I recently published a column at KMWorld pointing out some of the benefits of having one’s thoughts share a context with people who build things. Today I came across an article by Jethro Masis titled “Making AI Philosophical Again: On Philip E. Agre’s Legacy.” Jethro points to a 1997 work by the greatly missed Philip Agre that says it so much better:
…what truly founds computational work is the practitioner’s evolving sense of what can be built and what cannot” (1997, p. 11). The motto of computational practitioners is simple: if you cannot build it, you do not understand it. It must be built and we must accordingly understand the constituting mechanisms underlying its workings.This is why, on Agre’s account, computer scientists “mistrust anything unless they can nail down all four corners of it; they would, by and large, rather get it precise and wrong than vague and right” (Computation and Human Experience, 1997, p. 13).
(I’m pretty sure I read Computation and Human Experience many years ago. Ah, the Great Forgetting of one in his mid-60s.)
Jethro’s article overall attempts to adopt Agre’s point that “The technical and critical modes of research should come together in this newly expanded form of critical technical consciousness,” and to apply this to Heidegger’s idea of Zuhandenheit: how things show themselves to us as useful to our plans and projects; for Heidegger, that is the normal, everyday way most things present themselves to us. This leads Jethro to take us through Agre’s criticisms of AI modeling, its failure to represent context except as vorhanden [pdf], (Heidegger’s term for how things look when they are torn out of the context of our lived purposes), and the need to thoroughly rethink the idea of consciousness as consisting of representations of an external world. Agre wants to work out “on a technical level” how this can apply to AI. Fascinating.
Here’s another bit of brilliance from Agre:
For Agre, this is particularly problematic because “as long as an underlying metaphor system goes unrecognized, all manifestations of trouble in technical work will be interpreted as technical difficulties and not as symptoms of a deeper, substantive problem.” (p. 260 of CHE)
Tagged with: agre
• too big to know
Date: December 7th, 2014 dw
Here’s the opening of my latest column at KMWorld:
A couple of weeks ago, I joined other former students of Joseph P. Fell at Bucknell University for a weekend honoring him. Although he is a philosophy professor, the takeaway for many of us was a reminder that while hands are useless without minds to guide them, minds need hands more deeply than we usually think.
Philosophy is not the only discipline that needs this reminder. Almost anyone—it’s important to maintain the exceptions—who is trying to understand a topic would do well by holding something in her hands, or, better, building something with them…
A year ago, Harold Feld posted one of the most powerful ways of framing our excessive zeal for copyright that I have ever read. I was welling up even before he brought Aaron Swartz into the context.
Harold’s post is within a standard Jewish genre: the d’var Torah, an explanation of a point in the portion of the Torah being read that week. As is expected of the genre, he draws upon a long, self-reflective history of interpretation. I urge you to read it because of the light it sheds on our culture of copyright, but it’s also worth noticing the form of the discussion.
The content: In the Jewish tradition, Sodom’s sin wasn’t sexual but rather an excessive possessiveness leading to a fanatical unwillingness to share. Harold cites from a collection of traditional commentary, The Ethics of Our Fathers:
“There are four types of moral character. One who says: ‘what is mine is mine and what is yours is yours.’ This is an average person. Some say it is the Way of Sodom. The one who says: ‘what is mine is yours and what is yours is mine,’ is ignorant of the world. ‘What is mine is yours and what is yours is yours’ is the righteous. ‘What is mine is mine and what is yours is mine’ is the wicked.”
In a PowerPoint, it’d be a 2×2 chart. Harold’s point will be that the ‘what is mine is mine and what is yours is yours.’ of the average person becomes wicked when enforced without compassion or flexibility. Harold evokes the traditional Jewish examples of Sodom’s wickedness and compares them to what’s become our dominant “average” assumptions about how copyright ought to work.
I am purposefully not explaining any further. Read Harold’s piece.
The form: I find the space of explanation within which this d’var Torah — and most others that I’ve heard — operates to be fascinating. At the heart of Harold’s essay is a text accepted by believers as having been given by God, yet the explanation is accomplished by reference to a history of human interpretations that disagree with one another, with guidance by a set of values (e.g., sharing is good) that persevere in a community thanks to that community’s insistent adherence to its tradition. The result is that an agnostic atheist like me (I’m only pretty sure there is no God) can find truth and wisdom in the interpretation of a text I take as being ungrounded in a divine act.
