Some notes on new computer users, artificial intelligence, memes, and attentional economics, plus follow-ups and URL's. ** Beginners as a scarce resource. Forget koala bears. The endangered species whose conservation most concerns me is people who have never used computers. We need them. Those of us who know how to use computers have been corrupted; we have accommodated ourselves to bad interface design, conceptual confusion, primitive operating systems, and ludicrous security and trust models. Why have we put up with it? Well, first we assumed that it was our own fault, and then we forgot about it. In doing so, we have stunted ourselves, shriveled our imaginations, and steered our civilization into a blind alley. We are in trouble, and our only hope lies in the few remaining members of our species who have not yet been made stupid. It's true: people who have never used computers are the last flickering flame of humanity. We must sit at their feet and learn what they have to teach us. Whenever we invent a new version of our sorry gadgets, we should respectfully ask a few of these wise people to sacrifice their minds by learning how to use them. (I'm amazed that introductory computer science courses, unlike other bizarre medical experiments, don't require human subjects releases.) And as they "learn", as their minds are slowly taken from them, we should minutely document each step. Their every "confusion", their every "mistake", is a precious datum. As my own small contribution to rebuilding our civilization, I want to sketch a few of the phenomena that I have observed when, forgive me, I have taught people how to use computers. I'll start with something that happens on the Macintosh. The Macintosh, like every other computer, is a language machines. But new users face a chicken-and- egg problem: they don't know how to use the computer, so they need to ask questions; but they can't ask questions without knowing what the words mean; and the only way to learn what the words mean is by using the computer. Now, language is about distinctions. And the Macintosh interface is governed by several distinctions that sound the same to beginners but are importantly different. They are: (1) an application being open or closed, (2) a window being open or closed, (3) a window being selected or unselected, and (4) a window being visible or hidden. Granted, only psychopaths tell beginners about (4). But that leaves three importantly different distinctions. The difference between (1) and (2) is built on another, more basic distinction, an application versus a window. One of the very hardest things for new Macintosh users to understand is that an application can be open, even though it does not have any currently open windows. Almost as hard is the idea, built on the distinction among all three of them, that you can select a different window without closing the one that's currently selected (and maybe losing all of your work). So beginners get into the following loop: they start one application, do some work in the window that gets opened, close that window, start a different application, do some more work in that new window, close that window, try to start the first application again, and then get befuddled when no window opens. When they ask for help, their desktop is blank: it has no open windows. They do not have the language to explain how it got that way, and the helper starts talking technical language at them that they don't understand. (To make matters worse, the Macintosh is actually inconsistent: some applications allow zero windows to be open, while others automatically close themselves when you close their open window, thus helpfully conflating (1) and (2).) Far from being dumb, the beginner in this case has been following a perfectly rational beginner-logic, which is based not on concepts and distinctions but on things they can see and do. They reason, "to get it to do this, I do that". The distinction between an application and a window is a hard thing to see. It's only visible if you have multiple windows open in the same application, which beginners rarely do, or if you pull down the menu in the upper right hand corner of the screen, whose meaning is far too abstract for beginners to comprehend. Here is another thing that beginners do. They're sitting there at the keyboard, one hand on the mouse, and you're standing next to them, "helping" them. Either they've gotten themselves into trouble, or they're just learning a new feature of the machine. In either case, society has defined the situation, quite perversely, in terms of your great authority and their shameful cluelessness. You point at the screen. You say "see that box?". And before you can say another word, they click on something in the general vicinity of the box. You say "no". And before you can say another word, they click on something else in the general vicinity of the box. This cycle then repeats three or four times, with both of you becoming increasingly agitated as they destroy everything in sight. They're hearing your "no, no, no, no" as a God-like rebuke that condemns them to eternal damnation, and you're watching complete irrationality that you can't seem to stop. The underlying problem, evidently, is that the feeling of not-knowing- what-to-do is intolerable. They think they ought to know what to do, and they feel stupid because they don't, so they guess. And I'm not just talking about weak-minded, insecure people who lack confidence in other areas of their lives. Absolutely not. There's something about computers that reduces even powerful human beings to jello. What's worse, in many people this great fear of not-knowing-what-to-do is combined with an equally great fear of breaking something. (Although I haven't seen this myself, several people have told me stories about friends or family members who encountered an error message such as "illegal operation" and thought that they were going to jail.) The resulting mental state must be really something. The distressing sense of having broken something is in fact something that I can vaguely recall myself. The first program that I ever wrote, circa 1974, was a BASIC routine to print out the prime numbers. It printed "2", then set a variable to 3, and looped, adding 2 each time until it became equal to 100, which of course didn't happen. I was extremely pleased as the 110 baud teletype with its all-caps print ball and scroll of yellow paper printed out the prime numbers up to 97, and then I was horrified when it printed 101 and kept going. The machine was clattering away, there in the terminal room, printing prime numbers on beyond 200 and 300. I didn't know how to stop it. I went to the guy at the help desk, who for some strange reason lacked my sense of urgency about the problem. He came over, showed me the ridiculously obscure Univac code to stop the machine (break @@X CIO return, if I recall), and walked away. I was certain