2019/01/01 by Jim Blythe, John Bollenbacher, Di Huang +12 · 12 citations
Computer Science · Decision Sciences · Social Sciences · #Commodity #Data set #Language and cultural evolution #Mobile Crowdsensing and Crowdsourcing #Probability distribution #Range (aeronautics) #Scientific Computing and Data Management #Set (abstract data type) #Statistical model #Variety (cybernetics) #cs.MA #cs.SI
paper · pdf · doi:10.1007/978-3-030-24209-1_1
published in Lecture notes in computer science, 3-15 (Springer Science+Business Media)
openalex publication_date 2019/01/01 · openalex created_date 2019/07/12 · arxiv created 2019/08/15 · arxiv updated 2019/08/16 · openalex updated_date 2026/08/05
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.