But forget all that. Read Harold’s post, bubbelah.
Google self-driving cars are presumably programmed to protect their passengers. So, when a traffic situation gets nasty, the car you’re in will take all the defensive actions it can to keep you safe.
But what will robot cars be programmed to do when there’s lots of them on the roads, and they’re networked with one another?
We know what we as individuals would like. My car should take as its Prime Directive: “Prevent my passengers from coming to harm.” But when the cars are networked, their Prime Directive well might be: “Minimize the amount of harm to humans overall.” And such a directive can lead a particular car to sacrifice its humans in order to keep the total carnage down. Asimov’s Three Rules of Robotics don’t provide enough guidance when the robots are in constant and instantaneous contact and have fragile human beings inside of them.
It’s easy to imagine cases. For example, a human unexpectedly darts into a busy street. The self-driving cars around it rapidly communicate and algorithmically devise a plan that saves the pedestrian at the price of causing two cars to engage in a Force 1 fender-bender and three cars to endure Force 2 minor collisions…but only if the car I happen to be in intentionally drives itself into a concrete piling, with a 95% chance of killing me. All other plans result in worse outcomes, where “worse” refers to some scale that weighs monetary damages, human injuries, and human deaths.
Or, a broken run-off pipe creates a dangerous pool of water on the highway during a flash storm. The self-driving cars agree that unless my car accelerates and rams into a concrete piling, all other joint action results in a tractor trailing jack-knifing, causing lots of death and destruction. Not to mention The Angelic Children’s Choir school bus that would be in harm’s way. So, the swarm of robotic cars makes the right decision and intentionally kills me.
In short, the networking of robotic cars will change the basic moral principles that guide their behavior. Non-networked cars are presumably programmed to be morally-blind individualists trying to save their passengers without thinking about others, but networked cars will probably be programmed to support some form of utilitarianism that tries to minimize the collective damage. And that’s probably what we’d want. Isn’t it?
But one of the problems with utilitarianism is that there turns out to be little agreement about what counts as a value and how much it counts. Is saving a pedestrian more important than saving a passenger? Is it always right try to preserve human life, no matter how unlikely it is that the action will succeed and no matter how many other injuries it is likely to result in? Should the car act as if its passenger has seat-belted him/herself in because passengers should do so? Should the cars be more willing to sacrifice the geriatric than the young, on the grounds that the young have more of a lifespan to lose? And won’t someone please think about the kids m— those cute choir kids?
We’re not good at making these decisions, or even at having rational conversations about them. Usually we don’t have to, or so we tell ourselves. For example, many of the rules that apply to us in public spaces, including roads, optimize for fairness: everyone waits at the same stop lights, and you don’t get to speed unless something is relevantly different about your trip: you are chasing a bad guy or are driving someone who urgently needs medical care.
But when we are better able control the circumstances, fairness isn’t always the best rule, especially in times of distress. Unfortunately, we don’t have a lot of consensus around the values that would enable us to make joint decisions. We fall back to fairness, or pretend that we can have it all. Or we leave it to experts, as with the rules that determine who gets organ transplants. It turns out we don’t even agree about whether it’s morally right to risk soldiers’ lives to rescue a captured comrade.
Fortunately, we don’t have to make these hard moral decisions. The people programming our robot cars will do it for us.
Imagine a time when the roadways are full of self-driving cars and trucks. There are some good reasons to think that that time is coming, and coming way sooner than we’d imagined.
Imagine that Google remains in the lead, and the bulk of the cars carry their brand. And assume that these cars are in networked communication with one another.
Can we assume that Google will support Networked Road Neutrality, so that all cars are subject to the same rules, and there is no discrimination based on contents, origin, destination, or purpose of the trip?
Or would Google let you pay a premium to take the “fast lane”? (For reasons of network optimization the fast lane probably wouldn’t actually be a designated lane but well might look much more like how frequencies are dynamically assigned in an age of “smart radios.”) We presumably would be ok with letting emergency vehicles go faster than the rest of the swarm, but how about letting the rich go faster by programming the robot cars to give way when a car with its “Move aside!” bit is on?