that I had used up all $100 in my computer account, and I started plotting how I was going to pass the course, given that I wasn't going to be able to use the computer any more. So I guess we can sort of remember. But not really. I'm sure I can't get back the full horror of the situation, much less adjust my methods of teaching to accommodate similar horrors in the experience of others. Here's another pattern, one that starts to put beginnerdom in context. You've got a beginner -- someone who swears that machines spontaneously break when she walks into the room -- who is married to a gadget freak -- someone who enjoys having lots of machines around and actually likes it when none of them quite works. This happens a lot. Now, beginners need stability. Because of the nature of beginner logic -- "if I do this, it does that", or what urban geographers call "path knowledge -- the beginners don't know what the behavior of the machine depends on. So if things change -- configurations, versions, switches, wires, phone lines, whatever -- then the beginner's empirical regularities fall apart. The gadget freak identifies with the inside of the machine and cannot even see the surface of it; the beginner knows nothing but the surface of the machine and regards the inside as terra incognita. That's because the gadget freak knows all of the distinctions that the machine depends on while the beginner knows none. The two of them, freak and phobe, can live in this equilibrium situation forever, with the freak constantly undermining the stable conditions that the beginner would need in order to learn. The situation of the beginner is explicable, finally, in terms of the largest of technical contexts: the endless clashing of gears among standards. I tell students that computers "almost work", because they are guaranteed to run up against some kind of stupid incompatibility that the rest of us have long since learned to work around. The most common of these stupid incompatibilities might concern character sets. Think of the gibberish you get when you copy and paste a Word document into any of a number of other applications with different ideas about what (for example) a double-quote is. Prepare a Web page in Word. Or copy a block of text from a Web page that happens to include an apostrophe, and then paste it into Telnet. Granted, these situations provide professors with opportunities to explain concepts like the competitive pressures for and against compatibility. But I've never met a student who could care less about this topic when they're trying to get their first Web page working. Those are a few of the phenomena that provide us with faint clues about the blessed world of timeless wisdom in which beginners live. Other clues can be found in beginners' attempts to read computer manuals, although that's an area where most of us can recover some of the primal helplessness that is the beginner's lot. Just today, for example, I tried to figure out how to update my anti-virus software. Hoo boy. I believe that much more research is required in this area. And it's not just a matter of incremental fixes in the basic windows- and-mouse interface. We need to be open to the possibility, indeed the certainty, that computers as we know them today embody ideas about people's lives that are shallow, muddled, and immoral. We will learn this, if at all, from the "confusions" and "mistakes" of beginners. ** Markets are changing drastically with the Internet and other related technologies, but my impression is that structural problems prevent the field of economics from understanding the phenomena as fully as it might. Economics is divided into two schools: a unified and dominant neoclassical school and a fragmented and marginal institutional school. Each school defines the word "market" differently. For neoclassicists a market consists of a set of buyers, a set of sellers, and a good. For institutionalists a market consists of a framework of laws, rules, customs, and norms. Neoclassicists treat a technology as a function that tells you how much output you can produce for a given amount of certain inputs. Institutionalists treat a technology as a body of skill and knowledge that an organization has learned by using it. You might observe that the schools are both right. Neoclassicists oversimplify markets because they treat the institutional framework as trivial, ignoring its complexities or treating them as transaction costs. Institutionalists oversimplify markets by dwelling too much on the stasis of institutions; they cannot conceptualize the emergent aspects of market structure. Neoclassicists are strangely obsessed with vindicating their premises; institutionalists spend way too much of their time exploding them. A middle ground does emerge between the two schools in the depths of game-theoretic analyses of information. But everyone's still taking themselves way too seriously. ** How bad philosophy lives through our computers. The New York Times printed a curiously pointless article the other day about the current state of research in artificial intelligence. Do you suppose that this article was related to the forthcoming Spielberg movie on the subject? I'm sure we'll see publicity-driven articles about AI as the movie approaches. Do you suppose the movie will include a rational computer that discovers emotions? I'm bored already. At least the Times article had it right: AI turns out to be a technology of niches. AI is not about making intelligent computers, and it hasn't been for many years. It's about developing a specific class of algorithms that require data of very particular sorts. As sources of data multiply in the world, AI finds more corners where it can be useful. Most technologies are like that. What's misleading is the idea that AI people produce machines with any sort of generalized intelligence. With a small number of non-mainstream exceptions, they don't even try. Which is fine, or would be if they changed the name. I wrote my dissertation in AI, as long-time subscribers to this list know. I went into AI because I actually thought that it was a way to learn about people and their lives. It is, it turns out, but only in reverse: the more one pushes against AI's internal logic, the more one appreciates the profound difference between human beings and any sort of computers that we can even imagine. What I really learned during my time in AI was how to learn things in reverse by intuiting just how impossible some things really are, and by listening to the technology as it refuses to do the things it is advertised to do. I've already told several stories along these lines, but I haven't told the hardest one. It happened in 1985. I had been experimenting with the simple practice of writing out very detailed stories from everyday life -- absolutely ordinary events that had happened in the course of washing the dishes, taking out the trash, walking to work, and so on. AI