Tag Archives: politics

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:


forecasting the Presidential election using regression, simulation, or dynamic programming

Almost a year ago, I wrote a post entitled “13 reasons why Obama will be reelected in one year.” This post uses Lichtman’s model for predicting the Presidential election way ahead of time using 13 equally weighted “keys” – macro-level predictors. Now that we are closer to the election, Lichtman’s method offers less insight, since it ignores the specific candidates (well, except for their charisma), the polls, and the specific outcomes from each state. At this point in the election cycle, knowing which way Florida, for example, will fall is important for understanding who will win.  Thus, we need to look at specific state outcomes, since the next President needs to be the one who gets at least 271 electoral votes, not the one who wins the popular vote.

With less than two months until the election, it’s worth discussing two models for forecasting the election:

  1. Nate Silver’s model on fivethirtyeight
  2. Sheldon Jacobson’s model (Election analytics)

In this post, I am going to compare the models and their insights.

Nate Silver [website link]:

Nate Silver’s model develops predictions for each state based on polling data. He adjusts for different state polls applying a “regression analysis that compares the results of different polling firms’ surveys in the same state.” The model then adjusts for “universal factors” such as the economy and state-specific issues, although Silver’s discussion was a bit sketchy here–it appears to be a constructed scale that is used in a regression model. It appears that Silver is using logistic regression based on some of his other models. Here is a brief description of what goes into his models:

The model creates an economic index by combining seven frequently updated economic indicators. These factors include the four major economic components that economists often use to date recessions: job growth (as measured by nonfarm payrolls), personal incomeindustrial production, and consumption. The fifth factor is inflation, as measured by changes in theConsumer Price Index. The sixth and seventh factors are more forward looking: the change in the S&P 500 stock market index, and the consensus forecast of gross domestic product growth over the next two economic quarters, as taken from the median of The Wall Street Journal’s monthly forecasting panel.

Nate Silver’s methodology is here and here. It is worth noting that Silver’s forecasts are for election day.

Sheldon Jacobson and co-authors [website link]

This model also develops predictions for each state based on polling data. Here, Jacobson and his collaborators use Bayesian estimators to estimate the outcomes for each state.  A state’s voting history is used for it’s prior. State polling data (from Real Clear Politics) is used to estimate the posterior. In each poll, there are undecided voters. Five scenarios are used to allocate the undecided voters from a neutral outcomes to strong Republican or Democrat showings. Dynamic programming is used to compute the probability that each candidate would win under the five scenarios for allocating undecided votes. It is worth noting that Jacobson’s method indicates the Presidential election if it is held now; it doesn’t make adjustments for forecasting into the future.

The Jacobson et al. methodology is outlined here and the longer paper is here.

Comparison and contrast:

One of the main differences is that Silver relies on regression whereas Jacobson uses Bayesian estimators. Silver uses polling data as well as external variables (see above) as variables within his model whereas Jacobson relies on polling data and the allocation of undecided voters.

Once models exist for state results, they have to be combined to predict the election outcome. Here, Silver relies on simulation whereas Jacobson relies on dynamic programming. Silver’s simulations appear to simulate his regression models and potentially exogenous factors. Both the simulation and dynamic programming approaches model inter-state interactions that do not appear to be independent.

Another difference is that Silver forecasts the vote on Election Day whereas Jacobson predicts the outcome if the race were held today (although Silver also provides a “now”-cast). To do so, Silver adjusts for post-convention bounces and for the conservative sway that occurs right before the election:


This is interesting, because it implies that Silver double counts the economy (the economy influences voters who are captured by the polls). I’m not suggesting that this is a bad idea, since I blogged about how all forecasting models stress the importance of the economy in Presidential elections. It is worth noting that Silver’s “now”-cast is close to Jacobson’s prediction (98% vs. 100% as of 10/1)

Silver makes several adjustments to his model, not relying solely on poll data. The economic index mentioned earlier is one of these adjustments. Others are the post-convention bounces (those have both been weighed out by now). While Silver appears to do this well, the underlying assumption is that what worked in the past is relevant for the election today.  This is probably a good assumption as long as we don’t go too far in the past. This election seems to have a few “firsts,” which suggests that the distant past may not be the best guide. For example, the economy has been terrible: this is the first time that the incumbent appears to be heading toward reelection under this condition.

