George L. Cowgill had a major influence on the study of the ancient city of Teotihuacan and the development and promotion of quantitative methods in archaeology. His wit, teaching, and research influenced many in the profession. We draw on two published autobiographical works (Cowgill 2008a, 2013a), some unpublished autobiographical notes (Cowgill 1983), his many publications, and our own associations with George.

Spatially and temporally unpredictable rainfall patterns presented food production challenges to small-scale agricultural communities, requiring multiple risk-mitigating strategies to increase food security. Although site-based investigations of the relationship between climate and agricultural production offer insights into how individual communities may have created long-term adaptations to manage risk, the inherent spatial variability of climate-driven risk makes a landscape-scale perspective valuable. In this article, we model risk by evaluating how the spatial structure of ancient climate conditions may have affected the reliability of three major strategies used to reduce risk: drawing upon social networks in time of need, hunting and gathering of wild resources, and storing surplus food. We then explore how climate-driven changes to this reliability may relate to archaeologically observed social transformations. We demonstrate the utility of this methodology by comparing the Salinas and Cibola regions in the prehispanic U.S. Southwest to understand the complex relationship among climate-driven threats to food security, risk-mitigation strategies, and social transformations. Our results suggest key differences in how communities buffered against risk in the Cibola and Salinas study regions, with the structure of precipitation influencing the range of strategies to which communities had access through time.

While our fascination with understanding the past is sufficient to warrant an increased focus on synthesis, solutions to important problems facing modern society require understandings based on data that only archaeology can provide. Yet, even as we use public monies to collect ever-greater amounts of data, modes of research that can stimulate emergent understandings of human behavior have lagged behind. Consequently, a substantial amount of archaeological inference remains at the level of the individual project. We can more effectively leverage these data and advance our understandings of the past in ways that contribute to solutions to contemporary problems if we adapt the model pioneered by the National Center for Ecological Analysis and Synthesis to foster synthetic collaborative research in archaeology. We propose the creation of the Coalition for Archaeological Synthesis coordinated through a U.S.-based National Center for Archaeological Synthesis. The coalition will be composed of established public and private organizations that provide essential scholarly, cultural heritage, computational, educational, and public engagement infrastructure. The center would seek and administer funding to support collaborative analysis and synthesis projects executed through coalition partners. This innovative structure will enable the discipline to address key challenges facing society through evidentially based, collaborative synthetic research.

Addressing archaeology's most compelling substantive challenges requires synthetic research that exploits the large and rapidly expanding corpus of systematically collected archaeological data. That, in turn, requires a means of combining datasets that employ different systematics in their recording while at the same time preserving the semantics of the data. To that end, we have developed a general procedure that we call query-driven, on-the-fly data integration that is deployed within the Digital Archaeological Record digital repository. The integration procedure employs ontologies that are mapped to the original datasets. Integration of the ontology-based dataset representations is done at the time the query is executed, based on the specific content of the query. In this way, the original data are preserved, and data are aggregated only to the extent necessary to obtain semantic comparability. Our presentation draws examples from the largest application to date: an effort by a research community of Southwest US faunal analysts. Using 24 ontologies developed to cover a broad range of observed faunal variables, we integrate faunal data from 33 sites across the late prehistoric northern Southwest, including about 300,000 individually recorded faunal specimens.

Hundreds of thousands of archaeological investigations in the United States conducted over the last several decades have documented a large portion of the recovered archaeological record in the United States. However, if we are to use this enormous corpus to achieve richer understandings of the past, it is essential that both CRM and academic archaeologists change how they manage their digital documents and data over the course of a project and how this information is preserved for future use. We explore the nature and scope of the problem and describe how it can be addressed. In particular, we argue that project workflows must ensure that the documents and data are fully documented and deposited in a publicly accessible, digital repository where they can be discovered, accessed, and reused to enable new insights and build cumulative knowledge.

Archaeological data and research results are essential to addressing such fundamental questions as the origins of human culture; the origin, waxing, and waning of civilizations and cities; the response of societies to long-term climate changes; and the systemic relationships implicated in human-induced changes in the environment. However, we lack the capacity for acquiring, managing, analyzing, and synthesizing the data sets needed to address important questions such as these. We propose investments in computational infrastructure that would transform archaeology’s ability to advance research on the field’s most compelling questions with an evidential base and inferential rigor that have heretofore been impossible. At the same time, new infrastructure would make archaeological data accessible to researchers in other disciplines. We offer recommendations regarding data management and availability, cyberinfrastructure tool building, and social and cultural changes in the discipline. We propose funding synthetic case studies that would demonstrate archaeology’s ability to contribute to transdisciplinary research on long-term social dynamics and serve as a context for developing computational tools and analytical workflows that will be necessary to attack these questions. The case studies would explore how emerging research in computer science could empower this research and would simultaneously provide productive challenges for computer science research.

Diversity is generally valued, although it sometimes contributes to difficult social situations, as is recognized in recent social science literature. Archaeology can provide insights into how diverse social situations play out over the long term. There are many kinds of diversities, and we propose representational diversity as a distinct category. Representational diversity specifically concerns how and whether differences are marked or masked materially. We investigate several archaeological sequences in the U.S. Southwest. Each began with the coming together of populations that created situations of unprecedented social diversity; some resulted in conflict, others in long-term stability. We trace how representational diversity changed through these sequences. Specifically, we review the transregional Kayenta migration to the southern Southwest and focus empirical analyses on regional processes in the Cibola region and on painted ceramics. Results show that, initially, representational diversity increased above and beyond that caused by the combination of previously separate traditions as people marked their differences. Subsequently, in some instances, the diversity was replaced by widespread homogeneity as the differences were masked and mitigated. Although the social causes and effects of diversity are many and varied, long-term stability and persistence is associated with tolerance of a range of diversities.