had focused on stereotypically brainy activities like playing chess, but it was clear even then that something fundamental was missing. As I wrote out these stories, I became deeply impressed by their power to punch through the AI way of talking about people's lives. It's a hard thing to explain unless you've done it: the sudden realization that one's own absolutely commonplace everyday experience contradicts, and in the most transparently understandable way, everything that one has learned in school. I drew a large number of conclusions from these exercises, and it is important that these conclusions were all intertwined: breaking with only a few of the standard AI assumptions would not have sufficed, because they tend to reinforce one another. Breaking with all of your field's assumptions at once, however, tends not to be a swift career move. First of all, nobody will have the slightest idea of what you are talking about. Second of all, it will turn out that you haven't broken with 100% of the assumptions after all, and that the remaining assumptions, still unarticulated, will render your revolutionary new research enterprise internally incoherent. Third of all, the methods and techniques and infrastructures and evaluation criteria of your field will all be geared to the existing assumptions, so that you will have to reinvent everything from scratch, which of course can't be done. In short, you're hosed. Faced with this sort of impossible situation, slogans are important. If you can't explain it as engineering, then at least you can put on a good show. This is a longstanding tradition in AI, and it was my only chance. So I was dead-set on building a system that illustrated my technical ideas in some "domain" that symbolized ordinary everyday routine activity -- something that was the symbolic opposite of chess. That meant -- and this is obvious somehow if you know the history of AI -- that my program had to make breakfast. Even if the technical message didn't connect, the sheer fact that the program made breakfast would be enough for the time being. I don't have to go into the technical message of the breakfast-making project, except for two details. It was going to be important that the program decide from moment to moment what it was going to do, based on real-time perception of the breakfast-making equipment in front of it. In other words, even though we think of making breakfast as something very routine, that routine was going to emerge from a process that was basically improvised. It was going to be important as well that the system not be omniscient: that it be reliant on its senses for most of its knowledge about the precise states and arrangements of its materials from moment to moment. If this latter premise doesn't seem radical, then you don't know much about AI. One of the really profound, long-standing problems with AI is a conflation between the mind and the world. This confusion takes many different forms -- so many, in fact, that the problem is almost impossible to explain accurately. Suffice it to say that the standard theory of knowledge in AI is that you have a "world model" in your head -- something that possesses all of the things and relationships of the real world, except that it's made out of mind-stuff. This view of knowledge is deeply ingrained in the field. The problem is that, as a technical matter, it does not work. Building and maintaining a world model is computationally very burdensome. Of course it can be done if the world is a sufficiently simplified and controlled, and so AI research has tended to stick with "worlds" that are simplified and controlled in just the way that world-model-maintaining algorithms require. Although these "worlds" do resemble some of the exceedingly artificial environments of industry, they bear little relationship to the world of everyday life. People who care about industry are happy with this arrangement, but I cared about everyday life. So I threw out the standard AI theory of knowledge and perception, and I tried to build entirely new theories, based on the experiences that I had written down in my notebook. It turned out, most surprisingly, that the key is our embodiment: we are finite; we occupy specific locations and face in specific directions; and we can deal quite well with our environments without knowing just where we are, what time it is, the precise identities of everything around us, and all of the other objective information that is required to build a world model. Our knowledge of the world has a superficial quality: we deal largely with the surface appearance of things as they are evident to us in our particular standpoints in the world, and we build more complex and objective representations of the world, such as paper maps or mental internalizations of activities that involve reading paper maps, only in relatively rare occasions. The fact that we have bodies and live immersed amidst the things of the world turns out to be important from a computational perspective: being embodied and located, and dealing with things as they come, and as they present themselves, is computationally easier than building and manipulating world models. This is a deep and weird idea, but it remains nothing but a woolly intuition (or so AI says) until one builds a system that concretely illustrates it in practice. So I set out to build a system that, being embodied amidst the physical things of breakfast-making, could make breakfast. At first I explored doing all of this robotically. We had robots; we had software to drive the robots; we even had robots with three-fingered hands, which was easy enough mechanically but not at all easy mathematically and computationally. So I thought for a while that my dissertation would be about the theory of fingers, and I made several studies of people picking up telephones, holding forks, and otherwise exercising their fingers. It quickly became apparent that people were taking advantage of every single property of their hands, including the exact degree of "stickiness" and "stretchiness" of their skin -- neither of which was even remotely amenable to known computational methods or implementable in known materials on robots. The computational impossibility of the things people were doing with their hands was intriguing: either people were doing insanely complicated mental calculations of the stretchiness of their skin, or else people learned how to use their hands by trial and error without ever doing such calculations. Because the latter theory makes much more sense, I set about theorizing the paths by which people evolved routine ways of using their hands. (By "evolved" I don't mean in genetic terms but in the course of their individual lives.) I became an expert on the (strikingly unsatisfactory) physiology of the hand, and I read the literature on the neurophysiology of hand control. In short order these exercises produced interestingly