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What statistical techniques are most suited for studying (socio-technical) complex adaptive systems? (preferably ones suitable for a non statistician social scientist.

This is in concert with agent based modeling etc.

I was going with bayesian inference and nonparametrics due to its flexibility, quantification of uncertainty etc , but am scared off by the following line of discussion: 1, 2, 3.

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  • $\begingroup$ This is too unclear a question to be understandable by most readers of the forum. Could you try to make it more precise or more focussed? Thank you. $\endgroup$
    – Xi'an
    Nov 25 '16 at 19:13
  • $\begingroup$ Things mentioned in the link here: stat.cmu.edu/~cshalizi/462 "By studying systems with many strongly-interacting components, students will learn how stochastic models can illuminate phenomena beyond the usual linear/Gaussian/independent realm, as well as gain a deeper understanding of why stochastic models work at all." $\endgroup$
    – Bayesquest
    Nov 25 '16 at 19:25
  • $\begingroup$ Also things like heavy tailed distributions. $\endgroup$
    – Bayesquest
    Nov 25 '16 at 19:30
  • $\begingroup$ ps_I am also scared by those discussions! $\endgroup$
    – Xi'an
    Nov 25 '16 at 21:00

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