Struggle Forever!

A long time ago, back at Eidetic Illuminations, I wrote this post on what I called then (in a moment of pretension and vanity) Jeremy Trombley’s Economic Principle No. 1.  I called it Economic Principle No. 1 because, to me, it was (and still is) the most fundamental principle of economics – something that every economic theory should take for granted (but most don’t) and that should inform our economic choices.  The principle as formulated back then (and I like the vulgarity of it, so I’m not going to dress it up now):

Wealth may trickle down, but it fucking FLOWS upward!

What this means is that, in order to function appropriately, every economy needs a redistributive mechanism.  Without some kind of redistribution, wealth will tend to accumulate in the hands of fewer and fewer people creating an instability in the system – a blockage of flow, you might say. Every economy has some kind of redistribution – taxes, potlatch, gift economies, etc. – even our own. However, increasingly, what I think we see is a co-opting of redistribution such that the wealth goes back to the wealthy – creating the illusion of redistribution without the actual benefits.  In any case, I’ve covered all that before.  The point of this post is different.

I called this Economic Principle No. 1, and I stand by that.  However, thinking about it recently, I’ve realized that there’s a more fundamental issue – a necessary condition for Economic Principle No. 1 to be valid, which also goes largely unrecognized and has major economic implications.  That is:

Wealth is fluid.

In order for Economic Principle No. 1 to work, wealth must be understood to flow as a fluid rather than to be solid and static.  In fact, this is implicit in many of the ways we talk about wealth and money now – liquid assets, capital flows, etc., but I don’t think that the real implications are taken into consideration.  That wealth is fluid means that it’s about relations and flows rather than individual accumulation.  We have all probably heard parables of wealthy men with their gold on, say, a deserted island.  There the gold does the men no good, because it is not the substance that matters – not the artefact that bears the wealth – but the ability to exchange – to move wealth from one person to another and transform it from one kind of substance to another like the alchemist trick of turning lead into gold. If, on the other hand, we treat wealth as static, solid stuff, then the motivation will be to hold it, to keep it solid as much as possible, and allow it to accumulate.

All of this is fairly simplistic, I know.  Someday when I’ve finished my PhD and have time and resources to research whatever I want, I will indulge my interest in Economic Anthropology and explore these ideas in more complex and ethnographic contexts.  I kick myself for not having taken a course back at KU when I had the chance, but my schedule didn’t allow it.  Nevertheless, I believe that these principles are important for a reasonable understanding of economics, though they may not be deterministic and many complex factors may come into play in any given economy.  I wanted to put the idea out there, though, and see what kind of thought it generates.

“The bad news is you’re falling through the air, nothing to hang on to, no parachute. The good news is there’s no ground.”

-Chögyam Trungpa Rinpoche

I’ve used this quote before. It’s a beautiful sentiment and I stand by it, but it is misleading. The truth is, there is no ground except those we make for ourselves and others.

As Rinpoche’s quote suggests, a ground can be settling – providing a place for stable footing and an end to the perpetual cosmic fall. On the other hand a ground can be terrifying as one falls uncontrolably towards it.

If we can’t get rid of grounds and simply enjoy the fall, then we should at least try and have more of the former and fewer of the latter types.  One can never be sure, though, which type one is approaching when one is simply falling uncontrollably.

Another aspect of modeling that I’ve come across in my reading lately is the extent to which the practice disciplines model users in particular ways.  Modeling is not simply something that modelers do while sitting in front of the computer terminal, it is a practice that shapes the way modelers think and behave.  This is demonstrated effectively in three of the articles I’ve read: one by Anders Kristian Munk titled “Emancipating Nature: What the Flood Apprentice Learned froma Modelling Tutorial”, the second by Myanna Lahsen titled “Seductive Simulations? Uncertainty Distribution Around Climate Models”, and the third by Chunglin Kwa titled “Modelling Technologies of Control.”