generalizable principles for the evolution of routine ways of doing things, which I set about codifying. By now, of course, the thought of implementing any of this on a real robot was hopelessly impractical. So, following another long tradition in AI, I turned to simulation. I wrote a series of programs that simulated the movements of human hands, whereupon I discovered that the impossible-to-compute interactions between the skin of people's hands and the tools and materials of breakfast-making were still impossible to compute, and therefore impossible to simulate. Having been chased entirely out of the territory of studying how people used their hands, I fastened on the problem of modeling the processes by which routines evolve. I made numerous detailed studies of this question, again based on story-telling work in my notebook. It became clear that complex forms of routine activity, such as making breakfast, evolve over long periods, and that the successive steps in the evolutionary process could be more or less categorized. This was an excellent discovery, but it was still just an idea in my notebook, rather than something I knew how to build on a computer. I tried again, this time building a program to simulate a simplified "breakfast world". This, too, is a time-honored tradition in AI: the simplified simulated world that you can pretend captures the essence of the real-world activity you claim to be interested in. The problem (as you already know) is that the simplifications distort the reality to make it fit with the computational methods, which in turn are badly wrong-headed. And so it was in my own project. I couldn't simulate hands, so the "hand" became a simple pinching device that had magical abilities to grab whatever it wanted and exert whatever leverage it wanted upon it. But then I had to simulate things like the pouring of milk and cornflakes, the overflowing and spilling of containers, the mixing of things that were poured together, the dropping of things on floors, and all of the other events that would make life in breakfast world meaningful. Well, the physics of spilling, dropping, mixing, and all the rest is nearly as bad as the physics of skin, and so all of that stuff got simplified as well. Other simplifications slipped in along the way. When you or I make breakfast in the real world, we are an arm's-length from most of the materials and equipment involved (dishes, corn flakes, sink, toaster, table, utensils, countertops, etc). We are (as I insisted upon above) *amidst* those things. In particular, we see them in perspective projection. They occlude one another. When we move our heads, they take up different relationships in our visual fields, and the visual cortex (which is in the back of your head) does a lot of complicated work on the images that result. The visual cortex does not produce anything like a three-dimensional model of the whole world, and indeed one of the research questions is where exactly the boundary is located between the automatic, bottom-up, reflex-like processing of the visual cortex and the active, top-down, cognitively complicated, improvised kind of thinking that makes use of the results (which happens in the front of your head). But before I could answer this question, I had to simulate the real-world processes of perspective projection that cause those breakfast-images to be formed on your retina in the first place, as well as the first several stages of automatic processing of them. All of which was, of course, insanely difficult in computational terms. Again I had to simplify. Out went the perspective projection. The simulated robot now looked out at breakfast-world in orthogonal projection, meaning that it was no longer "amidst" the breakfast-stuff in any meaningful sense, and furthermore that breakfast-world had now become two-dimensional. And the simulation now happened in high-level, abstract terms of the sort that low-level vision might be imagined to deliver as its output, and not in anything like the real messy physics. I despaired because I saw what was going on: all of the traditional AI assumptions that I was trying to reject were creeping back into my project. My breakfast-making "robot" no longer had a body in any meaningful sense. Its "hands" were caricatures that just hung there in space. All of the materials and equipment of breakfast-making had become so simplified that there was no longer anything much for anyone to learn about them. States of the world had become discrete and well-defined in a way that was completely artificial. In fact, the "world" had become indistinguishable from AI's idea of a simplified mental representation of the world. Things were not going well. All through this story, I was explaining the grand philosophy and infuriating day-to-day logic of my project to another student, David Chapman. Being possessed of a pragmatic streak that I lack, David observed that breakfast-world was being relentlessly transformed into a video game. He liked the whole story about improvised activity and perceptual architecture, and one day he was in Santa Cruz watching someone play an actual video game when inspiration struck; he went home and wrote the program that I had been trying to write, except that the program played a video game rather than making breakfast. Programming environments were sufficiently powerful back then (this was in the age before Microsoft when giants strode the earth and Unix was regarded as a joke) that the task wasn't even particularly hard. I was horrified, of course, the whole everyday-life breakfast-making symbolic-ideological dimension of the project having been exchanged for a "domain" that involved killing (albeit penguins who kick ice cubes at attacking bees). But when you're an engineer it's hard to argue with results, and David had results where I had none. I gave up. We then sat down in David's office, talked through a series of video-game scenarios in terms of their (tangential, problematic, and confusing) relationship to the whole everyday-routine-activity theory, extended the program a bit, and were simultaneously impressed and disgusted with ourselves. I went home in a state of exasperated resignation, and David wrote a conference paper about the program and put my name on it. That paper is now one of the most cited papers in the history of the field, though not always for the reasons we would have hoped. The visual-system architecture that we pioneered is now conventional in the field, and we sometimes even get credit for it. But the whole philosophy of computation, improvisation, knowledge, and people's everyday lives is still the province of armchair philosophers. What happened? One problem was technological. Robot manipulators and visual systems weren't remotely well enough developed. Computers weren't powerful enough to perform the simulations needed to do a real job of breakfast-world, and I had to write layers of general-purpose