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Networks and problems

This was distributed by Phil Agre as part of his Red Rock Eaters News Service (http://dlis.gseis.ucla.edu/people/pagre/rre.html). It's quite useful for thinking about the nature of educational research.
Mark

Date: Sun, 27 Jan 2002 22:54:17 -0800
From: Phil Agre <pagre@alpha.oac.ucla.edu>
To: "Red Rock Eater News Service" <rre@lists.gseis.ucla.edu>
Subject: [RRE]notes and recommendations
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Networks and problems.

Different fields produce different kinds of knowledge. The idea of a diversity of knowledge, however, intimidates many people; it sounds to them like relativism, as if *anything* can count as knowledge if someone simply says so. That's silly; no such thing follows. Even so, it *is* a hard problem to understand how knowledge functions in society if knowledge is diverse, for example how to tell the difference between quality-control and censorship. The scholars who have argued for the diversity of knowledge, despite the quality of their research, have often been unconcerned with the public-relations problem that their insights suffer. They can win the argument about relativism when they are arguing with people equally as erudite as themselves, but they have historically not done a good job of translating the arguments into a rhetoric that wins public debates. That's partly because they are so concerned to defeat the mythology of unitary knowledge that they emphasize heterogeneity more than they emphasize the limits to heterogeneity. That's too bad, because the diversity of knowledge actually turns out to be related to the Internet's place in society.

Let me suggest an intuitive way to think about the differences between different kinds of knowledge. To simplify, I'll stick with academic fields. Every academic field, I will suggest, has two dimensions: problem and network. By the "problem" dimension of knowledge I mean the ways in which research topics are framed as discrete and separable, so that researchers -- whether individuals or teams -- can dig into them and produce publishable results without enaging in far-flung collaborations. By the "network" dimension of knowledge I mean the ways in which researchers organize themselves across geographical and organizational boundaries to integrate experience from many different sites. Every field has its own complexity in both of these dimensions, but often the emphasis is on one dimension or another. As a result, we can roughly and provisionally categorize academic fields as "problem" fields and "network" fields.

The prototype of a "problem" field is mathematics. Think of Andrew Wiles, who disappeared into his study for several years to prove Fermat's Last Theorem. The hallmark of "problem" fields is that a research topic has a great deal of internal depth and complexity. The math in Wiles' proof may seem like vast overkill for something so simple as the statement of Fermat's Last Theorem, but you can think of it as an engineering project that finished building a bridge over a conceptual canyon. Publicity value aside, the mathematicians value the bridge because they hope that it's going to carry heavier traffic in the future. Even so, it's not clear that Wiles' type of math represents the future. Math papers are more likely to be coauthored than in the old days, as mathematicians work increasingly by bringing different skills together. This is partly a legacy of the major math project of the 20th century, which aimed at the grand unification of fields rather than producing heavier theorems in a single area. That unification project opened up many seams of potential results along the edges between different areas of math. The increasing practical applicability of even very abstruse areas of math (e.g., in cryptography) didn't hurt either.

Even so, math is still weighted toward the "problem" dimension. Math people do form professional networks like anyone else, but the purpose of these networks is not so much to produce the knowledge as to ensure a market for it. The same thing is true in computer science, where professional networks also help with funding. And those are not the only problem fields. Cultural anthropology is a good example. The anthropologist goes to a distant island, spends two years learning the culture, and writes a book that uses it as raw material to explore a particular theoretical problem in depth. The "problem" nature of cultural anthropology is partially an artefact of technology; if long-distance communication is hard then it's easier to uphold the myth that humanity comes sorted into discrete cultures, and a fieldworker who travels great distances to study a culture has no choice but to define a large, solitary research project. But that doesn't change the fact that the best anthropology (and there's a lot of good anthropology being written) has intellectual depth to rival anything being done in computer science, even if the conceptual and methodological foundations of the research could hardly be more different.