Let’s say Google supports a strict version of Networked Road Neutrality. But let’s assume that Google won’t be the only player in this field. Suppose Comcast starts to make cars, and programs them to get ahead of the cars that choose to play by the rules. Would Google cars take action to block the Comcast cars from switching lanes to gain a speed advantage — perhaps forming a cordon around them? Would that be legal? Would selling a virtual fast lane on a public roadway be legal in the first place? And who gets to decide? The FCC?
One thing is sure: It’ll be a golden age for lobbyists.
I’ve posted [pdf] a terrible scan that I made of a talk given by Joseph P. Fell in Sept. 1970. “What is philosophy?” was presented to a general university audience, and in Prof. Fell’s way, it is both clear and deep.
Prof. Fell was my most influential teacher when I was at Bucknell, and, well, ever. He was and is more interested in understanding than in being right, and certainly more than in being perceived as right. This enables him to model a philosophizing that is both rigorous and gentle.
Although I’ve told him more than once how much he has affected my life, he is too humble to believe it. So I’m telling you all instead.
Tagged with: gratitude
Date: February 11th, 2014 dw
The history of Western philosophy usually has a presumed shape: there’s a known series of Great Men (yup, men) who in conversation with their predecessors came up with a coherent set of ideas. You can list them in chronological order, and cluster them into schools of thought with their own internal coherence: the neo-Platonists, the Idealists, etc. Sometimes, the schools and not the philosophers are the primary objects in the sequence, but the topology is basically the same. There are the Big Ideas and the lesser excursions, the major figures and the supporting players.
Of course the details of the canon are always in dispute in every way: who is included, who is major, who belongs in which schools, who influenced whom. A great deal of scholarly work is given over to just such arguments. But there is some truth to this structure itself: philosophers traditionally have been shaped by their tradition, and some have had more influence than others. There are also elements of a feedback loop here: you need to choose which philosophers you’ll teach in philosophy courses, so you you act responsibly by first focusing on the majors, and by so doing you confirm for the next generation that the ones you’ve chosen are the majors.
But I wonder if in one or two hundred years philosophers (by which I mean the PT-3000 line of Cogbots™) will mark our era as the end of the line — the end of the linear sequence of philosophers. Rather than a sequence of recognized philosophers in conversation with their past and with one another, we now have a network of ideas being passed around, degraded by noise and enhanced by pluralistic appropriation, but without owners — at least without owners who can hold onto their ideas long enough to be identified with them in some stable form. This happens not simply because networks are chatty. It happens not simply because the transmission of ideas on the Internet occurs through a p2p handoff in which each of the p’s re-expresses the idea. It happens also because the discussion is no longer confined to a handful of extensively trained experts with strict ideas about what is proper in such discussions, and who share a nano-culture that supersedes the values and norms of their broader local cultures.
If philosophy survives as anything more than the history of thought, perhaps we will not be able to outline its grand movements by pointing to a handful of thinkers but will point to the webs through which ideas passed, or, more exactly, the ideas around which webs are formed. Because no idea passes through the Web unchanged, it will be impossible to pretend that there are “ideas-in-themselves” — nothing like, say, Idealism which has a core definition albeit with a history of significant variations. There is no idea that is not incarnate, and no incarnation that is not itself a web of variations in conversation with itself.
I would spell this out for you far more precisely, but I don’t know what I’m talking about, beyond an intuition that the tracks end at the trampled field in which we now live.
, too big to know
Tagged with: 2b2k
Date: December 28th, 2013 dw
Jérôme Hergeux is giving a Berkman lunch talk on “Cooperation in a peer prodiuction economy: experimental evidence from Wikipedia.” He lists as co-authors: Yann Algan, Yochai Benkler, and Mayo Fuster-Morell.
NOTE: Live-blogging. Getting things wrong. Missing points. Omitting key information. Introducing artificial choppiness. Over-emphasizing small matters. Paraphrasing badly. Not running a spellpchecker. Mangling other people’s ideas and words. You are warned, people.