simulation software packages that in a perfect world would already exist. Another problem was scientific: although the scientists had pretty compelling hypotheses about the architecture of the relevant neurophysiology, they didn't know the details, and they certainly didn't have the canned software routines that I needed to simulate it. Those problems were enough to drive me toward trivialized, schematic simulations rather than real robotics in the real, physical world. In particular, they were enough to drive me toward simulations of breakfast-world that looked identical to the standard AI story about mental world models. Whether the simulation of breakfast-world was part of my simulation of the human mind or part of my simulation of the world that the simulated mind interacted with was pretty much arbitrary. This happens a lot in AI: the engagement between simulated-person and simulated-world is a software construct, and nothing about the software forces anyone to keep the simulated mind and world separate, or to interpose complex perceptual and motor apparatus between them. Those dynamics would have been enough to wreck my project, but I think that the problem actually runs deeper. Let us return to my theory. As people, we live in a world that is complicated and only moderately controlled; as a result, our actions are necessarily improvised to a great extent and our knowledge of the world is necessarily superficial and incomplete. Industrial machines, by contrast, live in a world that is rather simple and highly controlled; as a result, actions can be planned to a considerable extent and knowledge can be just short of complete. Industrial machines, moreover, are generally not mobile. Their relationships to their environments are generally stable, and so they are rarely in any doubt as to the identities of the things they are dealing with. The main exceptions are parts-sorting tasks that are themselves highly controlled. For decades now, the philosophy of industrial control has been to maintain a correspondence between the physical world and a digital model of that world; that is what control theory is about, and that whole approach feeds naturally into the AI methodology of creating and maintaining world models. And this is not just about AI: all of computer science works this way. Ever since it began, computer science has been geared to creating and maintaining detailed models of the world. At least control theory (known to most people as cybernetics) posits a constant, moment-to- moment interaction between controller and plant; it is a relationship of control rather than reciprocity, but at least it's a real-time engagement of some sort. Computer science doesn't even make this kind of relationship of control easy; the interaction between the digital realm inside the machine and the physical and social world outside the machine has always been fraught, to the extent we consider ourselves lucky that we can deal with computers through primitive keyboards and buttons. Descartes had a hard time explaining the relation between the mind and the physical world; it had to do with the pineal gland, which somehow, uniquely among all organs, had the capacity to interact causally with both the spiritual realm of the mind and the physical realm of the body. The keyboard, windows, and mouse are the pineal gland of the modern computer. The great themes of intellectual history are not conspiracies; they are not even matters of choice. They reproduce themselves on their own steam, coded not into single words or propositions but into what Nietzsche famously called "a mobile army of metaphors, metonyms, and anthropomorphisms -- in short, a sum of human relations, which have been enhanced, transposed, and embellished poetically and rhetorically, and which after long use seem firm, canonical, and obligatory". We do not hover above history watching this army's history; we don't even live on the ground with the army marching past us. Rather, the army marches through us and by means of us. It is reproduced in our gossip and our dissertations, in our politics and our job interviews, in our computer programs and the language by which we set about selling them. And exactly because computers are language machines, the great themes of intellectual history are reproduced through our machines, coded into them beneath a veneer of logic, so that another generation can think that that they are inventing it all for the first time. ** Large numbers of .com companies are trading for well under $1 a share. It wouldn't cost much to buy them all and shut them down. But it's okay: every important new technology goes through this stock-bubble phase, transferring wealth from the dumb to the quick. The point is to get back to the democratic vision of the Internet that was common sense before the failed experiment with the advertiser-supported Web got started. There's nothing wrong with someone making an honest buck on the Internet, of course. What's wrong is identifying the making of bucks as the essence of the medium. ** Concepts as raw material. My research has evolved to the point that it's unclassifiable. One of my own colleagues told me last week that it isn't even research! That was nice. I suppose you could say that I'm a theorist, except that (1) the word carries so many bad connotations, (2) I'm not interested in the problematics that organize research in social theory these days, and (3) I would rather write in broadly accessible language than in theory-speak. But you know, that's what tenure is for. I'm making a difference and if my work doesn't fit the usual classifications then that's how it goes. I'll just call what I'm doing "public design" and get on with it. I listed some of the tenets of public design last week. This week I want to talk about the raw material of public design, namely concepts. By "concepts" I really mean "analytical categories", that is, concepts that are thoroughly embedded in the history of social theory, but that only fully take on a meaning when they are applied to the analysis of particular cases. A public designer fashions concepts that pose the kinds of questions that the designers of new information technologies need to ask, including the questions that they haven't thought of yet. It's important, in my view, to be knowledgeable in the ways of concepts, and to be good at choosing among them. To show what I mean, I want to explain what's wrong with two widely-used concepts, "memes" and "attention economics", and what it would be like to start fixing them. The concept of a "meme" starts life inauspiciously by analogy to a terribly reductionist theory of genetics, namely that genes are out for themselves and organisms exist to reproduce them. Now, it is not at all clear that the concept of "genes" is even a useful way to explain the seriously complicated things that go on with reproduction (see Evelyn Fox Keller's new book), but put