Contrast these fields to some others: medicine, business, and library science. Medicine, business, and library science may not seem similar on the surface, but they have something important in common: they are all network-oriented. Because they study something that is complex and diverse (illnesses, businesses, and information), they build their knowledge largely by comparing and contrasting cases that arise in professional practice. Physicians don't make their careers by solving deep problems or having profound ideas; they make their careers by building networks that allow them to gather in one central location the phenomenology of a syndrome that has not yet been systematically described. Medical knowledge is all about experience-based patterns. It says, we've seen several hundred people with this problem, we've tried such-and-such treatments on them, and this is what happens. Business is the same way: we've investigated such-and-such an issue in the context of several businesses, and this is the pattern we've discerned. Library science, likewise, is concerned to bring order to the diversity of information as it turns up in the collections of library institutions worldwide.

When mathematicians look at business or computer scientists look at library science, they often scoff. They have been taught to value "problems", and they are looking for the particular kind of "depth" that signifies "good work", "real results", and so on. When they don't find what they are looking for, they often become disdainful. The problem is that they are looking in the wrong place. The don't realize that the "problems" that they are familiar with are largely artificial constructions. To fashion those kinds of problems, you need to take several steps back from reality. You're abstracting and simplifying, or more accurately someone else is abstracting and simplifying for you. Many job categories are devoted to suppressing the messy details that threaten to falsify the abstractions of computer science, starting with the clerks whose computer terminals demand that they classify things that refuse to be classified. The dividing-line between computer science and the business-school discipline of "MIS" is especially interesting from this point of view, since the MIS managers are much closer to the intrinsic complexity and diversity of day-to-day business. Computer scientists, as a broad generalization, have little feeling for the complexity and diversity of the real world. That's not to say that they are bad people or defective intellects, only that the field of computer science frames its knowledge in certain ways. It takes all kinds to make a world, and that goes for knowledge as well. We should encourage the creative tension between problem field and network fields, rather than arguing over who is best.

Medicine is an interesting case for another reason. Even though problem fields are higher-status than network fields as a broad generalization, medicine is an exception to the rule. If my theory is right, then, why doesn't medicine fall into the same undeservedly low-status bin as business and library science? The reasons are obvious enough. Medicine is a business unto itself -- at UCLA it's half the university's budget -- and it brings money in through patient fees, insurance reimbursements, and Medicare, as well as through research grants and student tuition. Money brings respect, all things being equal, although the increasingly problematic finances of teaching hospitals will test this dynamic in the near future. Medicine is also very aggressive in the way it wields symbols -- it's hard to beat life and death for symbolic value. What's more, business and library schools have stronger competitors than medical schools, so they have a greater incentive to speak in plain English. Precisely because they rely so heavily on symbols, medical schools have never had to explain how their knowledge works in ways that normal people can understand.

Professional schools in general tend to produce knowledge that is more network-like than problem-like, but historically they have very often responded to the disdain of the more problem-oriented fields by trying to become more problem-oriented themselves. This strategy is very old; in fact Merton described it perhaps fifty years ago. Unfortunately, it doesn't always work. You end up with professional schools whose faculties are trained in research methods that are disconnected from the needs of their students, or else you end up with factionalized schools that are divided between the scientists and the fieldworkers, or with people whose skills lie in network methods trying to solve problems because that's what the university wants. I think this is all very unfortunate. I'm not saying that every field should be homogenous, and even if everyone does the research they ought to be doing we'll still have the problem of how scholars with incommensurable outlooks can get along. Still, the asymmetry of respect between network knowledge and problem knowledge is most unfortunate.

I think the world would be better off if network knowledge were just as venerated as problem knowledge. Before this can happen, we need better metaphors. We are full of metaphors for talking about the wonders if problem knowledge, as we ought to be. When Andrew Wiles can go off in his room and prove Fermat's Last Theorem, that's a good thing, and there's nothing wrong with using the metaphor of "depth" to describe it. It's just that we need metaphors on the other side.

