Human Life Histories as Dynamic Networks: Using Network Analysis to Conceptualize and Analyze Life History Data

Abstract

The examination of multiple life history indicators is essential to evolutionary sciences. However, the statistical analysis of life history parameters’ covariation is not apparently clear, due to the statistical limitations of “classic” procedures, like Factor Analysis, and conceptual problems in interpreting covariation between life history indicators as latent factors. Here, we propose that Network Analysis represents a promising framework for the exploration of life history parameters. First, we briefly describe the following basic metric of Network Analysis: nodes, edges, proximities, clustering, centrality indices, and small-world estimations. Next, we show the implementation of Network Analysis using the empirical set of life history variables as an example (N = 460). We showed that Network Analysis provides the following: (1) optimal level of information—higher than factor analysis and lower than correlation analysis; (2) findings that are in accordance with the existing life history data; (3) the estimation of age at first birth as the central node in the network; (4) dynamic view of life history events which can represent a solid basis for causal life history models. In sum, Network Analysis shows high potential both for conceptualizing life history pathways as dynamic networks and for statistical analysis of the covariation between the life history indicators.

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References

  1. Barban, N., Jansen, R., De Vlaming, R., Vaez, A., Mandemakers, J. J., Tropf, F. C., et al. (2016). Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nature Genetics, 48, 1462–1472. https://doi.org/10.1038/ng.3698.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Blom, G. (1958). Statistical estimates and transformed beta-variables. NY: Wiley.

  3. Bollen, K., & Lennox, R. (1991). Conventional wisdom on measurement: a structural equation perspective. Psychological Bulletin, 110(2), 305. https://psycnet.apa.org/doi/10.1037/0033-2909.110.2.305–314.

    Article  Google Scholar 

  4. Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27, 55–71. https://doi.org/10.1016/j.socnet.2004.11.008.

    Article  Google Scholar 

  5. Borsboom, D., Fried, E. I., Epskamp, S., Waldorp, L. J., van Borkulo, C. D., van der Maas, H. L. J., & Cramer, A. O. J. (2017). False alarm? A comprehensive reanalysis of “Evidence that psychopathology symptom networks have limited replicability” by Forbes, Wright, Markon, and Krueger (2017). Journal of Abnormal Psychology, 126(7), 989–999 https://psycnet.apa.org/doi/10.1037/abn0000306.

    Article  Google Scholar 

  6. Chisholm, J. S., Quinlivan, J. A., Petersen, R. W., & Coall, D. A. (2005). Early stress predicts age at menarche and first birth, adult attachment, and expected lifespan. Human Nature, 16(3), 233–265. https://doi.org/10.1007/s12110-005-1009-0.

  7. Coall, D. A., & Hertwig, R. (2010). Grandparental investment: past, present, and future. Behavioral and Brain Sciences, 33, 1–19. https://doi.org/10.1017/S0140525X09991105.

    Article  Google Scholar 

  8. Copping, L. T., Campbell, A., & Muncer, S. (2014). Psychometrics and life history strategy: the structure and validity of the high K strategy scale. Evolutionary Psychology, 12, 200–222. https://doi.org/10.1177/147470491401200115.

    Article  PubMed  Google Scholar 

  9. Copping, L. T., Campbell, A., Muncer, S., & Richardson, G. B. (2017). The psychometric evaluation of human life histories: a reply to Figueredo, Cabeza de Baca, Black, Garcia, Fernandes, Wolf, and Woodley (2015). Evolutionary Psychology, 15, 1–14. https://doi.org/10.1177/1474704916663727.

    Article  Google Scholar 

  10. Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. (2015). State of the art personality research: a tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13–29. https://doi.org/10.1016/j.jrp.2014.07.003.

    Article  Google Scholar 

  11. Croft, D. P., James, R., & Krause, J. (2008). Exploring animal social networks. Princeton: Princeton University Press.

    Google Scholar 

  12. Dalege, J., Borsboom, D., van Harreveld, F., & van der Maas, H. L. (2017). Network analysis on attitudes: a brief tutorial. Social Psychological and Personality Science, 8, 528–537. https://doi.org/10.1177/1948550617709827.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Day, F. R., Helgason, H., Chasman, D. I., Rose, L. M., Loh, P. R., Scott, R. A., et al. (2016). Physical and neurobehavioral determinants of reproductive onset and success. Nature Genetics, 48, 617–623. https://doi.org/10.1038/ng.3551.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Del Giudice, M. (2019). Rethinking the fast-slow continuum of individual differences. PsyArXiv. August 29. https://doi.org/10.31234/osf.io/4uhz8.

