the hopes and possibilities of friendships of mutual concern

Aristotle distinguished several types of friendship, ranging from forms in which friends simply provide one another with utility or pleasure to a form in which friends seek out not just the pleasures or benefits of association but far more: one another’s virtues and excellence (their “real possibilities” some might say today).” The type of friendship from which we should consider learning is therefore not the friendship of long affection and intimacy, but the friendship of mutual concern, of care and respect for the other’s practice of citizenship, their full participation in the political world. This is the friendship of appreciation of the hopes and political possibilities of the other, the friendship recognizing, too, the vulnerabilities of those hopes and possibilities.” 

- from John Forester’s book, The Deliberative Practitioner  (1999, MIT Press)

the problem with problems

"The lesson here is not that situations determine actions, but that practical rationality depends far less on formulas or recipes than on a keen grasp of the particulars seen in the light of more general principles and goals. Taking practice stories more seriously, we ironically make decision making less central to practice and make the prior acts of problem construction, agenda setting, and norm setting more important (Murdoch 1970:37)."

- from John Forester’s book, The Deliberative Practitioner  (1999, MIT Press)

For my son’s fifth birthday, my wife and I bought him 10 pounds of LEGOs from eBay. It turns out, when kids move out of the house and leave behind a drawer full of LEGOs, some parents box them up, weigh them, and sell them. This works great for a 5-year-old with an imagination, but not if he or she wants to build a particular thing. LEGO is about imagination, but it is also about instructions and putting together sets around particular themes (space, Star Wars, etc.). Simply buying LEGOs by the pound doesn’t serve this purpose. The situation is the same with books or databases in a library. You need to invest in people who can organize these purchased (or more often these days, licensed) materials.

As a big believer in why LEGOs are a perfect metaphor for how our networked knowledge and combinatorial creativity work, I was especially moved by the above passage from R. David Lankes’s altogether excellent book-length essay on building better libraries for today’s complex world.

(In 1945, Vannevar Bush made a similar argument in his remarkably prescient essay on the future of information, predicting “a new profession of trail blazers, those who find delight in the task of establishing useful trails through the enormous mass of the common record.”)

The implications of grue - Mark Kingwell - Concrete Reveries

The transnational global city is the most significant machine our species has ever produced. Each massive conurbation, from Shanghai to Seattle, Toronto to Tokyo, is a testament to the human “desire to master nature,” that general drive for order, cleanliness and beauty, which Freud puts at the centre of the civilizing project. At the dawn of the twenty-first century, it is only a small exaggerations to say that cities are us, and we are cities. And yet, we fail, again and again, to understand them correctly. Almost all of our models or metaphors for thinking about cities are inadequate - not excepting the idea of “machine” used just a moment ago. Jane Jacobs labeled cities “problems in organized complexity,” an accurate tag that nevertheless stands out as an example of defining without definition. 

Cities are not biological entities, though they exhibit certain organic features, such as growth, disease and decline; they are not battlefields, though they are often riven by violence; they are not markets, though goods and services (also, to be sure, bodily fluids and air and excrement) are exchanged in massive volume through their various conduits, physical and otherwise. Nor are they architecture, despite being in large part accumulations of buildings. The urbanist Kevin Lynch identifies five attempts at a unifying model for the city; an organism, an economic engine, a communications network, a system of linked decisions and an arena of conflict. But these labels or models serve only to extend the problem they mean to solve. Each of these five models is both accurate and limited. While they may be rivals in terms of attention or resources, the models are neither exclusive nor exhaustive. The truth of one does not entail the falsity of another, and so multiple models may apply at once; and no single model explains or covers all the phenomena of a given city, so one model will not do. They are like incomplete transparent overlays on a flip chart: each model (with its associated explanatory power and ideological assumptions) can be added or subtracted at will; but no amount of manipulation of the overlays will explain the city as such. 

One reason for this is that all such models are diachronic; they use given time-slice analysis to try to predict, and so plan, future events. But prediction in cities, as in life, is a confidence game. Even good predictions now are likely to generate bad ones in future if the model remains static as the city changes. The philosopher Frank Cunningham, taking a page from analytic epistemology, argues usefully that so far from being inductively predictable, cities are grue-like. 

