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 slowly.
Systems engineering processes for evolving systems of systems (SoS) are often software-driven and software-intensive. At the same time, SoS have multiple levels of abstraction that correspond to the various levels in the SoS hierarchy where these SoS engineering processes take place. Multiple levels of management make it difficult to capture the actual state and relative value of work in these kinds of environments. The Kanban-based scheduling system (KSS) applies lean concepts to coordinate work queues to better to address these issues. The motivation to apply agile methodologies in multi-organizational multi-level environments is based on lean principles that encourage increased visibility of work in progress, limited work in progress, and identification of issues causing blocked work. Current research is focused on formulating the KSS principles and estimating expected performance of the KSS. This paper describes the KSS work flow, Kanban scheduling principles and a simulation model designed to estimate the KSS performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.