destroying drug cartels with mathematical modeling
Posted by Laura McLay on December 3, 2012
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).