“Grue” is a term of art coned by the philosopher Nelson Goodman for an imaginary colour that is now green but at some unknown future point will be blue. Two things that at time T are both apparently green, but one is actually true, meaning that at time T+n will be blue. (There is a complementary colour called “bleen” with the reverse properties: blue, then green). We might wish to argue, by induction, the proposition that all emeralds are green  because all emeralds so far discovered have been green. Thus the corollary predictive proposition that emeralds will be green in the future. But if T+n has not yet passed, all emeralds so far discovered are also true. So how can we say, based on present experience of their being green, that emeralds will be green in the future? 

Grue captures the general problem with inductive reasoning: namely, predicting future events based on our experience of present ones is ensnared in a contradiction. Grue, says Cunningham, “exposes a limit to the reliability of expectations based on experience: observations supporting a belief that something is green equally support its being true.” According to David Hume’s well-known proto-grue argument against induction, we cannot prove that the sun will rise tomorrow based on our experience of past sunrises because reliability of experience cannot be employed as a premise in a proof attempting to establish reliability of experience. Cities make concrete an issue that, with the example of true, is merely a thought experiment. We cannot rely on our past and present experience of cities to predict future events in them; and yet, our past and present experience of cities, including their being subject sometimes to rapid unpredictable change, is all we have to go on. 

The practical implications of grue (rather than green) cities is that models must be employed with skepticism. Likewise, predictions and plans must submit to a basic provisionality when it comes to what is being predicted or planned. A simpler way of expressing this caution is to say that cities are not systems or markets or arenas but rather, collisions: of natural conditions, material forces and human desire. Like automobile and aircraft crashes or other literal collisions, they may obey certain general laws (or law-like generalities), but beneath these, they are a tangle of vectors and imponderables. We can search through the wreckage with a find toothed comb and still not determine precisely what happened to get us her; an, even could we know that, it would not prevent every possible future crash, only possibly minimize the risk of some. 

Lest that metaphor seem too morbid, consider that cities are also, on this anti-indeuctive view, like persons. That is, they are forms of embodied consciousness — neither minds nor bodies conceived separately from the other but concoctions of both. Just as a person is not a mind using a body, or a body invaded by mind, a city is reducible to neither its citizens nor its material base, its built structure. Nor, for that matter, is it susceptible to any simple ordering of priority between the two. A neutron-bombed city is not a city but a ruin; a city without the structures to match its citizens’ aspirations and dreams is perhaps still a city in name but an incomplete and devastated one — another kind of ruin. 

All of this is really to say that cities are places. That may sound obvious (or merely deranged); but the ostensible obviousness of the concept belies a depth of challenge. What, after all, is a place? We say; it is an area of significance, a physical staging ground. But is is more than that. It is somewhere that matters, where we find or lose ourselves, where understanding good and bad is forced upon us. Places are environments, sites of action, horizons of concern. They are infused with our aspirations and beliefs, reflecting and shaping them both. Finding your way means moving from place to place — even if most of the time, we do not think consciously about it, lost in the reveries of our projects and aims. 

(pp 12-14); Concrete Reveries: Consciousness and the City; Mark Kingwell - Viking Canada (2008)

Technology is society made durable (Latour)

If we abandon the divide between material infrastructure on the one hand and social superstructure on the other, a much larger dose of relativism is possible. Unlike scholars who treat power and domination with special tools, we do not have to start from stable actors, from stable statements, from a stable repertoire of beliefs and interests, nor even from a stable observer. And still, we regain the durability of social assemblage, but it is shared with the nonhumans thus mobilised. When actors and points of view are aligned, then we enter a stable definition of society that looks like domination. When actors are unstable and the observers’ points of view shift endlessly we are entering a highly unstable and negotiated situation in which domination is not yet exerted.