  15. Del Giudice, M., Gangestad, S. W., & Kaplan, H. S. (2015). Life history theory and evolutionary psychology. In D. M. Buss (Ed.), The handbook of evolutionary psychology—vol 1: foundations (2nd ed., pp. 88–114). New York: Wiley.

    Google Scholar 

  16. Dunkel, C. S., Mathes, E. W., Kesselring, S. N., Decker, M. L., & Kelts, D. J. (2015). Parenting influence on the development of life history strategy. Evolution and Human Behavior, 36, 374–378. https://doi.org/10.1016/j.evolhumbehav.2015.02.006.

    Article  Google Scholar 

  17. Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18.

    Article  Google Scholar 

  18. Epskamp, S., Kruis, J., & Marsman, M. (2017a). Estimating psychopathological networks: be careful what you wish for. PLoS One, 12(6), e0179891. https://doi.org/10.1371/journal.pone.0179891.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017b). Generalized network psychometrics: combining network and latent variable models. Psychometrika, 82(4), 904–927. https://doi.org/10.1007/s11336-017-9557-x.

    Article  PubMed  Google Scholar 

  20. Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: a tutorial paper. Behavior Research Methods, 50, 195–212. https://doi.org/10.3758/s13428-017-0862-1.

    Article  PubMed  Google Scholar 

  21. Fan, J., Feng, Y., & Wu, Y. (2009). Network exploration via the adaptive LASSO and SCAD penalties. The Annals of Applied Statistics, 3, 521–541. https://doi.org/10.1214/08-AOAS215.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Fernández-Rhodes, L., Malinowski, J. R., Wang, Y., Tao, R., Pankratz, N., Jeff, J. M., et al. (2018). The genetic underpinnings of variation in ages at menarche and natural menopause among women from the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) study: a trans-ethnic meta-analysis. PLoS One, 13, e0200486. https://doi.org/10.1371/journal.pone.0200486.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Figueredo, A. J., Vásquez, G., Brumbach, B. H., & Schneider, S. M. (2007). The K-factor, covitality, and personality. Human Nature, 18, 47–73. https://doi.org/10.1007/BF02820846.

    Article  PubMed  Google Scholar 

  24. Figueredo, A. J., de Baca, T. C., & Woodley, M. A. (2013). The measurement of human life history strategy. Personality and Individual Differences, 55(3), 251–255. https://doi.org/10.1016/j.paid.2012.04.033.

    Article  Google Scholar 

  25. Figueredo, A. J., Cabeza de Baca, T., Black, C. J., Garcia, R. A., Fernandes, H. B. F., Wolf, P. S. A., & Woodley, A. (2015). Methodologically sound: evaluating the psychometric approach to the assessment of human life history. Evolutionary Psychology, 13, 299–338. https://doi.org/10.1177/147470491501300202.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Figueredo, A. J., Garcia, R. A., Menke, J. M., Jacobs, W. J., Gladden, P. R., Bianchi, J., et al. (2017). The K-SF-42: a new short form of the Arizona life history battery. Evolutionary Psychology, 15(1), 1474704916676276. https://doi.org/10.1177/1474704916676276.

    Article  PubMed  Google Scholar 

  27. Forbes, M. K., Wright, A. G., Markon, K. E., & Krueger, R. F. (2017). Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology, 126(7), 969–988 https://psycnet.apa.org/doi/10.1037/abn0000276.

    Article  Google Scholar 

  28. Fried, E. I., Eidhof, M. B., Palic, S., Costantini, G., Huisman-van Dijk, H. M., Bockting, C. L., et al. (2018). Replicability and generalizability of posttraumatic stress disorder (PTSD) networks: a cross-cultural multisite study of PTSD symptoms in four trauma patient samples. Clinical Psychological Science, 6(3), 335–351. https://doi.org/10.1177/2167702617745092.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432–441. https://doi.org/10.1093/biostatistics/kxm045.

