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Why We Can’t Agree

Howard Darmstadter considers different ways of seeing the world.

A wildebeest on the African plain – aware of much in its environment, unaware of much else. The presence of suitable grasses, the whereabouts of predators, and the actions of other wildebeest, get its attention; but wind currents, the flights of birds, and the doings of small mammals are of no concern.

The wildebeest’s awareness requires mental ‘models’ to represent environmental features. Certain features of the model correspond to certain features of the environment, and relations between features of the model correspond to relations between features of the environment. It’s like the correspondence between an accurate road map and a road system: Circles on the map correspond to cities, lines to roads, and the small distances between the map circles are proportional to the real distances between cities. However, mental models also map the environment dynamically as well as statically, and can be used to predict future experience from current. From environmental clues the wildebeest anticipates food or predators over the next hill. Of course mental models can be defective, predicting food or safety where there is none, with painful and sometimes terminal results.

Above the wildebeest soars an eagle with different mental models. Grass means little to it, as do wildebeest and other large animals; but wind currents, birds, and small mammals are important, and so are represented. Humans in the same landscape have still different models. A native hunter’s models may take account of signs indicating the presence of edible plants, game animals, and dangerous predators, whereas a geologist’s models may miss these signs but be alive to rock formations showing the area’s geological history.

The Uses of Different Models

I spoke of effective models as accurately representing the environment whilst defective models do not, but it’s a matter of degree. Just as all maps distort, so do all models. An effective model is simply one that often gives useful representations in those situations that most often confront and are most important to the model user.

Philosophers since Thales (7th C. BCE) have insisted – and modern science confirms – that observable events are controlled by unseen regularities. Today most of us believe that the world is made up of tiny wave-particles – electrons, gluons, and so forth. Models of this unseen world differ radically from simple models of the surface world, but we need the simpler models to run most of our lives. You can’t cook a dinner, manage an investment portfolio, or write a philosophy article using quantum theory.

There’s only one world out there, but no usable model can represent all its complexities. People in different situations with different needs may opt for different, and conflicting, models. We must each settle for those simplifications that suit our particular circumstances, accepting that occasionally the roast will burn, the investment sour, the article be rejected.

Maps once again offer a helpful analogy. All flat maps of a spherical world contain distortions: Is Greenland really that big relative to other countries? Different maps are accurate and inaccurate in different ways, so we shuttle between maps as our needs change. No one map, or model, can get it all right. Nor can we simply add different, individually accurate models together to get a single perfectly accurate model. Just as you can’t combine a flat Mercator projection map with a globe to get a single representation, so you can’t normally add two representations without changing features of one, or both. Maps have to simplify, and all simplifications distort.

And that’s why we can’t agree: There are psychological limits to our representational powers. The multi-model theory presented here doesn’t say that all models are equal; far from it. Wildebeest, eagles, and people are more likely to prosper with effective models than defective ones. But even models that are effective for some people or species in some situations will be less effective for others in different situations with different wants.

Scientists understand model limitations quite well, as when they try to construct a computer model of an organism:

“The point of a model is to remove unnecessary, cluttering details, while preserving the essence of whatever it is the model-maker wants to study. But even for an organism as well-researched as C. elegans, no one is sure which details are crucial and which extraneous. A living cell is a complicated mess of enzymes, ion channels, messenger molecules and voltages. Attempting to simulate everything faithfully would bring even a supercomputer to its knees.”
(The Economist, 24 May 2014)

Caenorhabditis elegans, the organism whose complexity proves too much for a supercomputer, is a 1mm-long 959-celled nematode. What hope, therefore, for a single all-purpose model of, say, a nation’s economic system?

Models and Human Society

The theory sketched here is itself a model: a simplification to address our needs in our particular situation. What makes this model useful for us?

We don’t feel challenged in learning that wildebeest and eagles use models that conflict with our own. We accept that evolution has shaped their models to their needs. We may feel challenged, however, when confronting humans whose models differ from our own. A common response is to try to convert them to our own models. In a small-scale society – a tribe or an office – it may be advantageous that everyone have similar models. But in a global society we must often deal with people whose situations and wants are different from our own. A multi-model understanding tells us that such differences may make conversion unlikely. Once we give up on conversion, we may look for those mutually beneficial accommodations that are possible even when models differ.

© Howard Darmstadter 2015

Howard Darmstadter is a retired lawyer and philosophy professor.

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