Sign In / Sign Out
Navigation for Entire University
- ASU Home
- My ASU
- Colleges and Schools
- Map and Locations
The pace of technological change is a subject of Project 7, which uses theoretical and ethnographic studies to investigate how demographic factors, such as population size and connectedness, influence the adoption and transmission of complex technology. The outcome of these projects will contribute fresh insights into the evolutionary/cultural events and processes by which humans adopted progressively complex technology—a hallmark of modern human uniqueness. Most significant is the potential to narrow the range of hypotheses regarding the times and places of origins and the “environments of innovation” in technological evolution by linking paleoanthropological to ethnographical and theoretical (modeling) research.
The human capacity to innovate and transmit novel technologies that are locally adaptive is unprecedented in nature, and is a key to our geographic expansion as a species. This project will help provide a detailed causal explanation for this process of cumulative cultural evolution, which is one of the three essential ingredients of the unique mode of human adaptation.
The cultural evolution of locally adaptive technology has played a crucial role in human ability to live in a much wider range of habitats than any other vertebrate species. The tools essential for life in even the simplest foraging societies are typically beyond the inventive capacities of individuals. These tools evolve, gradually accumulating complexity through the aggregate efforts of populations of individuals, typically over many generations. This ability is a key component in the package of adaptations that have made humans an outlier species in the natural world.
There is a range of hypotheses that explain how cultural evolution generates tools that individuals could not invent on their own. At one extreme, innovations are the product of conscious calculation: innovators understand their tools, and make deliberate improvements to them; later adopters learn innovations based on causal understanding. This spreads the cost of innovation over many actors and allows for the cultural accumulation of beneficial innovations. These models come from economics and are embraced by some evolutionary psychologists. At the other extreme, innovations are the result of accidental discovery or relatively myopic individual learning; adopters acquire innovations by copying successful or prestigious people, or because innovations are associated with better outcomes. In other words, people typically just do what they are taught, what seems successful, or what seems common. These models come from cultural evolutionary theory.
We propose to investigate this process of cumulative cultural evolution of technology in traditional societies. In this project we seek to understand the roots of successful innovations, and in particular the role of peoples’ causal understanding of technologies in mediating the frequency and scale of innovations people will make. In addition, we will study how demographic factors—including the size of the community of technology creators, and their connectedness to other communities of creators—might affect technological evolution. We will investigate these questions by studying causal understanding, functional properties, and spatiotemporal variation in Fijian house building and fermentation techniques used in Fiji and by the Himba in Namibia.
We have completed pilot interviews on house-building in Fiji, where traditional homes are known as bure. These houses provide shelter and safety; alternative, western houses are much more costly. Bure have complex structures, and the building process is complicated, including decisions about the selection of building materials, frame geometry and lashing, as well as decorative and socially important design features. So far, we have focused on a single design problem: structural strength. The problem is a crucial one in Fiji, since houses must stand up to hurricanes which strike about once every 4 years. In December 2009, eight of 46 houses across two villages were destroyed by hurricane Mick (Category 2); in 2012, an even greater number were reported destroyed by hurricane Evan (Category 5). And yet, some bure survived even Category 5 hurricanes, so it seems likely that these structures are well-adapted to solve this design problem.
Our pilot interviews demonstrated that in some domains, such as selection of wood type, subjects give clear causal explanations. In others, like the structural function of a long floating beam that parallels the ridgepole, participants would not give any verbal explanation. These beams are made of large, hard to obtain tree trunks, which suggests they must have an important function to be worth the investment. The lack of verbal explanation suggests that causal understanding hypotheses may not account for how builders learn and innovate during the building processes. We will build on these interviews using a variety of methods to study the role of innovation and variation as drivers in the cultural evolution of Fijian house technology.