    Article  PubMed  Google Scholar 

  30. Gagnon, A., Smith, K. R., Tremblay, M., Vézina, H., Paré, P. P., & Desjardins, B. (2009). Is there a trade-off between fertility and longevity? A comparative study of women from three large historical databases accounting for mortality selection. American Journal of Human Biology: The Official Journal of the Human Biology Association, 21, 533–540. https://doi.org/10.1002/ajhb.20893.

    Article  Google Scholar 

  31. Gillespie, D. O., Russell, A. F., & Lummaa, V. (2008). When fecundity does not equal fitness: evidence of an offspring quantity versus quality trade-off in pre-industrial humans. Proceedings of the Royal Society of London B: Biological Sciences, 275, 713–722. https://doi.org/10.1098/rspb.2007.1000.

    Article  Google Scholar 

  32. Giosan, C. (2006). High-K strategy scale: a measure of the high-K independent criterion of fitness. Evolutionary Psychology, 4, 394–405. https://doi.org/10.1177/147470490600400131.

    Article  Google Scholar 

  33. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424–438. https://doi.org/10.2307/1912791.

    Article  Google Scholar 

  34. Griskevicius, V., Tybur, J. M., Delton, A. W., & Robertson, T. E. (2011). The influence of mortality and socioeconomic status on risk and delayed rewards: a life history theory approach. Journal of Personality and Social Psychology, 100(6), 1015–1026. https://doi.org/10.1037/a0022403.

  35. Gruijters, S. L., & Fleuren, B. P. (2018). Measuring the unmeasurable: the psychometrics of life history strategy. Human Nature, 29, 33–44. https://doi.org/10.1007/s12110-017-9307-x.

    Article  PubMed  Google Scholar 

  36. Hayward, A. D., Nenko, I., & Lummaa, V. (2015). Early-life reproduction is associated with increased mortality risk but enhanced lifetime fitness in pre-industrial humans. Proceedings of the Royal Society B: Biological Sciences, 282, 20143053. https://doi.org/10.1098/rspb.2014.3053.

    Article  PubMed  Google Scholar 

  37. Hevey, D. (2018). Network analysis: a brief overview and tutorial. Health Psychology and Behavioral Medicine, 6, 301–328. https://doi.org/10.1080/21642850.2018.1521283.

    Article  Google Scholar 

  38. Hochberg, Z. E., Gawlik, A., & Walker, R. S. (2011). Evolutionary fitness as a function of pubertal age in 22 subsistence-based traditional societies. International Journal of Pediatric Endocrinology, 2, 1–7. https://doi.org/10.1186/1687-9856-2011-2.

    Article  Google Scholar 

  39. Humphries, M. D., & Gurney, K. (2008). Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One, 3, e0002051. https://doi.org/10.1371/journal.pone.0002051.

    Article  PubMed  Google Scholar 

  40. Hunt, J., & Hodgson, D. J. (2010). What is fitness and how do we measure it? In D. F. Westneat & C. W. Fox (Eds.), Evolutionary behavioural ecology (pp. 46–71). Oxford: Oxford University Press.

    Google Scholar 

  41. Jasienska, G., Bribiescas, R. G., Furberg, A. S., Helle, S., & Núñez-de la Mora, A. (2017). Human reproduction and health: an evolutionary perspective. The Lancet, 390, 510–520. https://doi.org/10.1016/S0140-6736(17)30573-1.

    Article  Google Scholar 

  42. Kirk, K. M., Blomberg, S. P., Duffy, D. L., Heath, A. C., Owens, I. P., & Martin, N. G. (2001). Natural selection and quantitative genetics of life-history traits in western women: a twin study. Evolution, 55, 423–435. https://doi.org/10.1111/j.0014-3820.2001.tb01304.x.

    Article  PubMed  Google Scholar 

  43. Knežević, G. (2003). Koreni amoralnosti [The roots of amorality]. Beograd: Institut za kriminološka i sociološka istraživanja, Institut za psihologiju.

    Google Scholar 

  44. Kogan, S. M., Cho, J., Simons, L. G., Allen, K. A., Beach, S. R., Simons, R. L., & Gibbons, F. X. (2015). Pubertal timing and sexual risk behaviors among rural African American male youth: testing a model based on life history theory. Archives of Sexual Behavior, 44, 609–618. https://doi.org/10.1007/s10508-014-0410-3.

