Bayesian Causalities, Mappings, and Phylogenies: A Social Science Gateway for Modeling Ethnographic, Archaeological, Historical Ecological, and Biological Variables

CS-DC'15 Panel on Synthesis of Ecological, Biological, and Ethnographic Data 9–13
  • Douglas WhiteEmail author
  • Paul Rodriguez
  • Eric Blau
  • Stuart Martin
  • Lukasz Lacinski
  • Thomas Uram
  • Feng Ren
  • Wesley Roberts
  • Tolga Oztan
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Extending the innovative “Def Wy” procedures for modeling evolutionary network effects (Dow, Cross-Cult Res 41:336–363, 2007; Dow and Eff, Cross-Cult Res 43:134–151, 2009; Dow and Eff, Cross-Cult Res 43:206–229, 2009), a Complex Social Science (CoSSci) Gateway was developed to provide complex analyses of ethnographic, archaeological, historical, ecological, and biological datasets with easy open access. Analysis begins with dependent variable y with n observations and X independent and other variables, and imputes missing data for all variates. Several (n × n) W* matrices measure evolutionary network effects such as diffusion or phylogenetic ancestries. W* is row-normalized to sum to 1 and combined to obtain a W, multiplied by X as WX, and allowing X and y multiplication by W:
$$ \overset{.}{W}y={\overset{.}{\alpha}}_0+{\overset{.}{\alpha}}_i\;\left(W{X}_{i=1,\;n}\right). $$
Wy measures the evolutionary autocorrelation portion of y discounting evolutionary effects of propinquity and phylogenetics. Tested for exogeneity (error terms uncorrelated with Wy or independent variables) the two-stage Ordinary Least Squares (OLS) results include measures of independent variable and deep evolutionary autocorrelation predictors. We show how these methods apply to a wide variety of problems in the social sciences to which ecological and biological variables will apply once contributed.


Ordinary Little Square Bayesian Network Ordinary Little Square Regression Structure Learning Conditional Probability Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Douglas R. White thanks the Santa Fe Institute for hosting multiple 1–2 week Causality Working Groups engaging Tolga Oztan, Peter Turchin, Amber Johnson, and many others on this topic in 2010–2014 and to Jürgen Jost and the MPI for Mathematics in the Sciences for hosting of our working group in June 2011. We thank Anthon Eff for his immense work in building the R code prior to and as used in CoSSci.


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Douglas White
    • 1
    Email author
  • Paul Rodriguez
    • 2
  • Eric Blau
    • 3
  • Stuart Martin
    • 3
  • Lukasz Lacinski
    • 3
  • Thomas Uram
    • 3
  • Feng Ren
    • 4
  • Wesley Roberts
    • 5
  • Tolga Oztan
    • 1
  1. 1.University of California, IrvineIrvineUSA
  2. 2.San Diego Supercomputer CenterUniversity of California, San DiegoSan DiegoUSA
  3. 3.Argonne National LaboratoryLemontUSA
  4. 4.Xiamen UniversityXiamenChina
  5. 5.Carnegie Library of PittsburghPittsburghUSA

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