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In the last few decades, the disaster prevention and management community has engaged in a serious discussion of the differential impact of disasters on vulnerable populations and to question the efficacy of the “command and control” model of disaster response (e.g. Handmer and Dovers, 2007; Cretney, 2016). Emerging from these discussions is a growing recognition of the importance of “social capital” in disaster resilience and the vital role local actors and actions play in disaster response (e.g. Lebel et al., 2006; Norris et al., 2008; Singh-Peterson et al., 2015). Recognition of both these important aspects of disaster prevention and management has led to recommendations that disaster prevention efforts be more intensively directed at building social capital within vulnerable communities and toward increasing local participation in decision-making (e.g. Gil-Rivas and Kilmer, 2016; Kasdan, 2016).
Humans have faced disasters throughout our history, and the past provides examples of both effective and ineffective mechanisms of disaster management. The basic premise of this paper is that the systematic analysis of ancient societies can provide an empirical test of whether the empowerment of local communities promotes resilience to climate-related disasters. While this paper is not unique in using archeological data to examine resilience to climate-related disasters (e.g. Cooper and Sheets, 2012; Fisher et al., 2009; Hegmon et al., 2008; Redman, 2005), it is unique in doing so using “controlled” cross-cultural comparison as its basic methodology. Controlled cross-cultural comparison refers to the comparison of a carefully selected sample of societies from different locations and of different scales and degrees of political complexity in order to identify statistical patterns or relationships that apply across a large range of social diversity.
Using the time-depth available with archeological data, controlled comparison allows one to test whether or not an assumed predictive condition actually precedes its assumed effects; that is, whether a society with the predictive condition empirically changes in the predicted manner over time. An important strength of this approach is that if a predictor variable or set of variables can be identified to explain a condition across a wide range of societies from differing time periods, then that variable or set of variables can be assumed to be generalizable across most human societies, including our own (see also...