Not all the pictures are from Papua New Guinea, so I guess technically there aren’t 420 images that you can use when teaching Pigs. But in this case, it is important to emphasize not just quantity, but quality. The pictures are high-quality, and they are very well cataloged: each one has extensive metadata describing when it was taken, and what and who is in each picture. They are organized by topic so you can see, for example, just the pictures with pork in them if that’s what you’re into.

In the interests of full disclosure, I’ll state right away that the people who did this work are friends of mine, so I’m hardly an impartial observer. But it seems to me that collections like this are The Future. As the Internet gets more and more turgid, filled with ad-encrusted crud and unverifiable assertions, carefully curated open access collections like this are so, so welcome.

The Rappaport photos are hardly novel. Museums and libraries all over the world are making their collections available — just check out the institutions participating in the Flickr Commons project. But the key step between availability and use is discovery: making sure people know about all the great resources out there.

That’s hard to do for libraries, for whom just producing digital collections is work enough. We need to use these collections regularly, and credit them when we do use them. It’s only when word of mouth spreads that people will really develop a sense of the many hidden treasures out there available for research and use.

So this week, the next time you need a picture for a powerpoint, why not get this process rolling and use a picture from the Roy Rappaport collection?

Last week, I surveyed mid-century formalist approaches to computing and culture, which took culture as ideational — a matter of mental states, structures, or content. Ethnoscience and cognitive anthropology epitomized this attitude toward culture, taking part in a cross-disciplinary “cognitive revolution.” As Paul Edwards has outlined, computers were central to the emergence of cognitive science, which was founded on an understanding of the mind-brain relation by analogy to software and hardware. George Miller, a pioneer of cognitive psychology, suggested that computers helped collapse the behaviorist paradigm. Where behaviorism limited psychologists’ theorizing to the mind’s strictly observable “outputs” — lever pulls and all that — the computer offered a model for thinking about “memory, syntactic rules, plans, schemata, and the like.” These notions could be instantiated in actual computers, providing a working model of what was going on in the mind. As Miller said: “We didn’t believe that computers were giant brains, but we could see the similarities.”

However, cognitive and otherwise ideational approaches to culture did not have a monopoly on computational models and methods.

Computers also proved useful for cultural materialists and ecologists such as Marvin Harris (himself a vociferous critic of ethnoscience), who thought of culture as part of a system of relationships with the material environment. For these anthropologists, computers could serve as aids to the computation of resources, populations, and so on, but their usefulness was not limited to making calculations easier. The feedback loops and systemic dependencies of cybernetics and computers provided a way to model what appeared to be similar relationships found in natural cycles of resource flow.¹

These ecologies had less in common with digital computers and more with analog computers, in which electronic parts would be arranged into systems analogous to the phenomena to be explained, and their behavior observed. How these systems behaved could then provide insight into control processes at play in other, similarly arranged systems. Establishing these analogical relationships was a form of theorizing or explaining: As Bateson said, “You make two statements, and what is true of both of them is the formal truth. This is what is called explanation.”

A central issue raised by cybernetics was one of representation: what did it mean to say that a computer “modeled” a particular ecological or cultural system? In what sense was a computer like a brain or an ecosystem? This problem opens up a literature much too vast to be dispatched here, but one aspect of particular anthropological relevance concerns the relationship between ritual behavior and environment. Classic texts of cultural ecology, like Roy Rappaport’s Pigs for the Ancestors (1968), and more recent ones, like Steven Lansing’s Priests and Programmers (1991), interpreted ritual practices in cybernetic terms as means of organizing resource flows. For Rappaport, the ritual kaiko pig feast of the Tsembaga in highland New Guinea could be understood as a form of resource management. For Lansing, the Balinese subak system of water temples and spiritual observance could be understood as a way to manage water rights.

These interpretations posed ritual and ecology as a kind of computational system in its own right. However, they notably did not take ritual behavior on its own terms: described in the language of ecologists, Tsembaga kaiko festivals and Balinese subaks were not efficacious for any metaphysical reason, but rather because they effectively coordinated the distribution and maintenance of natural resources. Rappaport, for instance, distinguishes between the “cognized model” of the Tsembaga — “the model of the environment conceived by the people who act in it” (1968:238) — and his own “operational” model, which indexes a material reality unacknowledged in the cognized model. Although the truth of the cognized model may be important to the people conducting the ritual, for Rappaport, “the important question concerning the cognized model […] is not the extent to which it conforms to ‘reality’ (i.e. is identical with or isomorphic with the operational model), but the extent to which it elicits behavior that is appropriate to the material situation of the actors” (1968:239).

This discrepancy — in which the “model” of the anthropologist is thought to work, while the “model” of the ritual participants is thought to be incidental — has been criticized. Particularly in the context of anthropology’s traditional field sites, the politics of computer simulation and representation are tied up with colonial practices of knowledge and power.² While a computer model’s correspondence to ecological phenomena is taken to be evidence of knowledge or explanation, such explanatory capacity or intent is not allowed for the “cognized” (we might say emic) model.

In his work on ethnomathematics and ethnocomputing, Ron Eglash has criticized this tendency: anthropologists, when modeling phenomena like the Balinese water temples or emergent patterns of mud terraces in the low hills of Ecuador, have typically considered their mathematical or computational qualities to be unintentional. Such anthropologists reserve “intent” for individuals who can express their motivations or plans in Western mathematical terms and regard these collective, often long-term developments as happy accidents.

The use of computers to produce functional models of processes both ecological and mental raised significant questions about the role and scope of anthropological description and explanation. What was the connection between the model and the thing modeled? What kinds of descriptions could count as explanations as well? These philosophical questions, about the epistemology and politics of modeling, anticipated later critiques of anthropological knowledge practices. And, curiously enough, although computers were sometimes credited with helping cognitive science overcome the behaviorist paradigm, a kind of behaviorism has re-emerged in big data analytics. With the growth of data sets containing user interactions with websites and other services, people interested in “culture,” especially in commercial contexts, have returned to thinking of it in behaviorist ways.