Jérôme explains the broader research agenda behind the paper. People are collaborating on the Web, sometimes on projects that compete with or replace major products from proprietary businesses and institutions. Standard economic theory doesn’t have a good way of making sense of this with its usual assumptions of behavior guided by perfect rationality and self-interest. Instead, Jérôme will look at Wikipedia where people are not paid and their contributions have no signaling value on the labor market. (Jérôme quotes Kizor: “The problem with Wikipedia is that it only works in practice. In theory it can never work.”)
Instead we should think of contributing to Wikipedia as a Public Goods dilemma: contributing has personal cost and not enough countervailing personal benefit, but it has a social benefit higher than the individual cost. The literature has mainly focused on the “prosocial preferences” that lead people to include the actions/interets of others, which leads them to overcome the Public Goods dilemma.
There are three classes of models commonly used by economists to explain prosocial behavior:
First, the altruism motive. Second, reciprocity: you respond in kind to kind actions of others. Third, “social image”: contributing to the public good signals something that brings you other utility. (He cites Napoleon: “Give me enough meals and I will win you any war.”)
His research’s method: Elicit the social prefs of a representative sample of Wikipedia contributors via an online experiment, and use those preferences to predict subjects’ field contributions to the Wikipedia project.
To check the reciprocity motive, they ran a simple public goods game. Four people in a group. Each has $10. Each has to decide how much to invest in a public project. You get some money back, but the group gets more. You can condition your contribution on the contributions of the other group members. This enables the researchers to measure how much the reciprocity motive matters to you. [I know I’m not getting this right. Hard to keep up. Sorry.] They also used a standard online trust game: You get some money from a partner, and can respond in kind.
Q: Do these tests correlate with real world behavior?
A: That’s the point of this paper. This is the first comprehensive test of all three motives.
For studying altruism, the dictator game is the standard. The dictator can give as much as s/he wants to the other person. The dictator has no reason to transfer the money. This thus measures altruism. But people might contribute to Wikipedia out of altruism just to their own Wikipedia in-group, not general altruism (“directed altruism”). So they ran another game to measure in-group altruism.
Social image is hard to measure experimentally, so they relied on observational data. “Consider as ‘social signalers’ subjects who have a Wikipedia user page whose size is bigger than the median in the sample.” You can be a quite engaged contributor to Wikipedia and not have a personal user page. But a bigger page means more concern with social image. Second, they looked at Barnstars data. Barnstars are a “social rewarding practice” that’s mainly restricted to heavy contributors: contribute well to a Wikipedia article and you might be given a barnstar. These shows up on Talk pages. About half of the people move it to their user page where it is more visible. If you move one of those awards manually to your user page, Jérôme will count you as a social signaller, i.e., someone who cares about his/her image.
He talks about some of the practical issues they faced in doing this experiment online. They illustrated the working of each game by using some simple Flash animations. And they provided calculators so you could see the effect of your decisions before you make them.
The subject pool came from registered Wikipedia users, and looked at the number of edits the user has made. (The number of contributions at Wikipedia follows a strong power law distribution.) 200,000 people register at Wikipedia account each month (2011) but only 2% make ten contributions in the their first month, and only 10% make one contribution or more within the next year. So, they recruited the cohort of new Wikipedia contributors (190,000 subjects), the group of engaged Wikipedia contributors (at least 300 edits) (18,989), and Wikipedia administrators (1,388 subjects). To recruit people, they teamed up with the Wikimedia Foundation to put a banner up on a Wikipedia page if the user met the criteria as a subject. The banner asked the reader to help with research. If readers click through, they go to the experiment page where they are paid in real money if they complete the 25 minute experiment within eight hours.
The demographics of the experiment’s subjects (1,099) matched quite closely the overall demographics of those subject pools. (The pool had 9% women, and the experiment had 8%).
Jérôme shows the regression tables and explains them. Holding the demographics steady, what is the relation between the three motives and the number of contributions? For the altruistic motive, there is no predictive power. Reciprocity in both games (public and trust) is a highly significant predictive. This tells us that reciprocal preference can lead you from being a non-contributor to being an engaged contributor; once you’re an engaged contributor, it doesn’t predict how far you’re going to go. Social image is correlated with the number of contributions; 81% of people who have received barnstars are super-contributors. Being a social signaler is associated with a 130% rise in the number of contributions you make. By both user-page length and barnstar, social image motivates for more contributions even among super-contributors.