First, we will document the level of causal understanding held by local house-building experts, who will be compared with non-experts, and non-builders (women). House-builders may have greater causal understanding than they express explicitly, so we will use a forced-choice interview paradigm to gauge causal understanding without requiring verbal explanations. In this task, we will ask participants targeted questions about the functionality of possible house frames that vary along a single dimension, under different environmental conditions. The conditions may include: wet and dry season, seaside versus inland locations, and unidirectional versus hurricane (multi-directional) winds. The houses may vary in terms of: materials used, frame structure, and method of construction (i.e., manner of lashing parts together). Participants with better causal understanding are expected to choose the most-functional house more often. Those with poorer understanding should show a weaker preference. As part of this method, we will have three external measures of the functionality of different parts of the bure homes. First, we will recruit a Fijian architect as a collaborator to assess bure structure durability from an engineering standpoint. Second, we will use survey data (detailed below) on bure construction and collapse, to assess how particular structural features affect bure survival. Finally, where possible we will test the explanations proffered by informants. For example, we will test the rot resistance of different woods empirically.
Our survey data will document spatiotemporal variation in bure construction in terms of structure and building techniques, identity of the builders and foreman, repair history, and time standing. We will also survey recently collapsed bure, as discussed above. The survey data will serve two functions. First, we can test the conclusions of our architectural expert and our local bure-building experts against the record of actual bure collapses and abandonments. Second, we can test the patterns of spatiotemporal variation based on functionality. We expect that there will be greater spatio-temporal variation for characteristics that do not have a functional purpose—that is, stylistic elements will vary more broadly across space and time, whereas functional aspects will be less variable.
When combined with the data on the average builder’s causal understanding of a particular functional trait, the survey data will allow us to narrow in on how causal understanding affects the likelihood of innovation for a particular trait. We can also combine data on who contributed or oversaw the building of each house, along with our causal understanding interviews, to assess whether individuals with better causal understanding are more or less likely to innovate. In addition, we can compare innovations made by foremen who have high versus low causal understanding, to assess whether those with high causal understanding differ in the types of innovations they make. We expect that those with high causal understanding will make more drastic changes than will those with low causal understanding. Finally, we can use the survey data on builder identities to calculate the total number of practicing builders in a village, and the degree to which their building practice overlaps with other villages. This will allow us to test whether, as predicted by cultural evolutionary models, larger and better-connected populations produce and maintain more innovations, and have more complex technologies as a result.
In addition to material artifacts, humans also depend upon technological know-how that does not take physical form, which as a result cannot be reverse-engineered by users. In this category, we will study food-processing technologies, with a particular focus on fermentation. In contrast to artifacts like houses, where there is visual and tactile feedback available, fermentation is causally opaque. In fact, a variety of fermentation technologies seem to have evolved in different societies without an accurate causal understanding of why fermentation makes food safe to eat; this understanding only arrived with explanations through modern chemistry and microbiology. This suggests that fermentation may have evolved through a trial-and-error process, more so than through deliberate tinkering based on causal understanding. We plan to apply our 3-part methodology to understand variation in fermented cassava starch breads (madrai) in Fiji, and to milk-souring techniques among the Himba in Namibia.
Our collaborator, Brooke Scelza (UCLA, will conduct pilot interviews on variation in milk fermentation techniques in Namibia at her fieldsite this summer. In addition to field research, we will compare the effectiveness and complexity of techniques adopted relatively recently by the Himba to those used by groups with longer histories of pastoralism, based on the electronic Human Relations Area Files (eHRAF)—an ethnological archival database—and research on milk fermentation techniques by the Food and Agriculture Organization of the United Nations (1990).
The study design we describe above can be applied to any technology where it is possible to assess causal understanding, the functionality of features, and spatiotemporal variation of these features. As such, our results will have clear implications for the archaeological record of modern human origins, including other projects in this proposal that seek to understand the timing of appearance and dispersal of foraging populations and their complex technologies during late Pleistocene environmental shifts in coastal southern Africa (e.g., Projects 3–6).