In the first article, by Munk, the author describes his experience undergoing a tutorial course on flood modeling, and the various stages that one must go through in order to build even a simple flood model.  The course begins with the development of a “perceptual model” of flooding. Not a computer model, but a conceptual model sketched out with pen and paper, this practice allows the modeler to think through all of the different factors that might come into play in a flood scenario: evapotranspiration, rainfall, soil, vegetation, human settlement, etc.  The effect of this practice is to isolate out those factors that can be accounted for in the model and those that can’t.  It separates out nature as a distinct domain which the model speaks for, but separated from the politics that are ever-present in the causes, consequences, and management of flooding:

The model, which claims to speak on behalf of nature, can be asked its opinion on different possible outcomes of a political process, which claims to speak on behalf of society.  The answers can of course be fed back across the divide, but it seems important to the professional ethos of the modeller that a divide is maintained in spite of the tendency of successive flood events to transgress any such partition.  If our role vis-á-vis nature will be to anticipate, then our role vis-á-vis society will be to notify.  Anticipating nature; notifying society.”

Once the perceptual model has been produced, the process of computer modeling can begin.  This process involves a number of translations and reductions – making the perceptual model fit to the computer’s interface.  This is another disciplining process, through repeated interactions with the computer and software, the modeler comes to understand and react to what the simulation needs.  The modeler is being shaped by the model just as the model is shaped by the modeler (the two are mangled, to use Pickering’s term):

What feels like a real achievement is that the model is actually running after an unending stream of bugs and error messages.  Finally, we seem to be doing something right; we have learned how to feed it the correct stuff; it is complying.  Or rather, we are complying: at this point in the training exercise the happy amateurs are tuning in to the demands of the software and for the first time we get a sense of what it means to become a modeller.”

In the end, the author reflects on how the process of flood modeling – of separating out nature from society – depends on the production of “thoroughly post-natural hybrid.”  Through this process the modeler is transformed and remade, acquiring new habits, dispositions and affects that no doubt carry over into other aspects of her life.

The second article, by Lahsen, is meant as a critique of Mackenzie’s certainty trough.  The certainty trough is a model for how different groups (generally within the sciences) view a particular scientific process.  As the author describes, it uses a “distance trope” to differentiate the different groups.  Those who are closest to the production of the process are typically somewhat aware of the uncertainties embodied in the process; those who are not involved in the production but use the methods and results tend to be less conscious of the uncertainties and thus less skeptical about the process; finally, those who are outside of the process entirely – because they are committed, presumably, to an entirely different methodology – tend to have the highest skepticism of the process.  Lahsen identifies, using modeling as her example, several points that complicate the certainty trough: first, that there are generally many sites of production involved, so distance is not an effective metaphor; second, that the distinction between producers and users is not very clear; third, that “outsiders” are often called upon to help validate models; and finally, that there may be psychological and social reasons why modelers would not have an overabundance of confidence in their models.

It’s this last feature that I want to discuss in terms of discipline.  That there are social reasons why modelers would appear over-confident in their models seems straight-forward.  Modelers compete for funding and access to resources, so they are invested in making their models appear as accurate and valuable as possible.  However, the psychological effects that Lahsen describes are not so evident.  From her research, she shows that, as a result of continual contact with the model, come to see the simulation as a reality itself.  Instead of talking about the simulated ocean in the model, the modelers will talk about simply “the ocean.”  In effect, the modelers are “seduced” by the models into believing that they are the reality that they attempt to simulate.  This leads to an attachment to the model, and, again, an overconfidence in the model’s depiction of reality.

The third article by Kwa explores models as tools of control.  He delves into the history of modeling, including environmental/climate modeling, economic modeling, and modeling for military purposes, in order to show a relationship between modeling and different modes of control.  His primary example comes from John Von Neumann’s work with weather modeling.  Von Neumann, among others, envisaged a computer model of weather on a global scale, which could be used to control weather and transform the climate to better suit human needs.  Examples include the use of “cloud seeding” to promote rain in drought-ridden areas, or to hinder enemy troops and obscure aircraft during military operations.  Another, more extreme example, is the use of nuclear explosions to redirect monsoons to cool hotter climates and improve conditions for agriculture.  This period from about WWII to 1973 (Kwa is specific in this regard), is one where technologists imagined that models and other technologies could bring about total, centralized control on a global scale.  In some ways, it is the structure of models themselves which give the illusion of total control – the way they are laid out offering a gods-eye view of the world, and one that can be easily manipulated and observed.