So here's a metaphor. I propose that we view the university as the beating heart of the knowledge society. The heart, as we all know, pulls in blue blood from all over the body, sends it over to the lungs until it's nice and red with oxygen, and then pumps it back out into the body. The university does something similar, and the predominant working method of business schools can serve as a good way to explain it. If you read business journals, especially journals such as the Harvard Business Review that are largely aimed at a practitioner audience, you will often see two-by-two matrices with words written in them. These sorts of simple conceptual frameworks (which I've talked about before) are a form of knowledge, but it's not widely understood what form of knowledge they are. Once we understand it, we'll be able to see how the university is like a heart.

So let's observe that there are at least two purposes that knowledge can serve: call them abstraction and mediation. Abstraction is the type of knowledge that the West has always venerated from Plato's day forward. It is something that rises above concrete particulars; in fact, it carries the implicit suggestion that concrete particulars are contaminants -- "accidents" is the medieval word -- compared to the fixed, permanent, perfect, essentially mathematical nature of the abstractions. Abstractions generalize; they extract the essence from things. They are an end in themselves. In Plato's theory we were all born literally knowing all possible knowledge already, since access to the ideals (as he called them) was innate. That made questions of epistemology (i.e., the study of the conditions of knowledge) not so urgent as they became subsequently, as the West began to recognize the absurdity of a conception of knowledge that is so completely detached from the material world.

But if knowledge can abstract, it can also mediate. The purpose of the two-by-two matrices in the business journals is not to embody any great depth in themselves, the way a theorem or an ethnnography might. Instead, their purpose is to facilitate the creation of new knowledge in situ. Choose a simple conceptual framework (transaction costs, core competencies, structural holes, portfolio effects), and take it out into real cases -- two or more, preferably more. Study what each conceptual framework "picks out" in each case; that is, use the conceptual framework to ask questions, and keep asking questions until you can construct a story that makes sense within the logic of that particular case. That's important: each case has its details, and each case is filled with smart people who have a great deal of practical knowledge of how to make a particular enterprise more or less work. So work up a story that makes sense to them, that fits with their understandings, yet that is framed in terms of the concepts you've brought in. Of course, that might not be possible; your new concepts may not pick out anything real in a particular case, in which you need to get new concepts. But once you've found concepts that let you make sense of several cases, now you can compare and contrast.

And that's where the real learning happens. Even with the concepts held constant, each case will tend to foreground some issues while leaving others in the background. Take the issues that are foreground in case A, and translate those issues over to cases B, C, D, and E, asking for each of them what's going on that might correspond to the issue from case A. It doesn't matter whether the other cases are all directly analogous to case A; even if the issue sorts out differently in those other cases, the simple fact that you've thought to ask the question will provoke new thoughts that may never have occurred to anybody before. That's what I mean by the mediating role of knowledge: it mediates the transfer of ideas back and forth between situations in the real world that might not seem at all comparable on the surface.

And that's the beating heart: what the university does is fashion concepts that allow ideas to be transferred from one setting to another. Each setting has its own language, so the university invents a lingua franca that gets conversation started among them. At first the ideas will pass through the doors of the university. A researcher will go out to several different sites, gather ideas, bring them home, think about them, and then scatter them in other sites. Eventually the concepts themselves will be exported, so that students who graduate into companies or consulting firms will become beating hearts on their own account. (That's a place where the analogy falters: maybe the university is more like a manufacturer of hearts.) We in modern society take for granted something remarkable: that nearly every site of practice is on both the donating and the receiving end of these mediated transfers of ideas. Often we don't realize it because the people who import ideas by mediation from other fields will often present them full-blown, without bothering to explain where they got them. Other times, a kind of movement will get going whereby researchers and practitioners unite across disciplinary lines around a particular metaphor that they find useful for mediating transfers among themselves: self-organization is one of the fashionable metaphors of the moment.

