[A paper I read] describes how European maps of the African continent evolved from the 15th to the 19th centuries.
You might have supposed that the process would have been more or less linear: as European knowledge of the continent advanced, the maps would have shown both increasing accuracy and increasing levels of detail. But that's not what happened. In the 15th century, maps of Africa were, of course, quite inaccurate about distances, coastlines, and so on. They did, however, contain quite a lot of information about the interior, based essentially on second- or third-hand travellers' reports. Thus the maps showed Timbuktu, the River Niger, and so forth. Admittedly, they also contained quite a lot of untrue information, like regions inhabited by men with their mouths in their stomachs. Still, in the early 15th century Africa on maps was a filled space.
Over time, the art of mapmaking and the quality of information used to make maps got steadily better. The coastline of Africa was first explored, then plotted with growing accuracy, and by the 18th century that coastline was shown in a manner essentially indistinguishable from that of modern maps. Cities and peoples along the coast were also shown with great fidelity.
On the other hand, the interior emptied out. The weird mythical creatures were gone, but so were the real cities and rivers. In a way, Europeans had become more ignorant about Africa than they had been before.
It should be obvious what happened: the improvement in the art of mapmaking raised the standard for what was considered valid data. Second-hand reports of the form "six days south of the end of the desert you encounter a vast river flowing from east to west" were no longer something you would use to draw your map. Only features of the landscape that had been visited by reliable informants equipped with sextants and compasses now qualified. And so the crowded if confused continental interior of the old maps became "darkest Africa", an empty space.
Of course, by the end of the 19th century darkest Africa had been explored, and mapped accurately. In the end, the rigor of modern cartography led to infinitely better maps. But there was an extended period in which improved technique actually led to some loss in knowledge.
Between the 1940s and the 1970s something similar happened to economics. A rise in the standards of rigor and logic led to a much improved level of understanding of some things, but also led for a time to an unwillingness to confront those areas the new technical rigor could not yet reach. Areas of inquiry that had been filled in, however imperfectly, became blanks. Only gradually, over an extended period, did these dark regions get re-explored.
...So why didn't high development theory get expressed in formal models? Almost certainly for one basic reason: high development theory rested critically on the assumption of economies of scale, but nobody knew how to put these scale economies into formal models.
...Economic theory is essentially a collection of models... Like it or not, however, the influence of ideas that have not been embalmed in models soon decays. And this was the fate of high development theory. Myrdal's effective presentation of the idea of circular and cumulative causation, or Hirschman's evocation of linkages, were stimulating and immensely influential in the 1950s and early 1960s. By the 1970s (when I was myself a student of economics), they had come to seem not so much wrong as meaningless. What were these guys talking about? Where were the models? And so high development theory was not so much rejected as simply bypassed.
I have just acknowledged that the tendency of economists to emphasize what they know how to model formally can create blind spots; yet I have also claimed that the insistence on modeling is basically right. What I want to do now is call a time out and discuss more broadly the role of models in social science.
...how do you know that the model is good? It will never be right in the way that quantum electrodynamics is right. At a certain point you may be good enough at predicting that your results can be put to repeated practical use, like the giant weather-forecasting models that run on today's supercomputers; in that case predictive success can be measured in terms of dollars and cents, and the improvement of models becomes a quantifiable matter. In the early stages of a complex science, however, the criterion for a good model is more subjective: it is a good model if it succeeds in explaining or rationalizing some of what you see in the world in a way that you might not have expected.
...[To build models,] you make a set of clearly untrue simplifications to get the system down to something you can handle; those simplifications are dictated partly by guesses about what is important, partly by the modeling techniques available. And the end result, if the model is a good one, is an improved insight into why the vastly more complex real system behaves the way it does.
...there are highly intelligent and objective thinkers who are repelled by simplistic models for a much better reason: they are very aware that the act of building a model involves loss as well as gain. Africa isn't empty, but the act of making accurate maps can get you into the habit of imagining that it is. Model-building, especially in its early stages, involves the evolution of ignorance as well as knowledge; and someone with powerful intuition, with a deep sense of the complexities of reality, may well feel that from his point of view more is lost than is gained.
...The problem is that there is no alternative to models. We all think in simplified models, all the time. The sophisticated thing to do is not to pretend to stop, but to be self-conscious -- to be aware that your models are maps rather than reality.
...When I look at the Murphy et al representation of the Big Push idea, I find myself wondering whether the long slump in development theory was really necessary. The model is so simple: three pages, two equations, and one diagram. It could, it seems, have been written as easily in 1955 as in 1989. What would have happened to development economics, even to economics in general, if someone had legitimized the role of increasing returns and circular causation with a neat model 35 years ago?
One would like to draw some morals from this story. It is easy to give facile advice. For those who are impatient with modeling and prefer to strike out on their own into the richness that an uninhibited use of metaphor seems to open up, the advice is to stop and think. Are you sure that you really have such deep insights that you are better off turning your back on the cumulative discourse among generally intelligent people that is modern economics? But of course you are.
And for those, like me, who basically try to understand the world through the metaphors provided by models, the advice is not to let important ideas slip by just because they haven't been formulated your way. Look for the folk wisdom on clouds -- ideas that come from people who do not write formal models but may have rich insights. There may be some very interesting things out there. Strangely, though, I can't think of any.
The truth is, I fear, that there's not much that can be done about the kind of apparent intellectual waste that took place during the fall and rise of development economics. A temporary evolution of ignorance may be the price of progress, an inevitable part of what happens when we try to make sense of the world's complexity.