that aside. The idea is that genes are abstract units of genetic material (DNA sequences or something more complicated) that somehow code for equally abstract attributes of an organism. Some genes die out when they are selected against, and so the genes that are still around are doing something right. Maybe organisms are just tricks that genes use to reproduce themselves. Alright. "Memes" are supposed to be analogous: discrete units of intellectual material (sequences of words or something more complicated) that play certain unspecified roles in human life. Some memes die out when cultures don't pass them along, and so the memes that are still around are doing something right, and maybe the social world exists as a set of tricks that memes use to reproduce themselves. It's clever in an adolescent way, this inversion of the usual story. What it really does, this inversion, is to point out that the usual story is itself inadequate. Obviously one needs a story that moves on different levels at once. A biological theory needs levels, such as ecology, physiology, and genetics. The necessary theory on each level must explain what it demands of the levels below and above it. This is hard work, both because of the difference in scale and because the concepts required to explain biochemistry (for example) differ in kind from those required to explain ecology. It's the same with memes. You can *say* that culture is meme's trick for reproducing themselves, but that doesn't tell us anything about how culture works. As with biology, you need a theory that operates on several levels and explains the relation between them. What's more, when you actually start the task of explaining the full range of social mechanisms by which memes reproduce, the concept of a meme starts to break apart. That's because there are lots of different social mechanisms, whose principles of operation are quite diverse. A good way to talk about this diversity is in terms of institutions. Every institution defines its own complex of roles and relationships, its own repertoire of activities and genres, its own body of rules and customs, and so on. In particular, an institution *defines* the people, places, and things that take place within it. You don't have doctors without a medical system, or students without a school system, or intellectual property without intellectual property law, just as you don't have a pitcher without baseball or a goalie without soccer. Every institution, in other words, defines its own variety of memes, and the dynamics into which memes enter might differ considerably from one institution to the next. We can make this more concrete by pointing at the diverse genres of communication that institutions employ. Every meme, when it actually gets communicated, takes some generic form: an aphorism, a political speech, a knock-knock joke, a theorem, a blues song, a tragedy, etc. Each of these genres plays its own part in a certain institution, or in a fairly small number of institutions. The social mechanism by which a meme of the blues gets reproduced is quite different from the social mechanism by which a meme of mathematics gets reproduced. You have different social roles, different kinds of publication and performance, different incentives, different rules for who gets to quote whom and how, different kinds of influence and fashion, and so on. The concept of "meme" promises that we can formulate meaningful generalizations about the methods by which memes reproduce, but once we investigate the question seriously it becomes unclear whether such generalizations are likely to exist. Does that mean that we can generalize about institutions instead, that institutions and not memes are actually the natural kinds of the social world? Yes, actually, up to a point. We can observe that institutions tend to reproduce themselves, and we can list some of the typical mechanisms by which they do so, for example by shaping the cognition of their participants. But "institution" is an analytical category, and not the sort of concept they have in physics, and so the real action only takes place when the categories and generalizations are brought to bear in analyzing a particular case. We can study how the university reproduces itself, or political parties, or the stock market, and we can also study how those institutions change over time, and all of the many generalizations about institutions can serve as rules of thumb and as rough templates that suggest questions to ask and the general form of explanations to offer, assuming of course the facts turn out to fit the patterns that the theory suggests. What happens in practice is that the institution is a sprawling place, and with a good strong concept you can find *something* in the institution that fits the pattern. Then someone else working from a different set of categories finds something different -- not incompatible, one hopes, but a different take on things -- and then some theorist becomes renowned by explaining how the different analytical approaches can be reconciled. In classes I give students a handful of concepts each week, tell them to write me 1000 words about how those concepts apply to the particular institution they know about, and then in the last few weeks I tell them to look at the overall picture that has emerged week-by-week and write thirty pages about it. This works. So you see what's wrong with the concept of a "meme" -- why it does not function adequately as an analytical category. Observe that I did not refute the "theory of memes" by providing contrary evidence to it. Rather, I explained why a "theory of memes" isn't even possible, at least not in a way that would be worth bothering with. Of course, it is still possible that someone will discover strong generalizations about memes that cut across institutional fields in a way that is entirely unforeseen. Zipf's Law turns up in the darnedest places, so who knows. But from the perspective of the savoir-faire about concepts that has been built up in the practice of public design, it does not seem likely. The critique of attention economics is similar, and you can probably recite it as well as I can. The core argument is that people's minds need to focus, that we have a hard time doing several things at once, and that "attention" is therefore a scarce commodity. Everyone has only 24 hours in a day, and so the situation invites economic concepts of the allocation of scarce resources. I have a limited amount of attention to allocate, and so I devise a strategy, make choices, trade some options against others, and so on. Starting from this point, it is easy enough to spin out a whole abstract theory of attentional economics. The problem is the car wreck that follows immediately when we try to apply this abstract theory to real situations in the world. The things we attend to are diverse, and they are diverse in the same way that memes are diverse: the