    Article  PubMed  Google Scholar 

  45. Kramer, N., Schäfer, J., & Boulesteix, A. L. (2009). Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics, 10, 384. https://doi.org/10.1186/1471-2105-10-384.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Krause, J., & Ruxton, G. (2010). Important topics in group living. In T. Székely, A. J. Moore, & J. Komdeur (Eds.), Social behaviour: genes, ecology and evolution (pp. 203–225). Cambridge University Press.

  47. Landherr, A., Friedl, B., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business & Information Systems Engineering, 2(6), 371–385. https://doi.org/10.1007/s12599-010-0127-3.

    Article  Google Scholar 

  48. Liu, J., & Lummaa, V. (2011). Age at first reproduction and probability of reproductive failure in women. Evolution and Human Behavior, 32, 433–443. https://doi.org/10.1016/j.evolhumbehav.2010.10.007.

    Article  Google Scholar 

  49. Međedović, J. (2018). Exploring the links between psychopathy and life history in a sample of college females: a behavioral ecological approach. Evolutionary Psychological Science, 4, 466–473. https://doi.org/10.1007/s40806-018-0157-5.

    Article  Google Scholar 

  50. Međedović, J. (2019). Harsh environment facilitates psychopathy's involvement in mating-parenting trade-off. Personality and Individual Differences, 139, 235–240. https://doi.org/10.1016/j.paid.2018.11.034.

    Article  Google Scholar 

  51. Međedović, J. (2019a). Life history in a postconflict society. Human Nature, 30, 59–70. https://doi.org/10.1007/s12110-018-09336-y.

    Article  PubMed  Google Scholar 

  52. Međedović, J. (2019b). Examining the link between religiousness and fitness in a behavioural ecological framework. Journal of Biosocial Science, advanced online publication, 1–12. https://doi.org/10.1017/S0021932019000774.

  53. Međedović, J. (in press). On the incongruence between psychometric and psychosocial-biodemographic measures of life history. Human Nature.

  54. Meij, J. J., Van Bodegom, D., Ziem, J. B., Amankwa, J., Polderman, A. M., Kirkwood, T. B. L., et al. (2009). Quality–quantity trade-off of human offspring under adverse environmental conditions. Journal of Evolutionary Biology, 22, 1014–1023. https://doi.org/10.1111/j.1420-9101.2009.01713.x.

    Article  PubMed  Google Scholar 

  55. Mell, H., Safra, L., Algan, Y., Baumard, N., & Chevallier, C. (2018). Childhood environmental harshness predicts coordinated health and reproductive strategies: a cross-sectional study of a nationally representative sample from France. Evolution and Human Behavior, 39, 1–8. https://doi.org/10.1016/j.evolhumbehav.2017.08.006.

    Article  Google Scholar 

  56. Miller, W. B., Bard, D. E., Pasta, D. J., & Rodgers, J. L. (2010a). Biodemographic modeling of the links between fertility motivation and fertility outcomes in the NLSY79. Demography, 47, 393–414. https://doi.org/10.1353/dem.0.0107.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Miller, W. B., Rodgers, J. L., & Pasta, D. J. (2010b). Fertility motivations of youth predict later fertility outcomes: a prospective analysis of National Longitudinal Survey of Youth data. Biodemography and Social Biology, 56, 1–23. https://doi.org/10.1080/19485561003709131.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Morgan, S. P., & Rackin, H. (2010). The correspondence between fertility intentions and behavior in the United States. Population and Development Review, 36, 91–118. https://doi.org/10.1111/j.1728-4457.2010.00319.x.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Neel, R., Kenrick, D. T., White, A. E., & Neuberg, S. L. (2016). Individual differences in fundamental social motives. Journal of Personality and Social Psychology, 110, 887–907. https://doi.org/10.1037/pspp0000068.

    Article  PubMed  Google Scholar 

  60. Nettle, D., & Frankenhuis, W. E. (in press). Life history theory in psychology and evolutionary biology: one research programme or two? Philosophical Transactions of the Royal Society, B.

  61. Richardson, G. B., Sanning, B. K., Lai, M. H., Copping, L. T., Hardesty, P. H., & Kruger, D. J. (2017). On the psychometric study of human life history strategies: state of the science and evidence of two independent dimensions. Evolutionary Psychology, 15(1), 1474704916666840. https://doi.org/10.1177/1474704916666840.

