Rethinking The Power Of Maps

[Edco Haglund]

AsI've said a few times now, all models are wrong. That includes laws and norms, employee handbooks and product roadmaps, system diagrams and city plans. If something is abstracting away from reality, it's creating a gap. What happens when that gap causes problems?

In his frustrating but provocative book The Utopia of Rules, David Graeber introduces the term "interpretive labor". He defines interpretive labor as the work of "trying to decipher others' motives and perceptions". He then starts using the term "imaginative labor", which he doesn't define, and which he seems to conflate with interpretive labor. (I said it was a frustrating book!)

I find Graeber's terminology confusing, and so I'd like to try my own definitions. Going forward, let us say that imaginative labor is the work of model-building, while interpretive labor is the work of bridging the model and reality. A product designer performs imaginative labor, while a customer support representative performs interpretive labor. The city planner performs imaginative labor, while the construction team performs interpretive labor. And so on.

(I find the incoherence of Graeber's definitions kind of funny, since he writes: "This confusion, this jumbling of different conceptions of imagination, runs throughout the history of leftist thought." You are not immune, dear professor. And if my definitions are also confusing, I suppose I am carrying on a great tradition!)

Imagine a retail employee given two conflicting instructions: 'always get a receipt before refunding an item' and 'the customer is always right'. Their handbook has no guide for how to resolve the contradiction. Whatever the employee's solution, the work put into finding it is interpretive labor. The pedestrian on the street outside, trying to judge whether it's safe to cross against the light, is performing interpretive labor too. So is the bus driver hesitating to let a passenger on when they don't have a fare.

Let's return yet again to the Toyota worker on the factory floor, faced with a flaw in the part passing by. They have to decide whether Toyota's rules about when to halt production apply, and this is interpretive labor. Toyota encouraged interpretive labor by creating a safe learning environment.

General Motors adopted the same process, but not the safe learning environment. They discouraged workers from halting production, saying that those who did so were lazy and trying to get out of work. The workers were left with little incentive to perform the interpretive labor of spotting problems and acting on them. The gaps between the "ideal car" and the "real car" might have been noticed by workers, but they were less likely to be reported.

Sometimes this happens because interpretive labor (or really, the interpretive laborer) is blamed for things that go wrong. In Nancy Leveson's Engineering a Safer World, she documents how individual operators are often blamed for accidents. For example, railroad higher ups blamed individual workers for getting themselves killed when trying to couple trains. Similarly, aircraft accidents are often blamed on pilots:

When we blame individuals for problems, we fail to look at the full system. Other issues that contributing to the failure are ignored, and thus cannot be reported to system designers.

Interestingly, interpretive labor performed successfully can also cause feedback to go unreported, allowing system designers to think that the system is working fine.

Imagine a maintenance worker given instructions to turn a room's heat to seventy degrees Celsius. "That can't be right," they think, reasonably, "the person who wrote this must have meant Fahrenheit." And so they turn the heat to a moderate 70 F, having correctly interpreted the author's intent. But the instructions are passed on to other workers as-is, and one day someone else reads them and thinks, "sounds hot, but okay".

Suddenly, there's another accident. And perhaps the individual will be blamed for it. "They should've known we couldn't want the building that hot!" their bosses might say. But that is not really the issue. The issue is not even that there was a typo in the instructions. Typos happen. The problem is that the feedback about the typo was never passed to someone who could correct it. There was no channel for information from the worker on the ground to the designer of the system.

Just a few days ago, John Bull wrote a tweet thread about the "trust thermocline". The thermocline is a metaphor taken from large bodies of water, which get gradually cooler the deeper you dive, until the water suddenly becomes frigid. In product development, users can gradually grow more and more annoyed, and then suddenly leave all at once.

When customers are unhappy with a product, but the cost of leaving is too high, they will will perform the interpretive labor necessary to keep the product in their lives. This labor is often invisible to product designers, but it is usually visible to user-facing workers. Bull writes:

The answer, in other words, is to have good systems of feedback which value and respond to the interpretive labor being performed by both users and user-facing workers. Unfortunately, this is a hard answer for privileged people to hear.