school, things we attend to are different from the family things, which are different from the legal things, and so on. They are "different" in all of the ways that institutions are different, but in particular they are different in the particular ways that attentional economics cares about. Some of them are long-term states that we can remain adequately aware of without ever devoting any particular moment to them, just because the necessary information can be inferred from information that comes to us for other purposes. Others are more like patterns that emerge from information that we encounter by chance -- not randomly, since the world around us has its regularities that makes exposure to the pattern predictable, but not by any explicit decision to allocate our attention either. And there are lots of other configurations -- different ways in which we can be said to be "aware" of things in the context of particular institutions with their particular kinds of roles and relationships and situations. Now, you could say that this kind of diversity is exactly what the theory of attentional economics is about. But the point is, you are not going to be able to explain the substance, the particulars, of the "attentions" that people maintain, without going into the particulars of the institutions that define them. The same thing goes for the theory of memes: you can attempt a taxonomy of the different ways in which ideas get reproduced across the diverse environments provided by different institutions. But you are going to have to characterize that diversity. The point is not that it's impossible: of course you can study attention, and of course you can study ideas and how they get reproduced. What's required is to take institutional concepts as a starting-point, and then construct the needed concepts within that analytical context. Will you have any generalizations when you get done? No, if you mean generalizations in the sense of physics or even the sense of neoclassical economics with its aggressive leveling of diverse social phenomena to an old-fashioned physics of equilibrium. Yes, if you mean analytical frameworks that can be taken to particular cases and used to make sense of the attentional and intellectual phenomena that are to be found constituted within those particular institutionally organized settings. To construct this analytical framework, you will definitely need to conceptualize the diversity of forms that attention and ideas take in different institutional settings, and this is what concepts like genre are for. Perhaps you can start developing additional concepts that help describe what you see in the particular cases you study. That will be good. But it will be quite different from the theories of memes and attentional economics, which try to go it alone, building whole theories on the slender foundations of single small ideas taken out of all context. It takes more than a few concepts to describe social life. It takes dozens, easily. But the best place to start is with what I call medium-sized concepts: concepts like institution and genre that differentiate the various contexts of human life in a way that supports large numbers of generally useful rules of thumb. There are lots of general lessons to learn about public design, but that's one of them. ** People read here and elsewhere about digital civil liberties issues and they ask me "what can I do?". My answer is, "pick something". I tell them pick some a specific small issue, learn all about it, set up a Web page, make contact with others who are doing the same thing, make sure that their level of commitment is sustainable, and settle in for the long haul. The goal is to know more and last longer than the other side, spread information, make a nuisance of yourself, keep it on a low boil, don't get burned out, and the world will slowly catch up with you. A good example of this strategy is the following article: http://www.latimes.com/print/asection/20001228/t000123590.html It's about a retiree who has committed himself to the cause of public access to police records. Granted, this is a complex issue, one of those classic open-records-versus-privacy things, but one where there are plenty of clear-cut areas where the public ought to be able to get access to information that their tax money is paying to produce. Of course the cops call him a nut, complain that he's never satisfied, etc. But I'm sure he regards such comments as the rewards of the job. The importance of the advice to "pick something" is profound. When you first develop a concern with a political issue, your unconscious mind is telling you that you're all alone and that it's you against the whole world. Sure, you know about the ACLU. You read the news. But that doesn't affect your basic belief system. What affects your basic belief system is picking an issue and then making contact with the other people who have picked issues. Once you feel yourself part of a network, and once you feel the positive energy that flows in a network of like-minded issue advocates, then your belief system will sort itself out and you'll believe in democracy. The problem lies in the gap between your initial sense of existential isolation and your eventual hooking-up with the like-minded. I wonder how we can use the Internet to help people bridge that gap. What if everyone who calls the ACLU could get referred to a low-overhead online institution that suits them up for combat and wires them with a hard-bitten network of allies in one minute flat? Once the news spreads through the culture that such a thing is possible, lots more people will step forward. ** An article in the New York Times heralds the arrival of 400-channel digital Time-Warner television to New York: http://www.nytimes.com/2000/12/28/technology/28CHAN.html?pagewanted=all I hate television, if that doesn't make me some kind of elitist jerk, and the thought of 400 channels is about the most repulsive thing I can imagine. But hey, if someone else finds value in it then who I am I to judge. Something does bother me about the NY Times article, though, and I suspect about the reality that it reports: its constant mention of addiction: If more people seem to be walking around the city glassy-eyed these days, it may be a symptom of remote-itis. Or more precisely, from the more than 400 channels of television that come from the click of those buttons. DTV is quickly becoming New Yorkers' latest addiction. Dawn and Sal Fahrenkrug of Flushing, Queens, and their 13-year-old daughter, Dara, say they did not use to watch much television. Then they joined the DTV cult. "I couldn't live without it," said Ms. Fahrenkrug ... Many people say they still watch the same amount of television, although the newfound variety has turned them into addicts, watching everything from Animal Planet to Sino Television. Time Warner has decided to charge digital subscribers less per premium channel (a couple of