    Article  PubMed  Google Scholar 

  62. Rodrigues, F. A. (2019). Network centrality: an introduction. In E. Macau (Ed.), A mathematical modeling approach from nonlinear dynamics to complex systems. Nonlinear systems and complexity (Vol. 22, pp. 177–196). Cham: Springer.

    Google Scholar 

  63. Sade, D. S. (1972). Sociometrics of Macaca mulatta: linkages and cliques in grooming matrices. Folia Primatologica, 18, 196–223. https://doi.org/10.1159/000155480.

    Article  Google Scholar 

  64. Sanjak, J. S., Sidorenko, J., Robinson, M. R., Thornton, K. R., & Visscher, P. M. (2018). Evidence of directional and stabilizing selection in contemporary humans. Proceedings of the National Academy of Sciences, 115, 151–156. https://doi.org/10.1073/pnas.1707227114.

    Article  Google Scholar 

  65. Sear, R. (2020). Do human ‘life history strategies’ exist? OSF Preprints, February 21, 2020. https://doi.org/10.31219/osf.io/hjezb.

  66. Sheppard, P., Pearce, M. S., & Sear, R. (2016). How does childhood socioeconomic hardship affect reproductive strategy? Pathways of development. American Journal of Human Biology, 28, 356–363. https://doi.org/10.1002/ajhb.22793.

    Article  PubMed  Google Scholar 

  67. Stearns, S. C., & Rodrigues, A. M. (in press). On the use of “life history theory” in evolutionary psychology. Evolution and Human Behavior. https://doi.org/10.1016/j.evolhumbehav.2020.02.001.

  68. Stewart-Williams, S., & Thomas, A. G. (2013). The ape that thought it was a peacock: does evolutionary psychology exaggerate human sex differences? Psychological Inquiry, 24, 137–168. https://doi.org/10.1080/1047840X.2013.804899.

    Article  Google Scholar 

  69. Tropf, F. C., Stulp, G., Barban, N., Visscher, P. M., Yang, J., Snieder, H., & Mills, M. C. (2015). Human fertility, molecular genetics, and natural selection in modern societies. PLoS One, 10, e0126821. https://doi.org/10.1371/journal.pone.0126821.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442. https://doi.org/10.1038/30918.

    Article  PubMed  Google Scholar 

  71. Webster, G. D., Graber, J. A., Gesselman, A. N., Crosier, B. S., & Schember, T. O. (2014). A life history theory of father absence and menarche: a meta-analysis. Evolutionary Psychology, 12(2), 147470491401200202. https://doi.org/10.1177/147470491401200202.

  72. Xu, Y., Norton, S., & Rahman, Q. (2018). Early life conditions, reproductive and sexuality-related life history outcomes among human males: a systematic review and meta-analysis. Evolution and Human Behavior, 39, 40–51. https://doi.org/10.1016/j.evolhumbehav.2017.08.005.

    Article  Google Scholar 

  73. Zietsch, B. P., & Sidari, M. J. (in press). A critique of life history approaches to human trait covariation. Evolution and Human Behavior. https://doi.org/10.1016/j.evolhumbehav.2019.05.007.

  74. Zietsch, B. P., Kuja-Halkola, R., Walum, H., & Verweij, K. J. (2014). Perfect genetic correlation between number of offspring and grandoffspring in an industrialized human population. Proceedings of the National Academy of Sciences, 111(3), 1032–1036. https://doi.org/10.1073/pnas.1310058111.

    Article  Google Scholar 

  75. Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101, 1418–1429. https://doi.org/10.1198/016214506000000735.

    Article  Google Scholar 

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Acknowledgments

The work on this manuscript was financed by the Serbian Ministry of Education, Science and Technological Development in the project 47011, realized by the Institute of Criminological and Sociological Research. The author wishes to thank Milan Jordanov for his help in performing the Network analysis. He also expresses his gratitude to Marco Del Giudice, the anonymous reviewers, and the handling editor for their useful comments on the previous version of the manuscript.

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Međedović, J. Human Life Histories as Dynamic Networks: Using Network Analysis to Conceptualize and Analyze Life History Data. Evolutionary Psychological Science 7, 76–90 (2021). https://doi.org/10.1007/s40806-020-00252-y

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Keywords

  • Life history theory
  • Network analysis
  • Factor analysis
  • Human behavioral ecology