Toward a Behavioral Ecology of Lithic Technology: Cases from Paleoindian Archaeology

1. Toward a Behavioral Ecology of Lithic Technology

 Toward a Behavioral Ecology of Lithic Technology In her edited volume Time, Energy, and Stone Tools, Robin Torrence (1989b:1) wrote: “Archaeologists have been notoriously poor at producing their own theories for behavior and have depended largely on borrowing from anthropology and ecology, with, it must be admitted, mixed results.” What Torrence was lamenting, I believe, is something felt by many archaeologists. We have masterfully created a superabundance of data through more than a century of fieldwork, but to make sense of it we have relied heavily on models borrowed from other fields rather than building archaeological theory from the ground up. This is especially true for the most common artifact in the archaeological record—the lowly flake—or perhaps the “lonely” flake, which until recently received relatively little attention from lithic analysts. Modern humans and their hominid ancestors relied on chipped stone technology for well over two million years and colonized more than 99 percent of the Earth’s habitable landmass doing so. Yet we have only a handful of informal models derived from ethnographic observation, experiments, engineering, and “common sense” to explain variability in archaeological lithic assemblages. For this reason, the study of lithic technology seems to have stalled. The intent of this study is to begin to develop a formal theory of lithic technology. Although, as many researchers have noted, people who make and use stone tools are no longer extant (except in a very few limited circumstances), this in no way should hamper our ability to develop theory pertaining to lithic technology. We have not, for instance, allowed the lack of extant cave artists or governments commissioning pyramids to stifle efforts to theoretically explore these topics. Because the fundamental processes of making, using, and discarding stone tools are, at their very root, exercises in problem solving, we can ask what conditions favor certain technological solutions. Whether we are asking how wide the business end of a scraper should be or whether a flake should be saved 1  Chapter 1 or discarded, answers must be sought that extend beyond a case-by-case basis. One avenue for addressing these questions theoretically is formal mathematical modeling. I define “formal model” as a model that is constructed mathematically, either built from mathematical expressions or algorithms (computer programs ). Formal models have the advantage of having explicit predictions that must derive from their assumptions, something not true of informal models. As Kelly (1992:56) has noted, “At present, then, many interpretations of stone tool assemblages as indicators of mobility are subjective, intuitive, and sometimes contradictory.” These contradictions often arise not from data themselves but from theoretical inadequacies, resulting from the development and application of models that occasionally lack a logical foundation because they are often constructed with faulty, or at least unsubstantiated premises, or because they do not explicitly state goals, currencies, or constraints. My intent is not to discredit all previous work based on informal models, nor to say that there is only one way to “do” archaeology. Formal mathematical models are but one of a myriad of tools that can be used to learn about the world. Instead, I intend to highlight the utility of formal mathematical models for a particular area of archaeological research. Mathematical models, by their very nature, entail model causal relationships that have unambiguous predictions. In contrast to some early criticisms of the use of formal models of kin selection in anthropology (e.g., Sahlins 1976:44–45), formal mathematical modeling of human behavior does not assume that prehistoric humans were slide rule–toting Einsteins who moved from camp to camp calculating optimal solutions to foraging problems (Boone and Smith 1998; Dawkins 1989:291–292). After all, there is overwhelming evidence that all species of animals from invertebrates to vertebrates are optimizers (Alexander 1996; Krebs and Davies 1984), even though they are rarely if ever conscious of the underlying principles governing their behavior (fig. 1.1). Of course, there is a simple explanation of why animals are optimizers—behaviors that maximize fitness are favored by natural selection, a process in and of itself that rewards optimization (at least with respect to the number of surviving offspring) for a given set of environmental variables. Furthermore, there are many studies that show unequivocally that complex human subsistence behaviors can be explained with reference to very simple mathematical Toward a Behavioral Ecology of Lithic Technology  models from foraging theory (e.g., Belovsky 1987; Hawkes et al...