Modeling cocaine traffickers and counterdrug interdiction forces as a complex adaptive system.

Counterdrug interdiction efforts designed to seize or disrupt cocaine shipments between South American source zones and US markets remain a core US "supply side" drug policy and national security strategy. However, despite a long history of US-led interdiction efforts in the Western Hemisphere, cocaine movements to the United States through Central America, or "narco-trafficking," continue to rise. Here, we developed a spatially explicit agent-based model (ABM), called "NarcoLogic," of narco-trafficker operational decision making in response to interdiction forces to investigate the root causes of interdiction ineffectiveness across space and time. The central premise tested was that spatial proliferation and resiliency of narco-trafficking are not a consequence of ineffective interdiction, but rather part and natural consequence of interdiction itself. Model development relied on multiple theoretical perspectives, empirical studies, media reports, and the authors' own years of field research in the region. Parameterization and validation used the best available, authoritative data source for illicit cocaine flows. Despite inherently biased, unreliable, and/or incomplete data of a clandestine phenomenon, the model compellingly reproduced the "cat-and-mouse" dynamic between narco-traffickers and interdiction forces others have qualitatively described. The model produced qualitatively accurate and quantitatively realistic spatial and temporal patterns of cocaine trafficking in response to interdiction events. The NarcoLogic model offers a much-needed, evidence-based tool for the robust assessment of different drug policy scenarios, and their likely impact on trafficker behavior and the many collateral damages associated with the militarized war on drugs.

Assessing population exposure to coastal flooding due to sea level rise.

The exposure of populations to sea-level rise (SLR) is a leading indicator assessing the impact of future climate change on coastal regions. SLR exposes coastal populations to a spectrum of impacts with broad spatial and temporal heterogeneity, but exposure assessments often narrowly define the spatial zone of flooding. Here we show how choice of zone results in differential exposure estimates across space and time. Further, we apply a spatio-temporal flood-modeling approach that integrates across these spatial zones to assess the annual probability of population exposure. We apply our model to the coastal United States to demonstrate a more robust assessment of population exposure to flooding from SLR in any given year. Our results suggest that more explicit decisions regarding spatial zone (and associated temporal implication) will improve adaptation planning and policies by indicating the relative chance and magnitude of coastal populations to be affected by future SLR.

Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen.

Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm's landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity (r = .75, p < 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people's preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.