequently shot.

McCue used a similar method to develop a risk-based method for locating and scheduling police officers on New Year’s Eve. Her method reduced random gunfire by 47%, increased gun seizures by 246%, and saved $15K. The key was noting that most of the violence in New Year’s Eve occurs in a two hour window around midnight–and being able to convince decision-makers to radically change shift assignments.

In her talk, McCue noted that most of the time, data analysis confirms what is already known. But sometimes, it identifies new things. For example, Wal-Mart was able to identify that before storms hit people stock up on bottled water and strawberry Pop Tarts. Her approach is similar, with McCue’s Pop Tarts being non-intuitive ways to fight crime. After scouring non-acquaintance rape data, for example, McCue was able to determine that prior property crime offenses (not prior assault or peeping Tom offenses) was the most likely indicator for rape.

The most interesting thing I learned about the talk is about how behavioral analysis should be used in Numb3rs-esque ways to fight crime. For example, segmentation uses behavioral analysis, much like marketing, since it, in effect, targets certain populations. It makes no sense to look at homicide, McCue argues. Instead, she looks at drug-related homicide, manslaughter, etc. McCue developed a segmentation tool to determine motive for homicides, in order to help develop a list of suspects early on. Once a list of likely suspects are formed, the perpetrator can be found. Her model only takes data that would be available early in an investigation, since it is critical to make progress on a case (with 48 hours) before it goes cold. Finding likely suspects is one of the most critical parts of an investigation.  Apparently, behavior models are more accurate than models that rely on the “hard data,” since hard data is unreliable whereas behavior is homogeneous. Being a numbers nerd, this makes me uncomfortable (I could barely type that last sentence), but I impressed with the results.

Related posts: