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Journal of Computer Applications in Archaeology
Article . 2024 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.48550/ar...
Article . 2021
License: CC BY NC SA
Data sources: Datacite
DBLP
Article . 2022
Data sources: DBLP
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The Past as a Stochastic Process

Authors: David H. Wolpert; Michael H. Price; Stefani A. Crabtree; Timothy A. Kohler; Jürgen Jost; James Evans; Peter F. Stadler; +2 Authors

The Past as a Stochastic Process

Abstract

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

Keywords

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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    popularity
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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
gold