Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem

Simulating and predicting planetary-scale techno-social systems poses heavy computational and modeling challenges. The DARPA SocialSim program set the challenge to model the evolution of GitHub, a large collaborative software-development ecosystem, using massive multi-agent simulations. We describe our best performing models and our agent-based simulation framework, which we are currently extending to allow simulating other planetary-scale techno-social systems. The challenge problem measured participant's ability, given 30 months of meta-data on user activity on GitHub, to predict the next months' activity as measured by a broad range of metrics applied to ground truth, using agent-based simulation. The challenge required scaling to a simulation of roughly 3 million agents producing a combined 30 million actions, acting on 6 million repositories with commodity hardware. It was also important to use the data optimally to predict the agent's next moves. We describe the agent framework and the data analysis employed by one of the winning teams in the challenge. Six different agent models were tested based on a variety of machine learning and statistical methods. While no single method proved the most accurate on every metric, the broadly most successful sampled from a stationary probability distribution of actions and repositories for each agent. Two reasons for the success of these agents were their use of a distinct characterization of each agent, and that GitHub users change their behavior relatively slowly

Two decades of active layer thickness monitoring in northeastern Asia

This study summarizes seasonal thawing data collected in different permafrost regions of northeast Asia over the 1995–2018 period. Empirical observations were undertaken under the Circumpolar Active Layer Monitoring (CALM) program at a range of sites across the permafrost landscapes of the Yana-Indigirka and Kolyma lowlands and the Chukotka Peninsula, and supplemented with 10 years of observations from volcanic mountainous areas of the Kamchatka Peninsula. Thaw depth observations, taken using mechanical probing at the end of the thawing season, and ground temperature measurements, were analyzed with respect to air temperatures trends. The data from 24 sites (16 in the Indigirka-Kolyma region, 5 in Chukotka and 3 in Kamchatka) reveal different reactions of the active layer thickness (ALT) to recent changes in atmospheric climate. In general, there is a positive relation between ALT and summer air temperatures. Since the early 2000s positive ALT anomalies (compared with mean data from all sites) prevail in the Kolyma and Chukotka area, with only one alas site showing a negative ALT trend. The only active site in the Kamchatka Mountains shows no significant thaw depth changes over the period of observation. Two other Kamchatka sites were affected during a volcanic eruption in 2012

Massive multi-agent data-driven simulations of the github ecosystem

Simulating and predicting planetary-scale techno-social systems poses heavy computational and modeling challenges. The DARPA SocialSim program set the challenge to model the evolution of GitHub, a large collaborative software-development ecosystem, using massive multiagent simulations. We describe our best performing models and our agent-based simulation framework, which we are currently extending to allow simulating other planetary-scale techno-social systems. The challenge problem measured participant’s ability, given 30 months of metadata on user activity on GitHub, to predict the next months’ activity as measured by a broad range of metrics applied to ground truth, using agent-based simulation. The challenge required scaling to a simulation of roughly 3 million agents producing a combined 30 million actions, acting on 6 million repositories with commodity hardware. It was also important to use the data optimally to predict the agent’s next moves. We describe the agent framework and the data analysis employed by one of the winning teams in the challenge. Six different agent models were tested based on a variety of machine learning and statistical methods. While no single method proved the most accurate on every metric, the broadly most successful sampled from a stationary probability distribution of actions and repositories for each agent. Two reasons for the success of these agents were their use of a distinct characterization of each agent, and that GitHub users change their behavior relatively slowl