The Evolution of Ignorance
Paul Krugman’s recent talk of intuition vs. models has inspired me to dig up one of his old papers from 1994, “The Fall and Rise of Development Economics.” The paper is long but the theme is clear—knowing more sometimes causes us to know less. Here is an (extremely) abridged version:
Krugman uses the example of the mapping of Africa where “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…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.”
He parallels this to story of Hirschmann and how an idea was rejected by the economics community because it was impossible to be formally modeled—that is, until our modeling techniques got better and the idea was, well, modeled. Like it or not, this is the way modern economics works—with models as necessary simplifications of reality.
“Still, 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.
But that initial narrowing is very hard for broad minds to accept. And so they look for an alternative. 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.”
“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.”
I’ll stop my rampant butchering of the paper as there are many more interesting bits. If you’re interested, more here.