Mediating concepts can be used in various ways, but in general what you see is a mixture of two approaches: explicit comparing/contrasting of particular cases and something that looks more like abstraction. The resulting abstractions, however, usually have no great depth in themselves; their purpose is simply to summarize all of the issues and ideas and themes that have come up in the various cases, so that all of them can be transferred to new situations en masse. This is what "best practices" research is. It's also what physicians do when they codify the knowledge in a particular area of medicine; the human body is too complicated, variable, and inscrutable to really understand in any great depth, and so codified medical knowledge seeks to overwhelm it with a mass of experience loosely organized within some operational concepts and boiled down into procedures that can be taught, and whose results can be further monitored. This is the important thing about network knowledge: it really does operate in networks -- meaning both social networks and infrastructures -- and networks are institutions that have to be built and maintained. In a sense, network knowledge is about surveillance, and mediating concepts exist to render the results of surveillance useful in other places.

The mediating role of concepts can help us to explain many things. It is a useful exercise, for example, to deliberately stretch the idea of mediation to situations where its relevance is not obvious. Philosophy, for example, has long been understand as the ultimate abstraction, something very distant from real practice. This is partly a side-effect of the unfortunate professionalization of philosophy that led to the hegemony of analytical philosophy in the English-speaking world perhaps a century ago, but really it dates much further back into the ancient Greek mythologies of ancient times. The popular conception of philosophy as the discipline of asking questions with profound personal meaning is almost completely unrelated to the real practice of philosophy at any time or place in history. There are exceptions. One of Heidegger's motivations, especially in his earliest days, was to reconstruct philosophy around the kinds of profound meanings that he knew from Catholic mysticism. Some political philosophers have tried to make themselves useful to actual concrete social movements. But for the most part, philosophy has been terribly abstract from any real practice.

Yet, if we take seriously the mediational role of concepts, then maybe the situation is more complicated. One role of the university is precisely to create concepts that are so abstract that they can mediate transfers of ideas between fields that are very distant indeed. Perhaps we could go back and write a history of the actual sources of scholars' ideas, and maybe we would find that the very abstract concepts that scholars learned in philosophy often helped them to notice analogies that inspire new theories. Analogies have long been recognized as an important source of inspiration for new discoveries, especially in science but in other fields as well, and nothing facilitates the noticing of analogies so efficiently as an abstract idea that can be used to describe many disparate things.

I would like to see the university take the mediating role of concepts more seriously. I would like every student to be taught a good-sized repertoire of abstract concepts that have historically proven useful for talking about things in several disparate fields -- examples might include positive and negative feedback, hermeneutics, proof by contradiction, dialectical relationships, equilibrium concepts from physics, evolution by natural selection, and so on -- and teach them not as knowledge from particular fields, but as schemata that help in noticing analogies and mediating the transfer of ideas from one topic to another. The students would be drilled on the use of these concepts to analyze diverse cases, and on comparing and contrasting whatever the analyses turn up, and then they be sent off to take classes in their chosen majors. After a while we could do some intellectual epidemiology to see which of the concepts actually prove useful to the students, and we could gradually evolve the curriculum until we've identified the most powerful concepts. I do realize the problem with this proposal: it is bound to set off power struggles along political lines, and between the sciences and humanities, over the best repertoire of concepts to teach. But that's life.

The mediating role of concepts, and network knowledge generally, are also a useful way to re-understand fields that we normally understand mostly in terms of their problem knowledge. (You'll recall that my classification of fields as "network fields" and "problem fields" is a heuristic simplification, and that every field has both dimensions.) What is the network-knowledge dimension of math or computer science? I've already described one role of professional networking in each field, which is to provide an audience for one's work. All research depends on peer review, so it's in your interest to get out there and explain the important of your research to everyone who might be asked to evaluate it. Likewise, if you need funding for your research then you'll probably want to assemble a broad coalition of researchers who explain the significance of their proposed research in similar ways, so that you can approach NSF or the military with a proposition they can understand.