Both models rely on good polls for predicting voter turnout. The polls in recent months have been conducted on a “likely voter basis,” From what I’ve read, this is the hardest part of making a prediction. The intuition is that it’s easy to make a poll, but it’s harder to predict how this will translate into votes. Silver explains why this issue is important in response to a CNN poll:

Among registered voters, [Mr. Obama] led Mitt Romney by nine percentage points, with 52 percent of the vote to Mr. Romney’s 43 percent. However, Mr. Obama led by just two percentage points, 49 to 47, when CNN applied its likely voter screen to the survey.

Thus, the race is a lot closer when looking at likely voters. Polling is a complex science, but those who are experts suggest that the race is closer than polls indicate.

Jacobson’s model overwhelmingly predicts that Obama will be reelected, which is in stark contrast to other models that give Romney a 20-30% chance of winning as of 9/16 and a ~15% of winning today (10/1). Jacobson’s model predicted an Obama landslide in 2008, which occurred. The landslide this time around seems to be due to a larger number of “safe” votes for Obama in “blue” states (see the image below). Romney has to win many battleground states to win the election. The odds of Romney winning nearly all of the battleground states necessary to win is ~0% (according to Jacobson as of 9/30). This is quite a bold prediction, but it appears to rely on state polls that are accurately calibrated for voter turnout. To address this, Jacobson uses his five scenarios that suggest that even with a strong conservative showing, Romney has little chance of winning.  Silver and InTrade predict a somewhat closer race, but Obama is still the clear favorite  (e.g., Intrade shows that Romney has a 24.1% of winning as of 10/1) .

Additional reading:

Special thanks to the two political junkies who gave me feedback draft of this blog: Matt Saltzman and my husband Court.

Sheldon Jacobson’s election analytics predictions as of 9/16

Who will be the Republican nominee?

The race for the Republican Presidential nomination has changed so much in the past week that it is hard to keep up. I enjoy reading Nate Silver’s NY Times blog when I have a chance. A week ago (Jan 16) he wrote a post entitled “National Polls Suggest Romney is Overwhelming Favorite for GOP Nomination, where he noted that Romney had a 19 point lead in the polls. He wrote


Silver compared the average size of the lead following the New Hampshire primary across the past 20+ years of Presidential campaigns. He sorted the results according to decreasing “Size of Lead” the top candidate had in the polls. The image below is from Silver’s blog, where it suggests that Romney has this race all but wrapped up.

It looks almost impossible for Romney to blow it. I stopped following the election news until Gingrich surged ahead and the recount in Iowa led to Santorum winning the caucus.

A mere week later, it looks like Romney’s campaign is in serious trouble. Today (Jan 23), Silver wrote a post entitled “Some Signs GOP Establish Backing Romney is Tenuous.”  His forecasting model for the Florida primary on January 31 now predicts that Newt Gingrich has an 81% chance of winning. This is largely because Silver weighs “momentum” in his model, which Gingrich has in spades.

Two months ago, I blogged about how Obama will win the election next year. I was only half-serious about my prediction. Although the model seems to work, it is based on historical trends that may not sway voters today. Plus, I had no idea who the Republican nominee would be. Despite my prediction, I certainly envisioned a tight race that Obama could lose. Not so much these days.

A lot has changed in the past week (and certainly in the past two months!)

My question is, what models are useful for making predictions in the Republican race? Will the issue of “electability” ever become important to primary voters?


community based operations research



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optimizing school bus routes








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Seeing as how tomorrow is election day, I rounded up a few election-related OR posts.  Don’t forget to vote!

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