This article represents a systematic effort to answer the question, What are archaeology’s most important scientific challenges? Starting with a crowd-sourced query directed broadly to the professional community of archaeologists, the authors augmented, prioritized, and refined the responses during a two-day workshop focused specifically on this question. The resulting 25 “grand challenges” focus on dynamic cultural processes and the operation of coupled human and natural systems. We organize these challenges into five topics: (1) emergence, communities, and complexity; (2) resilience, persistence, transformation, and collapse; (3) movement, mobility, and migration; (4) cognition, behavior, and identity; and (5) human-environment interactions. A discussion and a brief list of references accompany each question. An important goal in identifying these challenges is to inform decisions on infrastructure investments for archaeology. Our premise is that the highest priority investments should enable us to address the most important questions. Addressing many of these challenges will require both sophisticated modeling and large-scale synthetic research that are only now becoming possible. Although new archaeological fieldwork will be essential, the greatest pay off will derive from investments that provide sophisticated research access to the explosion in systematically collected archaeological data that has occurred over the last several decades.

From a regional perspective, the late thirteenth-century aggregation of village populations into large towns in the northern Southwest appears to be a brief and dramatic episode of social reorganization. That it is apparent across such a range of cultural and ecological circumstances suggests that a regional perspective will be needed to understand why it occurred. However, if we are to understand how it occurred—the social processes involved in the aggregation of populations into large towns—a high resolution, long-term view of the settlement history of particular localities provides a necessary complement to the regional view. We present a detailed examination of the development of one town in the American Southwest, the large, prehistoric Zuni town of Heshot uła. Without the long-term demographic reconstruction made possible by a fine-grained seriation and full-coverage survey, we would have seen this key transition as much more rapid and much less closely tied to the local situation than it now appears to be. Although this pueblo was built about A.D. 1275, we argue that its appearance was, at once, the culmination of demographic processes operating over at least 150 years, and the outcome of two more rapid, qualitative organizational transformations separated by a century.

Corruccini questions our identification of kin group-based cemeteries and our inference of an ascriptive component of leadership at Hawikku based on the presumed use of flawed sex determinations, statistical problems, and inattention to potential gender-based differences in morphological similarity. We address each of these topics and present new evidence to support our contention that cemeteries at Hawikku were kin based.

Archaeologists, especially those doing regional surveys, generally assume that there is a correlation between the areal extent of a habitation site and the number of people living at that site. This paper uses a combination of archaeological and historical data from the Peruvian Andes to examine this assumption. We find that, in this case, only a weak correlation holds between these two factors. However, a consideration of site function and topography may render the apparent ambiguities understandable.

Despite the central role that kinship plays in key anthropological arguments, recent archaeological efforts to detect kinship have been notably scarce. Here, age and sex distributions and dental morphology traits that reflect genetic affinity are used to argue that specific kin groups were buried in formal, spatially discrete cemeteries in the ancestral Zuni settlement of Hawikku. The inferred kin groups are then used to investigate Hawikku political structure. Results show that community leaders, identified on the basis of mortuary treatments and grave offerings, were selected from a small number of kin groups, suggesting an ascriptive element to leadership selection.

Settlement-pattern studies are plagued by the problem of analyzing site distribution maps for time periods during which not all sites are contemporaneous. In a key insight, Dewar (1991) used counts of sites occupied at the beginning and end of a period to estimate, through simulation, the number of simultaneously occupied sites. While Dewar's treatment is an important contribution, drawbacks to his method are identified and remedies proposed. First, an equation that obviates the need for the simulation is provided. It is then shown that Dewar's method fails to take into account those sites that are both established and abandoned during the period. Finally, this problem is remedied through a modification of the equation that incorporates the single-period sites in the estimate of the number of contemporaneous sites. Application to Valley of Mexico settlement data suggests larger numbers of simultaneously occupied sites and shorter site use spans than were obtained by Dewar.

This article briefly reviews methodological issues relevant to subsurface testing and advances a general method for objectively assessing the reliability of an archaeological survey program based on subsurface survey methods such as the excavation of shovel tests or the use of auger holes. The proposed technique employs simulation to model subsurface testing strategies using simple principles of probability and assumptions that are explicit and realistic. An arbitrary layout of test units can be evaluated to yield an estimate of the proportions of sites of different sizes and artifact densities that will be found (or missed) by the testing program.

A method for measuring diversity is proposed that uses an archaeologically derived underlying frequency distribution of classes of artifacts to generate theoretical expectations for the number of different classes of items that should be found in a collection of a given total size. Because sample size is controlled for, collections of different sizes can be directly compared in a simple graphical display. Because of its rigor and simplicity, this method serves to focus interpretive attention on issues of anthropological (rather than methodological) importance. Examples from the archaeological literature are used to illustrate the operation and potential applicability of this method to a wide range of archaeological problems.

This article discusses an approach to spatial analysis which is more closely tailored to archaeological objectives and archaeological data than are more "traditional" quantitative techniques such as nearest neighbor analysis. Heuristic methods, methods which make use of the problem context and which are guided in part by intuitively derived "rules," are discussed in general and with reference to the problem of spatial analysis in archaeology. A preliminary implementation of such a method is described and applied to artificial settlement data and artifact distributions from the Magdalenian camp of Pincevent. Finally, the prospects for further development of heuristic methods are elaborated.