Reciprocity incentivizes contributions only for those who are not concerned about their social image. So, reciprocity and social image are both at play among the contributors, but among separate groups. I.e., if you’re motivated by reciprocity, you are likely not motivated by social image, and vice versa.
Now Jérôme focuses on Wikipedia administrators. Altruism has no predictive value. But Wikipedia participation is negatively associated with reciprocity; perhaps this is because admins have to have thick skins to deal with disruptive users. For social image, the user page has significant revelance for admins, but not barnstars. Social image is less strong among admins than among other contributors.
Jérôme now explores his “thick skin hypothesis” to explain the admin results. In the trust game, look at how much the trustor decides how much to give to the stranger/partner. Jérôme ’s hypothesis: Among admins, those who decide to perform more of their policing role will be less trusting of strangers. There’s a negative correlation among admins between the results from the trust game and their contributions. The more time they say they do admin edits, the less trusting they are of strangers in the tests. That sort of make sense, says Jérôme. These admins are doing a valuable job for which they have self-selected, but it requires dealing with irritating people.
Q: Maybe an admin is above others and is thus not being reciprocated by the group.
A: Perfectly reasonable explanation, and it is not ruled out by the data.
Q: Did you come into this with an idea of what might motivate the Wikipedians?
A: These are the three theories that are prevalent. We wanted to see how well they map onto actual field behavior.
Q: Maybe the causation goes the other way: working in Wikipedia is making people more concerned about social image or reciprocity?
A: The correlations could go in either direction. But we want to know if those explanations actually match what people do in the field.
Q: Heather Ford looks at why articles are deleted for non-Western topics. She found the notability criteria change for people not close to the topics. Maybe the motives change depending on how close you are to the event.
A: Sounds fascinating.
Q: Admins have an inherent bias in that they focus on the small percentage of contributors who are annoying jerks. If you spend your time working with jerks, it affects your sense of trust.
A: Good point. I don’t have the data to answer it.
Q: [me] If I’m a journalist I’m likely to take away the wrong conclusions from this talk, so I want to make sure I’m understanding. For example, I might conclude that Wikipedia admins are not motivated by altruism, whereas the right conclusion is (isn’t it?) that the standard altruism test doesn’t really measure altruism. Why not ask for self-reports to see?
A: Economists are skeptical about self-reports. If the reciprocity game predicts a correlation, that’s significant.
Yochai Benkler: Altruism has a special meaning among economists. It refers to any motivation other than “What’s in it for me?” [Because I asked the question, I didn’t do a good job recording the answers. Sorry.]
Q: Aren’t admins control freaks?
A: I wouldn’t say that. But control is not a pro-social motive, and I wanted to start with the theories that are current.
Q: You use the number of words someone writes on a user page as a sign of caring about social image, but this is in an context where people are there to write. And you’re correlating that to how much they write as editors and contributors. Maybe people at Wikipedia like to write. And maybe they write in those two different places for different reasons. Also, what do you do with these findings? Economists like to figure out which levers we pull if we’re not getting enough contributors.
Q: This sort of data seems to work well for large platforms with lots of users. What’s the scope of the methods you’re using? Only the top 100 web sites in the world?
A: I’d like to run this on all the peer production platforms in the world. Wikipedia is unusual if only because it’s been so successful. We’re already working on another project with 1,000 contributors at SourceForge especially to look at the effects of money, since about half of Open Source contributions are for money.
Fascinating talk. But it makes me want to be very dumb about it, because, well, I have no choice. So, here goes.
We can take this research as telling us something about Wikipedians’ motivations, about whether economists have picked the right three prosocial motivations, or about whether the standard tests of those motivations actually correlate to real-world motivations. I thought the point had to do with the last two alternatives and not so much the first. But I may have gotten it wrong.