According to Kwa, around 1973, there was a shift from this kind of large-scale modeling towards more localized modeling efforts with specific problems in mind.  He says that this was the result of the emergence of the counter-culture movements in the 1960s rather than any kind of specific failures of the models themselves.  The advent of the personal computer further promoted localization and specificity in modeling approaches.  Kwa suggests that this does not mean that the goal of control was abandoned with the earlier modeling methods, but that it shifted into this localized register.

In my mind, the question is what kinds of body-minds are produced out of these modeling practices? How do the habits, dispositions, and affects developed in front of a computer screen carry over into other aspects of life?  None of the authors address this specifically, but it’s an important question that I might try to investigate in my own research.  Furthermore, I would hope to answer the question what happens when modeling methods are changed?  What kinds of new subjectivities might be produced?  It’s a lot to think about.

The following quotes come from John Steinbeck’s The Log from the Sea of Cortez (co-written, supposedly, with Ed Ricketts, but he is not credited on my edition):

“… the Mexican sierra has ‘XVII-15-IX’ spines in the dorsal fin. These can easily be counted. But if the sierra strikes hard on the line so that our hands are burned, if the fish sounds and nearly escapes and finally comes in over the rail, his colors pulsing and his tail beating the air, a whole new relational externality has come into being – an entity which is more than the sum of the fish plus fisherman. The only way to count the spines of the sierra unaffected by this second relational reality is to sit in a laboratory, open an evil-smelling jar, remove a stiff colorless fish from formalin solution, count the spines, and write the truth ‘D. XVII-15-IX.’ There you have recorded a reality which cannot be assailed – probably the least important reality concerning either the fish or yourself.

“It is good to know what you are doing. The man with his pickled fish has set down one truth and has recorded in his experience many lies. The fish is not that color, that texture, that dead, nor does he smell that way.

…

“Let’s go wide open. Let’s see what we see, record what we find, and not fool ourselves with conventional scientific strictures. We could not observe a completely objective Sea of Cortez anyway, for in that lonely and uninhabited Gulf our boat and our selves would change it the moment we entered. By going there we would bring a new factor to the Gulf. Let us consider that factor and not be betrayed by this myth of permanent objective reality. If it exists at all, it is only available in pickled tatters or in distorted flashes. Let us go into the Sea of Cortez, realizing that we become forever a part of it; that our rubber boots slogging through a flat of eel-grass, that rocks we turn over in a tide pool, make us truly and permanently a factor in the ecology of the region. We shall take something away from it, but we shall leave something too.”

This is what I mean when I talk about the ontology (maybe I should say ecology) of knowledge production.  Producing knowledge is about more than the creation of symbolic and conceptual realities that either correspond to a reality “out there” or don’t depending on your epistemological outlook.  Producing knowledge is the production of relationships, connections, entanglements between beings.  The man in the lab with his pickled fish is one kind of relationship, and the fisherman on his boat with the thrashing, spiny, colorful fish is a different one.  Both entail the creation of knowledge – knowledge is one kind of relationship that is built out of the encounter – but also much more than that.  The very act of studying something, holding it in your hand, dissecting it, putting it in a glass jar (or, for those of us who study people, interviewing them, doing participant-observation, excavating a site, and so on) changes the thing and yourself.  A new relationship is made a new thing is made – knowledge, but so much more than that; a new way of being, a new form of entanglement.  Focusing exclusively on the production of knowledge in the form of epistemological symbols and concepts (as was the tendency in anthropology after the “linguistic turn” and Writing Culture) limits our perspective on the effects of our scientific practices and constrains our imagination of the many other possible sorts of relationships that could be composed.

To me, it is not an either-or issue.  Viewing knowledge production as a fundamentally ontological process broadens the scope of possibilities for research.  No longer does research have to be only about composing an image or representation of some thing – this is what got us (anthropologists, at least) into trouble in the first place!  Instead, research can be about building relationships – what kinds of relationships can we build, what kinds of relationships do we (the researcher and the subjects of her research – seen now as the collaborators they always were) want to build?

See the work of John Law on method for more.