In a 2021 interview with Charlie Warzel, social media researcher Erin Gallagher explained how agents had been manipulating social media to influence elections for years, but it went largely ignored because it wasn't happening in the United States:

Social media companies, journalists, and lawmakers could have responded to the problems being reported on in Latin America. They could have learned from the work already being done by Latin American civic leaders and activists. Instead they were ignored until United States elections started to be clearly and transparently targeted.

In each of these cases, there are people who have identified a problem with the model (the model of election security, disease, caregiving, etc). But because the people who best understand the problems are from groups with less power, their needs and their feedback have been ignored. And in each case, the problem has only grown.

In the previous section I listed some examples where the gap between models and reality impacted marginalized groups, and feedback from those groups was ignored. It's possible to read them and think, naively, that bias has an unfortunate side effect of making us disregard important feedback about dysfunctional systems. That may sometimes be true, but interpretive labor can also be a site of explicit power struggle.

If the government suddenly started drug-testing home mortgage interest deduction recipients, those recipients would protest, and in far more effective ways than welfare recipients are capable of. They would sue; they would donate to political opponents; they would write letters to the editor and be interviewed on cable news. They would send feedback up the chain and change the system. Their political power is precisely what keeps the government from foisting unnecessary interpretive labor onto them.

Welfare applicants, as a whole, have no such power. They can be forced to do the interpretive labor of filling out forms, taking drug tests, and petitioning for exceptions. This extra labor is both a symptom of their lack of power and a way to keep them from gaining power. Interpretive labor takes time and energy. It can leave you exhausted.

Part of how white people maintain dominance over people of color, and men over women, is by asserting a model of the world which is ignorant of the realities of less privileged groups. The lack of corrective feedback which would make their models more true is a feature, not a bug.

Systems only function when people are willing to perform the interpretive labor that bridges the gap between the formal and the actual. When laborers collectively withhold that work, they can bring the system to a halt.

Finally: civil disobedience. It is hard to imagine a more formalized or abstract model than a legal code. What is the reality that the model of law maps to? There's no single answer, but I would say that the reality law is meant to map to is one in which the people who live under the law are treated fairly, and flourish happily.

Civil disobedience occurs when a group of people are unhappy with the law, often because it treats them unfairly. Going along with the law, and absorbing the consequences of its unfairness, is a form of interpretive labor. With civil disobedience, groups choose to no longer perform that labor, and attempt to send corrective feedback to society at large. Sometimes this results in the system being changed.

I am aware that the framework I've sketched out here is as abstract and untested as a model can be. Every single example I've cited has a complex context which I'm not truly addressing. Nevertheless, I offer it as an initial attempt to develop what for me has become a keystone in how I see the world.

Despite attempts to build on the cartographic communication paradigm through an enhanced understanding of geovisualization, for example as a process from exploring unknowns to revealing knowns (MacEachren, 1995), the development of a broad theoretical framework with the clear goal of improving map design (at least with the international traction of the map communication models) has proved to be elusive. More importantly, the sustained lack of debate concerning the development of new theoretical frameworks in this applied discipline has widened the gulf between theory and practice. Professional cartographers struggle to make their maps relevant (Norwood and Cumming, 2012), while amateur map-makers are creating and disseminating curiosity-driven maps via social media that are liked and shared by tens of thousands. There is a need for a theoretical framework to support the creation and design of maps that better serve the needs of society.

In contrast with 1960s and 1970s, mass feedback can now be generated (however articulate and accurate) and received via social media, and maps can be subsequently edited, refined and re-issued in a fraction of the effort, time and cost. Rather than being finished, fixed and stable representations, digital maps are displayed in a multitude of formats and are constantly in flux. If the pursuit of optimal design solutions remains, there are now significant opportunities for user-focused cartographers to explore the use of feedback as a means for improving map design, which, according to Montello (2002: 298) should be considered in terms of its effectiveness for helping people to understand the world.

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