| Autor: |
David H. Wolpert, Michael H. Price, Stefani A. Crabtree, Timothy A. Kohler, Jürgen Jost, James Evans, Peter F. Stadler, Hajime Shimao, Manfred D. Laubichler |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of Computer Applications in Archaeology, Vol 7, Iss 1, Pp 134–152-134–152 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2514-8362 |
| DOI: |
10.5334/jcaa.113 |
| Popis: |
Historical processes manifest remarkable diversity. Nevertheless, scholars have long attempted, with some success, to identify patterns and categorize historical actors and influences. 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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