The analyst’s tools, however, do not have to be modified and the gradient that discriminates between more and less stable assemblages does not correspond in the least to the divide between technology and society. It is as if we might call technology the moment when social assemblages gain stability by aligning actors and observers. Society and technology are not two ontologically distinct entities but more like phases of the same essential action. By replacing those two arbitrary divisions with syntagm and paradigm, we may draw a few more methodological conclusions. The description of socio-technical networks is often opposed to their explanation, which is supposed to come afterwards. Critics of the sociology of science and technology often suggest that even the most meticulous description of a case-study would not suffice to give an explanation of its development. This kind of criticism borrows from epistemology the difference between the empirical and the theoretical, between ‘how’ and ‘why’, between stampcollecting - a contemptible occupation - and the search for causality - the only activity worthy of attention.

Yet nothing proves that this kind of distinction is necessary. If we display a socio-technical network - defining trajectories by actants’ association and substitution, defining actants by all the trajectories in which they enter, by following translations and, finally, by varying the observer’s point of view - we have no need to look for any additional causes. The explanation emerges once the description is saturated. We can certainly continue to follow actants, innovations, and translation operations through other networks, but we will never find ourselves forced to abandon the task of description to take up that of explanation. The impression that one can sometimes offer in the social sciences an explanation similar to those of the exact sciences is due precisely to the stabilization of networks, a stabilization that the notion of explanation simply does not ‘explain’!

Explanation, as the name indicates, is to deploy, to explicate. There is no need to go searching for mysterious or global causes outside networks. If something is missing it is because the description is not complete. Period. Conversely, if one is capable of explaining effects of causes, it is because a stabilized network is already in place.

Our second conclusion relates to relativism and the heterogeneity of networks. Criticisms of studies of controversy insist on the local, soft, and inconsistent nature of the results. They have the impression that network analysis recreates ‘that night when all the cows are grey’ ridiculed by Hegel. Yet networks analysis tends to lead us in exactly the opposite direction. To eliminate the great divides between science/society, technology/science, macro/micro, economics/research; humans/non-humans, and rational/irrational is not to immerse ourselves in relativism and indifferentiation.

Networks are not amorphous. They are highly differentiated, but their differences are fine, circumstantial, and small; thus requiring new tools and concepts. Instead of ‘sinking into relativism’ it is relatively easy to float upon it.

Finally, we are left with the accusation of immorality, apoliticism, or moral relativism. But this accusation makes no more sense than the first two. Refusing to explain the closure of a controversy by its consequences does not mean that we are indifferent to the possibility of judgement, but only that we refuse to accept judgements that transcend the situation. For network analysis does not prevent judgement any more than it prevents differentiation. Efficiency, truth, profitability, and interest are simply properties of networks, not of statements.

Domination is an effect not a cause. In order to make a diagnosis or a decision about the absurdity, the danger, the amorality, or the unrealism of an innovation, one must first describe the network. If the capability of making judgements gives up its vain appeals to transcendance, it loses none of its acuity.

Source: conclusion of Bruno Latour’s essay “Technology is society made durable

Stampcollecting! Causation seeking! Some high quality sentences in there. 

beyond the stable state

From Donald Schön’s 1971 work Beyond the Stable State

“I have believed for as long as I can remember in an afterlife within my own life — a calm, stable state to be reached after a time of troubles. When I was a child, that afterlife was Being Grown Up. As I have grown older, its content has become more nebulous, but the image of it stubbornly persists.

The afterlife-within-my-life is a form of belief in what I would like to call the Stable State. Belief in the stable state is belief in the unchangeability, the constancy of central aspects of our lives, or belief that we can attain such constancy. Belief in the stable state is wrong and deep in us. We institutionalize it in every social domain. We do this in spite of our talk about change, our acceptance of change and our approval of dynamism. Language about change is for the most part talk about very small change, trivial in relations to a massive unquestioned stability; it appears formidable to its proponents only by a peculiar optic that leads a potato chip company to see a larger bag of potato chips as a new product. Moreover, talk about change is as often as not a substitute for engaging in it.”