But none of that speaks to the network nature of the knowledge itself. What is network-like about knowledge in math and computing? It's true that neither field employs anything like the case method. But they do have something else, which is the effort to build foundations. Much of math during the 20th century, as I mentioned, was organized by the attempt to unify different fields, and that means building networks of people with deep knowledge in different areas. Only then can proposed foundations be tested for their ability to reconstruct the existing knowledge in each area. In computing, the search for foundations takes the form of layering: designing generic computer functionality that can support diverse applications. In that kind of research, it's necessary to work on applications and platforms simultaneously, with the inevitable tensions that I also mentioned above. So in that sense math and computer science have a network dimension, and I think that each field would profit by drawing out and formalizing its network aspects more systematically.

Even though anthropology is built on deep case studies, the network nature of its knowledge becomes clearer as you speak with the more sophisticated of its practitioners. Anyone who engages seriously with the depths of real societies is aware that theoretical categories apply differently to different societies, and that there's a limit to how much you can accomplish by spinning theories in abstraction from the particulars of an ethnographic case. I am basically a theorist myself, but I realize that my research -- that is, the theoretical constructs I describe -- is only valuable for the sense it makes of particular cases. So I read case studies, and I try to apply my half-formed concepts to those cases, or else I draw on concepts that have emerged from particular cases, and then I try to do some useful work with them. My work is also influence by personal experience, usually in ways that I don't write about. But I can only go so far before it's time to start testing the concepts against real cases again, and that's why I often move from one topic to another, contributing what I can until I feel like I'm out on a limb, beyond what I can confidently say based on existing case studies and common knowledge. It *is* possible to useful things without being directly engaged with cases, for example pointing out internal inconsistencies in existing theories, sketching new areas of research that other people haven't gotten around to inventing concepts for, noticing patterns that have emerged in the cases so far, or comparing and contrasting theoretical notions that have arisen in different contexts. But if you believe that theory can blast off into space without any mooring in real cases then you're likely to do the sort of pretentious big-T Theory that gives us all a bad name.

Anthropologists are thoroughly infused with that understanding, and so the best ones really do refuse abstraction. They see their theoretical constructs very much as ways of mediating between different sites. Their concern is not practical, so they are not interested in moving ideas from one site to another on a material level. They are usually not trying to help the people they study. Rather, they are interested in describing the fullness of the social reality they find in a given place, and like the business people they understand that the real test is the extent to which their story about a particular case makes internal sense. Granted, they are less concerned than the business people to be understandable to the people they are studying, although that too is changing as the "natives" become more worldly themselves, and as it becomes more acceptable by slow degrees to study "us" as well as "them". In any case, I think that the anthropologists' relationship to theory is healthy, and I wish I could teach it to people in other fields. Anthropology is also becoming more network- like as reality becomes more network-like, and as the myth of discrete cultures becomes more and more of an anachronism, but that's a topic for another time.

Knowledge is diverse because reality is diverse. In fact, reality is diverse on two levels. A field like medicine, business, or library science derives knowledge by working across the diversity of illnesses, businesses, and information, gathering more or less commensurable examples of each under relatively useful headings that can be used as to codify and monitor practice. And then the various fields themselves are diverse: they are diverse in diverse ways. Fields that pride themselves on abstraction operate by suppressing and ignoring diversity. That can be okay as a heuristic means of producing one kind of knowledge -- knowledge that edits the world in one particular way, and that can be useful when recombined with knowledge that edits the world in other ways. But it's harmful when abstraction is mistaken for truth, and when fields that refuse to abstract away crucial aspects of reality are disparaged as superficial compared to the artificial depth at the other end of campus. Let's keep inventing metaphors that make network-oriented fields sound just as prestigious and heroic as problem-oriented fields. The point, of course, is not just to mindlessly praise the work, since bad research can be done anywhere. The point, rather, is to render intuitive the standards that can and should guide us in evaluating research of diverse types. If we don't, then we will disserve ourselves by applying standards that don't fit, or else no standards at all.

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