
arXiv: 2112.05876
Historical processes manifest remarkable diversity. Nevertheless, scholars have long attempted to identify patterns and categorize historical actors and influences with some success. A stochastic process framework provides a structured approach for the analysis of large historical datasets that allows for detection of sometimes surprising patterns, identification of relevant causal actors both endogenous and exogenous to the process, and comparison between different historical cases. The combination of data, analytical tools and the organizing theoretical framework of stochastic processes complements traditional narrative approaches in history and archaeology.
20 pages, 4 figures
FOS: Computer and information sciences, Computer Science - Machine Learning, historical trajectories, Econometrics (econ.EM), QA75.5-76.95, computational history, time series data sets, Statistics - Applications, Machine Learning (cs.LG), FOS: Economics and business, Archaeology, Electronic computers. Computer science, stochastic processes, Applications (stat.AP), social evolution, CC1-960, Economics - Econometrics
FOS: Computer and information sciences, Computer Science - Machine Learning, historical trajectories, Econometrics (econ.EM), QA75.5-76.95, computational history, time series data sets, Statistics - Applications, Machine Learning (cs.LG), FOS: Economics and business, Archaeology, Electronic computers. Computer science, stochastic processes, Applications (stat.AP), social evolution, CC1-960, Economics - Econometrics
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