So, suppose instead of talking about altruism, reciprocity, and social image we instead talk about the correlation between the six tests the researchers used and Wikipedia contributions. We would then have learned that Test #1 is a good predictor of the contribution levels of beginner Wikipedians, Test #2 predicts contributions by admins, Test #3 has a negative correlation with contributions by engaged Wikipedians, etc. But that would be of no interest, since we have (ex hypothesis) not made any assumptions about what the tests are testing for. Rather, the correlation would be a provocation to more research: why the heck does playing one of these odd little games correlate to Wikipedian productivity? It’d be like finding out that Wikipedian productivity is correlated to being a middle child or to wearing rings on both hands. How fascinating!… because these correlations have no implied explanatory power.
Now let’s plug back in the English terms that indicate some form of motivation. So now we can say that Test #3 shows that scoring high in altruism (in the game) does not correlate with being a Wikipedia admin. From this we can either conclude that Wikipedia admins are not motivated by altruism, or that the game fails to predict the existing altruism among Wikipedia admins. Is there anything else we can conclude without doing some independent study of what motivates Wikipedia admins? Because it flies in the face of both common sense and my own experience of Wikipedia admins; I’m pretty convinced one reason they work so hard is so everyone can have a free, reliable, neutral encyclopedia. So my strong inclination – admittedly based on anecdote and “common sense” (= “I believe what I believe!”) – is to conclude that any behavioral test that misses altruism as a component of the motivation of someone who spends thousands of hours working for free on an open encyclopedia…well, there’s something hinky about that behavioral test.
Even if the altruism tests correlate well with people engaged in activities we unproblematically associate with altruism – volunteering in a soup kitchen, giving away much of one’s income – I’d still not conclude from the lack of correlation with Wikipedia admins that those admins are not motivated by altruism, among other motivations. It just doesn’t correlate with the sort of altruism the game tests for. Just ask those admins if they’d put in the same amount of time creating a commercial encyclopedia.
So, I come out of Jérôme’s truly fascinating talk feeling like I’ve learned more about the reliability of the tests than about the motivations of Wikipedians. Based on Jérôme’s and Yochai’s responses, I think that’s what I’m supposed to have learned, but the paper also seems to be putting forward interesting conclusions (e.g., admins are not trusting types) that rely upon the tests not just correlating with the quantity of edits, but also being reliable measures of altruism, self-image, and reciprocity as motives. I assume (and thus may be wrong) that’s why Jérôme offered an hypothesis to explain the lack-of-trust result, rather than discounting the finding that admins lack trust (to oversimplify it).
(Two concluding comments: 1. Yochai’s The Leviathan and the Penguin uses behavioral tests like these, as well as case studies and observation, to make the case that we are a cooperative species. Excellent, enjoyable book. (Here’s a podcast interview I did with him about it.) 2. I’m truly sorry to be this ignorant.)
Now, as part of the settlement, the school district has agreed to treat the child as a boy. Thus does an entire institution find itself compelled to accept the cultural left’s moral categories and priorities. This is why the Times labels transgender “the next civil rights frontier.” There’s always one, isn’t there?
This is from Ron Dreher’s post at The American Conservative about “Progressivism’s Next Battle.”
But what interests me is his comment, “There’s always one, isn’t there?” You can practically hear the sigh.
Well, yes, Ron, there is always one. Progressives are progressive because we believe in progress, and we believe in progress because — generalizing, of course — we believe three basic things.
First, human understanding is conditioned by history, culture, language. We are products of our times.
Second, our understanding tends towards some serious errors. For example, we tend to prefer the company of — and to trust — people who are like us. Worse, we go seriously wrong in judging the relevant ways people are like us, giving far too much weight to differences that make no real difference.
Third, we humans are capable of learning. When it comes to policies and institutions, the great lesson that we keep learning and need to keep learning is that few of the differences actually matter. Put positively, we need to keep learning that people are actually more like us than we thought. The great progressive impulse is to find more and more common humanity, and to adjust our policies around that truth. (And, as an aside that I both believe, and I hope Ron Dreher will find annoying: Nope, it doesn’t end with humans. We need to stop torturing and killing animals because we like the way they taste.)
So, yes, there always is a next frontier. But it’s not because progressives are sneaky land grabbers who are never satisfied. It’s because we are committed to the endless process of discovering our common humanity, and thus becoming fully human.
