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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

  • Conference paper
  • First Online:
First Complex Systems Digital Campus World E-Conference 2015

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

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 http://intersci.ss.uci.edu (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.

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Notes

  1. 1.

    This was a feature of a 2014 publication of the PNAS predicting moral god religion using the data of the Ethnographic Atlas [3]. The direction of this article is to explore the use of Bayesian learning graphs, in an early stage of analysis, using the more extensive dataset of the Standard Cross-Cultural Sample [4]. Our variables SuperjhWriting and AnimXbwealth substitute for the PNAS variables; in doing so they do not multiply but rather distinguish a dominant ecological region of pastoralism, associated with Islam as a major Moral God religion from the large states and empires present in both the Islamic and the Christian regions of West Eurasia and North Africa.

  2. 2.

    The Wy variable is thus intended to be endogenous by definition.

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Acknowledgments

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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Authors and Affiliations

  1. University of California, Irvine, Irvine, CA, USA

    Douglas White & Tolga Oztan

  2. San Diego Supercomputer Center, University of California, San Diego, San Diego, CA, USA

    Paul Rodriguez

  3. Argonne National Laboratory, Lemont, IL, USA

    Eric Blau, Stuart Martin, Lukasz Lacinski & Thomas Uram

  4. Xiamen University, Xiamen, Fujian, China

    Feng Ren

  5. Carnegie Library of Pittsburgh, Pittsburgh, PA, USA

    Wesley Roberts

Authors
  1. Douglas White
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  2. Paul Rodriguez
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  3. Eric Blau
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  4. Stuart Martin
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  5. Lukasz Lacinski
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  6. Thomas Uram
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  7. Feng Ren
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  8. Wesley Roberts
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  9. Tolga Oztan
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Corresponding author

Correspondence to Douglas White .

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Editors and Affiliations

  1. Paris, France

    Paul Bourgine

  2. Univ de Strasbourg, Strasbourg, France

    Pierre Collet

  3. ECAM Strasbourg-Europe, Schiltigheim, France

    Pierre Parrend

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White, D. et al. (2017). Bayesian Causalities, Mappings, and Phylogenies: A Social Science Gateway for Modeling Ethnographic, Archaeological, Historical Ecological, and Biological Variables. In: Bourgine, P., Collet, P., Parrend, P. (eds) First Complex Systems Digital Campus World E-Conference 2015. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-45901-1_9

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