Matthew Peeples-Risks and Rewards of Social Networks in the Ancient Southwest

Monthly lecture presented by the Arizona Archaeological and Historical Society December 18k 2023

Archaeological data provide the only direct source of information for exploring the structure and dynamics of social systems beyond the historic record. Not only are archaeologists increasingly able to replicate the findings of other social scientists, we are also beginning to discover patterns in human societies that transcend the time scales typically considered in comparative research. In this talk, I outline the efforts of the one large collaborative research team (cyberSW) over the last 15 years to apply network methods and models toward questions at the intersection of social networks and culture. This research involves the analyses of a massive settlement and material culture database spanning a period of 1,000 years across the U.S. Southwest and the Mexican Northwest. Our work suggests the nature of networks and the risks and rewards associated with network positions are historically contingent and tied to broader trends in political complexity and demographic scale. Such associations are difficult to uncover within a single regional/cultural context, and thus, such large-scale archaeological network studies have considerable potential for revealing comparative insights both within archaeology and beyond.

Speaker Matt Peeples is an associate professor and archaeologist in the School of Human Evolution and Social Change at Arizona State University and Director of the ASU Center for Archaeology and Society. His research focuses on integrating archaeological data with methods and models from the broader social and behavioral sciences to address questions revolving around the nature of human social networks over the long term. This work involves the development of large-scale archaeological settlement and material cultural databases, fieldwork in the U.S. Southwest, and the application of a variety of computational methods to archaeological data.