The crisis which emerges from the erosion of this stable state, the onslaught of change and turbulence that exists in the “modern world,” one that Schön and others apparently felt acutely in the late 1960′s and early 1970′s when this text was written and is still felt today some 40 years later, is then described in the following pages:

“In these situations there is not a lack of information. There is not an “information gap”. There is an information overload, too many signals, more than can be accounted for; and there is as yet not theory in terms of which new information can be sought or new experiments undertaken. “Uncertainty” is a way of talking about the situation in which no plausible theory has emerged. For this reason, pragmatism is no response. We cannot in these situations, say “Let us get the data,” “Let us experiment,” “Let us test,” for there is as yet nothing to test. Out of the uncertainty, out of the experience of a bewildering array of information, new hypotheses must emerge — and from them, mandates for gathering data, testing, experiment, can be derived. But in the first instance they do not as yet exist, and until they exist the method of pragmatism cannot be applied. The period of uncertainty must be traversed in order that pragmatism may become an appropriate response.

The feeling of uncertainty is anguish. The depth of anguish increases as the threatening changes strike at more central regions of the self. In the last analysis, the degree of threat presented by a change depends on its connection to self-identity. Against all of this we have erected our belief in the stable state.”

Schön goes onto outline 3 typical responses to the erosion of the stable state: return to an idyllic past-state (which is not really achievable, nor did the past state ever really exist), revolt (“reactionary radicalism”), and mindlessness (drugs, violence, etc.). All are deemed unproductive organizational responses (no kidding) and he then works his way towards the main premise of his book, a more positive response in the form of a learning organization: a networked, adaptive model that is responsive to flux and change.

More from Schön:

“Constructive responses to the loss of the stable state must confront the phenomenon directly. They must do so at the level of the institution and of the person.

  • If our established institutions are threatened with disruption, how can we invent and bring into being new or modified institutions capable of confronting challenges to their stability without freezing and without flying apart at the seams?
  • If we are losing stable values and anchors for personal identity, how can we maintain a sense of self-respect and self-identity while in the very process of change?

The present work is an effort to come to grips with these questions. It proceeds on the following assumptions:

  • The loss of the stable state means that our society and all of its institutions are in continuing processes of transformation. We cannot expect new stable states that will endure even for our own lifetimes.
  • We must learn to understand, guide, influence, and manage these transformations. We must make the capacity for undertaking them integral to ourselves and our institutions.
  • We must, in other words, become adept at learning. We must become able not only to transform our institutions, in response to changing situations and requirements; we must invent and develop institutions which are ‘learning systems,’ that is to say, systems capable of bringing about their own continuing transformation.
  • The task which the loss of the stable state makes imperative, for the person, for our institutions, for our society as a whole is to learn about learning.

What is the nature of the process by which organizations, institutions and societies transform themselves?

What are the characteristics of effective learning systems?

What are the forms and limits of knowledge that can operate within processes of social learning?

What demands are made on a person who engages in this kind of learning?

These are the questions we will be asking in the pages that follow. ”

While written in 1971, sounding very contemporary. 

the postmodernity of big data

Though both are projects that address positions about empiricism and meaning making, postmodernism and Big Data are in some senses opposites: Big Data is an empirically grounded quest for truth writ large, accelerated by exponentially expanding computing power. Postmodernism casts doubt on the very idea that reason can unearth an inalienable truth. Whereas Big Data sees a plurality of data points contributing to a singular definition of the individual, postmodernism negates the idea that a single definition of any entity could outweigh its contingent relations. Big Data aims for certainties — sometimes called “analytic insights” — that fly in the face of postmodernist doubt about knowledge. Postmodernism was confined to the faculty lounge and the academic conference, but Big Data has the ability to dictate new rules of behavior and commerce. An e-commerce outfit is almost foolish not to analyze browsing data and algorithmically determine likely future purchases, or as Jaron Lanier put it in Who Owns the Future, “your lack of privacy is someone else’s wealth.”




Stowe’s great summary of a brilliant interview series with many people whom I admire and respect. Honoured to be included in such esteemed company.