Modeling and the Art of Noticing

Modeling gets a bad rap. They are too abstract, we say, too disconnected from reality! We want real, empirical research. We want sensitive and nuanced understandings of the way things work. Models, because they create artificial worlds, are dissociated from the real. Because they are based on numerical calculations, do not allow for nuance. Based on my experience with modeling and my discussions with modelers, I think these are misconceptions that only help to push us further away from our understanding of modeling and the kinds of noticing that can be done with models. Instead of disregarding models, we should engage more with them so that we can ensuring that modeling is done right – as an art of noticing – rather then allowing them to be misused.

Anna Tsing in her latest book calls for a science based on “arts of noticing.” These arts would involve a kind of thick description for the natural world that would focus on particular histories and localized processes. One example she offers is Japanese forestry, which recognizes the essential role that peasant societies have played in creating and maintaining the pine landscapes that foster matsutake mushrooms. By attending to these particularities, the foresters are able to forgo broad generalizations – e.g. erosion is bad – in favor of a more nuanced perspective – erosion creates soil conditions that favor pine rather than deciduous forests.

From this perspective models could easily be dismissed, because, on the surface, they are based in numerical abstractions rather than particular historical dynamics, and are divorced from reality. In fact, Tsing herself makes such a dismissive statement: “Natural history descriptions, rather than mathematics modeling, is the necessary first step – as in the economy” (144). However, as part of my dissertation research, I’ve been talking with modelers and doing modeling myself, and I think this is a mistaken conception of the process. It’s possible for models to be abstract and divorced from reality, but not a necessary part of their function. Modeling can be an art of noticing.

First, let me dispel the notion that models are not based in reality. They are – or rather, any good functioning scientific model must be. The numerical processes, and systems of differential equations that drive models are not derived from nothing, they are based on empirical observations of the real world. Scientists measure flows, concentrations, quantities, and other features of the world and then derive mathematical equations to accurately represent what they see. If the equations don’t match observations, then they are discarded and reformulated until they do – and this is an ongoing process because equations never perfectly match the observations. As a result of this ongoing process, modeling can reveal areas in which we are lacking information because we simply hadn’t thought to collect that kind of data. A good example of this from my research is an understanding of lag times for the movement of water from land to river. The models weren’t capturing some aspects of our observations, and so it was recognized that there is a lag time between when a drop of water hits the ground and when it flows down into the stream. It seems obvious, but isn’t how the models represented flow before. This sparked research into the way that water moves through the landscape and how long it takes to get from land to river – the answers have been astonishing. It can take years for water to migrate through the soil and percolate up into the water column. This has serious implications for nutrient management as it suggests that we are dealing much more with a legacy issue than an application issue.

You might say “Okay, so models are based in reality, but they’re still abstract – not based in the particularities of landscape and history.” History is a tricky one, but, I would argue, not necessarily the fault of the models – history can be applied alongside modeling rather than built into it and we can use the two to develop a much richer description of what’s going on in a landscape. It’s a question of the contexts in which we use the models, not a function of the models themselves, and we – as social scientists – need to push for the incorporation of history and broader social forces when we have the opportunity (this is one of my goals in my own research).

On the other hand, models are based in the physical particularities of a landscape – at least those that are made use of in particular landscapes are. It’s possible, of course, to create a model of an entirely artificial landscape to test out various numerical modeling methods. However, every model that I’ve seen is restructured and “calibrated” to the particular landscape involved – in my case, the Chesapeake Bay watershed. There are elements of the soil, the geology, the biotic environment, etc. that must be taken into consideration when applying a model. Sometimes it means that one model simply cannot be used because it has been calibrated to a very different landscape, and so researchers need to find one that will work in their region or build one from scratch. This has to do with the fact that, although they are mathematical and both the inputs and outputs are quantitative, it seems that the way models work internally is very qualitative. All of these mathematical processes converge in complex dynamics that resemble much more the flow of water than the calculation of values. As a result, models can help us understand things that are happening in a system that can’t be observed directly, but are a consequence of known dynamics. A good example of this is edge of stream movements of water. I can’t say that I understand it yet, but the flow of water at the edge of a stream is not something we can observe even with complex instruments. However, they are processes that result from known hydrodynamics, and so, when we run the models, we can get a sense of what’s going on in those places we can’t sense directly. That’s not to say that the modeled processes are the same as what actually happens, but it can help us understand.

All of this underscores that fact that we actually model all the time – modeling is an essential part of “noticing” that Tsing – for all the insight she provides – simply ignores. The forester out on the landscape is not simply taking in information with her senses, she is processing that information through a set of conceptions about the landscape that she has and then drawing conclusions. This process is always present – data requires models to be made sense of, but models must be altered when they cannot effectively make sense of the data. The friction – to use Tsing’s own model – between the conception and the observation can be a productive one, in other words. But this depends on how models are used. Models can be simply input-output streams that take quantitative data and turn it into more quantitative data – used to manage some aspect of the landscape (e.g. nutrient runoff). It is the interaction between reality and model that generates productive friction, and I think that’s the value of recognizing arts of noticing, and recognizing modeling as an essential part of those arts. If we maintain this model/reality split, then we essentially cede modeling to those who would use it for more abstract and insensitive approaches – global finance, neoliberal governance, etc. Instead, we must embrace modeling, and ensure that it is part of a broader art of noticing.


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