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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
  • 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)

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.

Keywords

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.

Notes

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.

References

  1. 1.
    Botero C, Gardner B, Kirby K, Bulbulia J, Gavin M, Gray RD (2014) The ecology of religious beliefs. Proc Natl Acad Sci U S A 111(47):16784–89ADSCrossRefGoogle Scholar
  2. 2.
    White DR (2015) Oscillatory complexity in human history: earth’s asymmetric biogeography and ethnographic data. Conference paper CS-DC’15, session on synthesis of ecological, biological and ethnographic data, ASU, ArizonaGoogle Scholar
  3. 3.
    Murdock GP (1967) Ethnographic atlas. Pittsburgh University Press, PittsburghGoogle Scholar
  4. 4.
    Murdock GP, White DR (1969) Standard cross-cultural sample. Ethnology 8(4):329–369CrossRefGoogle Scholar
  5. 5.
    Dow M, Eff A (2013) Determinants of monogamy. J Soc Evol Cult Psychol 7(3):211–238CrossRefGoogle Scholar
  6. 6.
    Dow MM (2007) Galton’s Problem as multiple network autocorrelation effects. Cross-Cult Res 41:336–363Google Scholar
  7. 7.
    Kelejian HH, Prucha IR (1998) A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. J Real Estate Financ Econ 17(1):99–121CrossRefGoogle Scholar
  8. 8.
    Dow MM, Eff EA (2009) Cultural trait transmission and missing data as sources of bias in cross-cultural survey research: explanations of polygyny re-examined. Cross-Cult Res 43(2):134–151CrossRefGoogle Scholar
  9. 9.
    Sanderson SK, Roberts WW (2008) The evolutionary forms of religious life: a cross-cultural, quantitative analysis. Am Anthropol 110(4):454–466CrossRefGoogle Scholar
  10. 10.
    Scutari M, Denis J-B (2014) Bayesian networks, with examples in R, Texts in statistical science. Chapman & Hall/CRC, LondonzbMATHGoogle Scholar
  11. 11.
    Antonakis J, Bendahan S, Jacquart P, Lalive R (2010) On making causal claims: a review and recommendations. Leadersh Q 21(6):1086–1120CrossRefGoogle Scholar
  12. 12.
    Højsgaard S, Edwards D, Lauritzen S (2012) Graphical models with R. Springer, New YorkCrossRefzbMATHGoogle Scholar
  13. 13.
    Nagarajan R, Scutari M, Lèbre S (2013) Bayesian networks in R with applications in systems biology. Use R!, vol 48. Springer, New YorkGoogle Scholar
  14. 14.
    Scutari M, Nagarajan R (2013) On identifying significant edges in graphical models of molecular networks. Artif Intell Med 57(3):207–217, http://www.aiimjournal.com/article/S0933-3657(12)00154-6/abstract?cc=y=CrossRefGoogle Scholar
  15. 15.
    Cook TD, Shadish WR, Wong VC (2008) Three conditions under which experiments and observational studies produce comparable causal estimates: new findings from within-study comparisons. J Policy Anal Manage 27(4):724–750CrossRefGoogle Scholar
  16. 16.
    Shadish WR, Cook TD (2009) The renaissance of field experimentation in evaluating interventions. Annu Rev Psychol 60:607–629CrossRefGoogle Scholar
  17. 17.
    Thistlethwaite DL, Campbell DT (1960) Regression-discontinuity analysis: an alternative to the ex post facto experiment. J Educ Psychol 51(6):309–317CrossRefGoogle Scholar
  18. 18.
    Brown JH (1995) Macroecology. University of Chicago Press, ChicagoGoogle Scholar
  19. 19.
    Lomolino MV, Brown JH, Whittaker R, Riddle BR (2010) Biogeography, 4th edn. Sinauer Associates, SunderlandGoogle Scholar
  20. 20.
    Harcourt A (2012) Human biogeography. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  21. 21.
    Antonakis J, Bendahan S, Jacquart P, Lalive R (2014) Causality and endogeneity: problems and solutions. In: Day DV (ed) The Oxford handbook of leadership and organizations. Oxford University Press, New York, pp 93–117Google Scholar
  22. 22.
    Dow MM, Anthon Eff E (2009) Multiple imputation of missing data in cross-cultural samples. Cross-Cult Res 43(3):206–229CrossRefGoogle Scholar
  23. 23.
    Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70MathSciNetzbMATHGoogle Scholar

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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