I’m ok with that.
Tagged with: politics
Date: August 1st, 2013 dw
In 1960, the academic journal Technology and Culture devoted its entire Autumn edition  to essays about a single work, the fifth and final volume of which had come out in 1958: A History of Technology, edited by Charles Singer, E. J. Holmyard, A. R. Hall, and Trevor I. Williams. Essay after essay implies or outright states something I found quite remarkable: A History of Technology is the first history of technology.
You’d think the essays would have some clever twist explaining why all those other things that claimed to be histories were not, perhaps because they didn’t get the concept of “technology” right in some modern way. But, no, the statements are pretty untwisty. The journal’s editor matter-of-factly claims that the history of technology is a “new discipline.” Robert Woodbury takes the work’s publication as the beginning of the discipline as well, although he thinks it pales next to the foundational work of the history of science , a field the journal’s essays generally take as the history of technology’s older sibling, if not its parent. Indeed, fourteen years later, in 1974, Robert Multhauf wrote an article for that same journal, called “Some Observations on the State of the History of Technology,” that suggested that the discipline was only then coming into its own. Why some universities have even recognized that there is such a thing as an historian of science!
The essay by Lewis Mumford, whom one might have mistaken for a prior historian of technology, marks the volumes as a first history of technology, pans them as a history of technology, and acknowledges prior attempts that border on being histories of technology.  His main objection to A History of Technology— and he is far from alone in this among the essays — is that the volumes don’t do the job of synthesizing the events recounted, failing to put them into the history of ideas, culture, and economics that explain both how technology took the turns that it did and what the meaning of those turns meant for human life. At least, Mumford says, these five volumes do a better job than the works of three British nineteenth century who wrote something like histories of technology: Andrew Ure, Samuel Smiles, and Charles Babbage. (Yes, that Charles Babbage.) (Multhauf points also to Louis Figuier in France, and Franz Reuleaux in Germany.)
Mumford comes across as a little miffed in the essay he wrote about A History of Technology, but, then, Mumford often comes across as at least a little miffed. In the 1963 introduction to his 1934 work, Technics and Civilization, Mumford seems to claim the crown for himself, saying that his work was “the first to summarize the technical history of the last thousand years of Western Civilization…” . And, indeed, that book does what he claims is missing from A History of Technology, looking at the non-technical factors that made the technology socially feasible, and at the social effects the technology had. It is a remarkable work of synthesis, driven by a moral fervor that borders on the rhetoric of a prophet. (Mumford sometimes crossed that border; see his 1946 anti-nuke essay, “Gentlemen: You are Mad!” ) Still, in 1960 Mumford treated A History of Technology as a first history of technology not only in the academic journal Technology and Culture, but also in The New Yorker, claiming that until recently the history of technology had been “ignored,” and “…no matter what the oversights or lapses in this new “History of Technology, one must be grateful that it has come into existence at all.”
So, there does seem to be a rough consensus that the first history of technology appeared in 1958. That the newness of this field is shocking, at least to me, is a sign of how dominant technology as a concept — as a frame — has become in the past couple of decades.
 Techology and Culture. Autumn, 1960. Vol. 1, Issue 4.
 Melvin Kranzberg. “Charles Singer and ‘A History of Technology'” Techology and Culture Autumn, 1960. Vol. 1, Issue 4. pp. 299-302. p. 300.
 Robert S. Woodbury. “The Scholarly Future of the History of Technology” Techology and Culture Autumn, 1960. Vol. 1, Issue 4. pp. 345-8. P. 345.
 Robert P. Multhauf, “Some Observations on the State of the History of Technology.” Techology and Culture. Jan, 1974. Vol. 15, no. 1. pp. 1-12
 Lewis Mumford. “Tools and the Man.” Techology and Culture Autumn, 1960. Vol. 1, Issue 4. pp. 320-334.
 Multhauf, p. 3.
 Lewis Mumford. Technics and Civilization. (Harcourt Brace, 1934. New edition 1963), p. xi.
 Lewis Mumford. “Gentlemen: You Are Mad!” Saturday Review of Literature. March 2, 1946, pp. 5-6.
