Reason is not purely literal, but largely metaphorical and imaginative. Reason is not dispassionate, but emotionally engaged. ... Rather, the mind is inherently embodied, reason is shaped by the body, and since most thought is unconscious, the mind cannot be known simply by self-reflection.
Mental Maps
Simplified models systematically fail in complex reality.
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You drive down the highway at sixty miles per hour, unperturbed by the fact that another car is coming your way in the opposite lane at the same speed. How do you know there won’t be a terrible collision? You unthinkingly assume that the driver (whom you can’t even see, and almost certainly don’t know) wants to stay alive and knows that the best way of doing that is staying on the right side of the road.
Those that are not quite rational but perhaps not a bad rule of thumb are called “heuristics.” Those that utterly fly in the face of reason are called “fallacies.”
Individual humans know embarrassingly little about the world, and as history has progressed, they have come to know less and less. A hunter-gatherer in the Stone Age knew how to make her own clothes, how to start a fire, how to hunt rabbits, and how to escape lions. We think we know far more today, but as individuals, we actually know far less.
A conceptual model is an explanation, usually highly simplified, of how something works. It doesn’t have to be complete or even accurate as long as it is useful. ... Simplified models are valuable only as long as the assumptions that support them hold true.
Suddenly, processes as disparate as the creation of permanent last names, the standardization of weights and measures, the establishment of cadastral surveys and population registers, the invention of freehold tenure, the standardization of language and legal discourse, the design of cities, and the organization of transportation seemed comprehensible as attempts at legibility and simplification. In each case, officials took exceptionally complex, illegible, and local social practices, such as land tenure customs or naming customs, and created a standard grid whereby it could be centrally recorded and monitored.
The physicists were shocked at the assumptions the economists were making—that the test was not a match against reality, but whether the assumptions were the common currency of the field.
On the one hand, we have been taught to analyze, to use our rational ability, to trace direct paths from cause to effect, to look at things in small and understandable pieces, to solve problems by acting on or controlling the world around us. That training, the source of much personal and societal power, leads us to see presidents and competitors, OPEC and the flu and drugs as the causes of our problems.
In 1972 the American meteorologist Edward Lorenz wrote a paper with an arresting title: “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?” A decade earlier, Lorenz had discovered by accident that tiny data entry variations in computer simulations of weather patterns—like replacing 0.506127 with 0.506—could produce dramatically different long-term forecasts. It was an insight that would inspire “chaos theory”: in nonlinear systems like the atmosphere, even small changes in initial conditions can mushroom to enormous proportions.
While awareness of the need for a causal model has grown by leaps and bounds among the sciences, many researchers in artificial intelligence would like to skip the hard step of constructing or acquiring a causal model and rely solely on data for all cognitive tasks. The hope—and at present, it is usually a silent one—is that the data themselves will guide us to the right answers whenever causal questions come up. I am an outspoken skeptic of this trend because I know how profoundly dumb data are about causes and effects.
Our mental models aren’t reality. They are tools, like the models weather forecasters use to predict the weather.