Comments and other data left on a PDF detailing Homeland Security's proposal to build "mega" detention and processing centers reveal the personnel involved in its creation. A PDF that Department of Homeland Security officials provided to New Hampshire governor Kelly Ayotte's office about a new effort to build "mega" detention and processing centers across the United States contains embedded comments and metadata identifying the people who worked on it. The seemingly accidental exposure of the identities of DHS personnel who crafted Immigration and Customs Enforcement's mega detention center plan lands amid widespread public pushback against the expansion of ICE detention centers and the department's brutal immigration enforcement tactics. Metadata in the document, which concerns ICE's "Detention Reengineering Initiative" (DRI), lists as its author Jonathan Florentino, the director of ICE's Newark, New Jersey, Field Office of Enforcement and Removal Operations. In a note embedded on top of an FAQ question, "What is the average length of stay for the aliens?"

The U.S. Postal Service often gets a bad rap for slow deliveries and budget overruns but lately has been embracing use of an edge artificial intelligence platform across 195 processing centers nationwide. The AI benefits include the ability to track a missing package in a couple of hours instead of several days under a previous routine, said Todd Schimmel, a manager of letter technology at USPS, in a statement. Training the USPS system for computer vision of packages was also vastly simplified with AI capabilities. What might have taken two weeks on a network of servers with 800 CPUs was reduced to 20 minutes on four Nvidia V100 Tensor Core GPUs in a single HPE Apollo 6500 server, Nvidia said. Now, each edge server processes 20 terabytes of images a day from more than 1,000 mail processing machines.

The real world is awash with multi-agent problems that require collective action by self-interested agents, from the routing of packets across a computer network to the management of irrigation systems. Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group. Given appropriate mechanisms describing agent interaction, groups may achieve socially beneficial outcomes, even in the face of short-term selfish incentives. In many cases, collective action problems possess an underlying graph structure, whose topology crucially determines the relationship between local decisions and emergent global effects. Such scenarios have received great attention through the lens of network games. However, this abstraction typically collapses important dimensions, such as geometry and time, relevant to the design of mechanisms promoting cooperation. In parallel work, multi-agent deep reinforcement learning has shown great promise in modelling the emergence of self-organized cooperation in complex gridworld domains. Here we apply this paradigm in graph-structured collective action problems. Using multi-agent deep reinforcement learning, we simulate an agent society for a variety of plausible mechanisms, finding clear transitions between different equilibria over time. We define analytic tools inspired by related literatures to measure the social outcomes, and use these to draw conclusions about the efficacy of different environmental interventions. Our methods have implications for mechanism design in both human and artificial agent systems.