 Lewis Mumford. “From Erewhon to Nowhere.” The New Yorker. Oct. 8, 1960. pp. 180-197.
CNN.com has posted my op-ed about the Rolling Stone cover that features Dzhokhar Tsarnaev. It’s not the favorite thing I’ve ever written, but I had about an hour to do a draft.
There are two things I know I’d change without even going through the scary process of re-reading it:
First, CNN edited out any direct assertion that the Tsarnaev’s are guilty. So, there are some “alleged”s awkwardly inserted, and some language that works around direct attribution of guilt. I’m in favor of the presumption of innocence, of course. But inserting the word “alleged” is a formalism without real effect, except when the allegedly alleged murderer’s lawyers call. But, I get it. (CNN also removed some of the links I’d put, including to the cover itself and to the Wikipedia NPOV policy.)
Second, I wanted to say something more directly about the distinction between the sympathy that feels bad for someone’s troubles and the sympathy that lets us understand where the person is coming from. My post too quickly rules out sympathy of any kind because I knew that if I asked for sympathetic understanding, many readers would accuse me of feeling sympathetic toward the perpetrator rather than toward his victims, as if one rules out the other. So, I opted to strike any positive use of the term. (Of course that didn’t stop many of the commenters from claiming that I’m excusing the Tsarnaevs. Ridiculous.)
So, I’ll say it here: sympathetic understanding is a crucial human project, and, in truth, it often does lead toward sympathetic feelings. For example, in The Executioner’s Song, Norman Mailer leads us through the story of the mass killer Gary Gilmore, providing explanations that implicitly run the gamut from psychological to economic to social to Nietzschean to Freudian . Inevitably we do feel some emotional sympathy for Gilmore, although without thinking him one whit less culpable. It sucked to be Gary Gilmore, and that doesn’t mean it didn’t suck far worse to be one of his victims.
In the same way, the common narrative about the Tsarnaev brothers (which I, too, accept) is that the older brother was a rotten apple who drew the younger brother into evil. I think the story of how the younger brother became “radicalized” — in quotes because it is an exteriorized word — is more interesting to most of us than how the older brother got there. If and when they make the movie, the part of the younger brother will be the plum role. That’s the narrative that has been served to us, and there are reasons to think that it’s basically right: the younger brother didn’t show signs of radicalization — his friends were genuinely shocked — while the older brother did. (The Rolling Stone article elaborates this narrative by explaining not only how the younger came to his beliefs, but also by showing that he kept the change hidden.) This narrative also fits well into our cultural narrative about youth being innocent until corrupted. But my point is not that the narrative is true or false or both or neither. It is that we generally hold to that narrative in this case, and it is a narrative that naturally engenders some element of emotional sympathy for the corrupted youth. And what’s wrong with that? Sympathy doesn’t have to take sides. Our judgment does that. Understanding how Dzhokhar Tsarnaev went from innocent to a murderer of children (allegedly!) and what it was like to be him doesn’t mean that I hold him less culpable, that I want his sentence reduced, or — most importantly — that I now have less sympathy for his victims. Indeed, the whole power of The Narrative depends upon our continuing horror at what he did.
To say otherwise is to deny the power of narrative and art. It means we should ban not just The Executioner’s Song but also In Cold Blood, Crime and Punishment and even Madame Bovary, each of which bring us to both cognitive and emotional sympathy for people who did bad things . It is also to deny that evil is the act of humans and thus is a possibility for each of us, at least in a “there but for the grace of God” sense. And, to my way of thinking, our outrage at any attempt to understand those who commit incontrovertibly evil acts is intended exactly to silence that scariest of thoughts.
 It’s been decades since I read The Executioner’s Song, so I’m probably misrepresenting which exact explanatory theories Mailer employs.
 I know Madame Bovary is different because her acts of adultery even within the frame of the book are so thoroughly understandable.
 At the last minute when finishing this post, I removed a sentence referencing Hannah Arendt’s complex phrase “the banality of evil.” It raised too many issues for a final paragraph. (And I have a sense I will regret including Madame Bovary in the list. See footnote 2.)
Tagged with: blogs
Date: July 20th, 2013 dw
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