dollars per month, instead of $6.95), on the assumption that the new options there are about 200 mean that consumers will sign up for many, many more. In short, they are banking on addiction. Sarah Gibson, a convert in Murray Hill, finds herself in a trance flipping from channel to channel. Call me what you will, but I don't think addiction is funny. Either these people are just making a joke, in which case it's not a good joke, or they do really mean addiction, in which case it's really not a good joke. And it's not just about digital television. I've been in the room when corporate marketing people talked seriously about wanting to make their product addictive, as if this were a good thing. Of course it *is* a good thing from a financial standpoint, if you're the dealer. But it's not a good thing in terms of the lives it ruins, the minds it blots out, the families it wrecks, the good deeds that go undone, and the general level of blight that addiction brings to the culture. People in 12-step groups often see the whole culture in terms of addiction, and I know just what they're talking about. When you've got thousands of advertising campaigns promoting addictive thought-forms and behavior, you can't possibly have a healthy culture. Now, I realize that television, like most addictive things, also has healthy uses. And I favor banning or medicalizing addictive things that have no useful purpose. Television is protected by free speech, so our queries about this talk of addiction aren't going to rise to the level of banning anything. Even so, I don't want to think that a world of infinite bandwidth is also a world in which every last potential for addiction has been thoroughly exploited. And even if it's not, I don't want people going around making jokes about it. (Someone's going to ask me what I thought of Steven Soderbergh's new film, "Traffic". "Traffic" is an intelligent, serious, involving, technically sophisticated film, worthier than anything the studios normally make. The film interweaves four stories, the best of which stars Benecio Del Toro, who is stunning as a Mexican cop, and its attempt to paint a sprawling canvas of the drug war sometimes works. But it also suffers from a weak script with generic dialogue, flat characters, excessive speechifying, and plot holes you could drive a truck through -- the action too often turns on people being two notches dumber than they would be in real life. Ultimately the drug war sweeps the director away just as it sweeps away his characters, and the film ends up feeling indecisive. As with a lot of good Hollywood movies, you'll enjoy yourself if you turn your brain off.) ** In my comments on Kahn and Wiener's "Year 2000" book, which appeared in 1967, I asserted that Moore's Law originated at a later date. It would seem that I was wrong; Intel's Web site claims that Moore first presented his Law in 1965: http://www.intel.com/intel/museum/25anniv/hof/moore.htm Intel, though, starts its graph only with the 4004, which was circa 1972, not acknowledging the particular computers that Moore must have had in mind. In fact I had thought that Moore formulated his law in 1972, so maybe I was actually thinking of the 4004's introduction. It also seems that I was uncharitable to Kahn and Wiener on a closely related point. They regarded as a "conservative" a prediction that "computer capacities [would] continue to increase by a factor of ten every two or three years until the end of the century". That's wrong if "computer capacities" means processor speed. But on the previous page (as I had noted) they defined computer capacity as the product of processor speed and memory capacity. By that measure they were about right, with computers becoming more powerful by something in the vicinity of 15 orders of magnitude. Since Moore did predate them, I assume that they were following his estimates. The remainder of my assessment still holds, however. For example, the idea that computers would become intelligent after a 15-orders-of-magnitude improvement (by their measure) was entirely unreasonable, and did not come true. ** Some URL's. election Justice Unrobed http://www.law.com/cgi-bin/gx.cgi/AppLogic+FTContentServer?pagename=law/View&c=Article&cid=ZZZW1FJFZGC&live=true&cst=1&pc=0&pa=0&s=News&ExpIgnore=true&showsummary=0 Untruisms of 2000 http://washingtonpost.com/wp-dyn/articles/A64924-2000Dec29.html High Court's Florida Decision Was Based on Political Distortion http://www.newsday.com/coverage/current/editorial/thursday/nd8132.htm Right and Wrong http://www.tnr.com/010101/notebook010101.html Almost Everything We Thought About the Florida Recount Is Wrong! http://slate.msn.com/code/kausfiles/kausfiles.asp?Show=12/28/2000 Black Precinct in Gulf County Theorizes about Botched Ballots http://www.herald.com/content/archive/news/elect2000/decision/109528.htm Blacks' Votes Were Discarded at Higher Rates, Analysis Shows http://www.herald.com/content/archive/news/elect2000/decision/072960.htm http://www.herald.com/content/archive/news/elect2000/decision/110966.htm http://www.herald.com/content/archive/news/elect2000/photoart/badvote1228.gif http://www.herald.com/content/archive/news/elect2000/photoart/votemachine1228.gif If the Vote Were Flawless... http://www.herald.com/content/archive/news/elect2000/decision/104268.htm Bush's Coup: The Fall of Democracy in America http://www.mormons.org/reflections/bush_coup.htm A Badly Flawed Election http://www.nybooks.com/nyrev/WWWfeatdisplay.cgi?20010111053F A Racial Gap in Voided Votes http://washingtonpost.com/wp-dyn/articles/A52733-2000Dec26.html other political stuff Photographs of Signs Enforcing Racial Discrimination http://lcweb.loc.gov/rr/print/085_disc.html Keeping an Eye on the Conservative Internet Media http://conwebwatch.tripod.com/ origins of the Whitewater hoax http://www.arktimes.com/001208coverstorya.html theological papers on projective identification http://www.theol.rug.nl/~vanderme/erasmus/texts/split_uk.htm http://www.theol.rug.nl/~vanderme/erasmus/texts/klein_uk.htm http://www.theol.rug.nl/~vanderme/erasmus/texts/winni_uk.htm intellectual property Copy Protection for Prerecorded Media http://www.research.ibm.com/resources/magazine/2000/number_2/solutions200.html#two The Web in 2001: Paying Customers http://www.useit.com/alertbox/20001224.html Hatch's New Tune http://tm0.com/sbct.cgi?s=112055577&i=288031&d=799620 everything else The Future of Telecommunications and Networking http://www.interesting-people.org/200012/0082.html "The depth and degree of the universal hatred of unsolicited commercial e-mail by Internet users is amazing." http://www.mailorder.com/news/Complaint/cdoc2_pg01.htm the Pentium IV mess http://www.emulators.com/pentium4.htm Group Plans to Launch Online K-12 Curriculum http://www.latimes.com/print/asection/20001228/t000123593.html Companies Turning Cool to Telecommuting Trend http://www.latimes.com/print/asection/20001228/t000123582.html end