Marshaling the black ants: Practical knowledge in a small village in Malaysia

One of my favorite bits from James Scott’s Seeing Like a State:

While doing fieldwork in a small village in Malaysia, I was constantly struck by the breadth of my neighbors’ skills and their casual knowledge of local ecology. One particular anecdote is representative. Growing in the compound of the house in which I lived was a locally famous mango tree. Relatives and acquaintances would visit when the fruit was ripe in the hope of being given a few fruits and, more important, the chance to save and plant the seeds next to their own house. Shortly before my arrival, however, the tree had become infested with large red ants, which destroyed most of the fruit before it could ripen. It seemed nothing could be done short of bagging each fruit. Several times I noticed the elderly head of household, Mat Isa, bringing dried nipah palm fronds to the base of the mango tree and checking them. When I finally got around to asking what he was up to, he explained it to me, albeit reluctantly, as for him this was pretty humdrum stuff compared to our usual gossip. He knew that small black ants, which had a number of colonies at the rear of the compound, were the enemies of large red ants. He also knew that the thin, lancelike leaves of the nipah palm curled into long, tight tubes when they fell from the tree and died. (In fact, the local people used the tubes to roll their cigarettes.) Such tubes would also, he knew, be ideal places for the queens of the black ant colonies to lay their eggs. Over several weeks he placed dried nipah fronds in strategic places until he had masses of black-ant eggs beginning to hatch. He then placed the egg-infested fronds against the mango tree and observed the ensuing week-long Armageddon. Several neighbors, many of them skeptical, and their children followed the fortunes of the ant war closely. Although smaller by half or more, the black ants finally had the weight of numbers to prevail against the red ants and gain possession of the ground at the base of the mango tree. As the black ants were not interested in the mango leaves or fruits while the fruits were still on the tree, the crop was saved.

This successful field experiment in biological controls presupposes several kinds of knowledge: the habitat and diet of black ants, their egg-laying habits, a guess about what local material would substitute as movable egg chambers, and experience with the fighting proclivities of red and black ants. Mat Isa made it clear that such skill in practical entomology was quite widespread, at least among his older neighbors, and that people remembered something like this strategy having worked once or twice in the past. What is clear to me is that no agricultural extension official would have known the first thing about ants, let alone biological controls; most extension agents were raised in town and in any case were concerned entirely with rice, fertilizer, and loans. Nor would most of them think to ask; they were, after all, the experts, trained to instruct the peasant. It is hard to imagine this knowledge being created and maintained except in the context of lifelong observation and a relatively stable, multigenerational community that routinely exchanges and preserves knowledge of this kind. [Chapter 9]

Is thinking about monetization a waste of our best minds?
















Consider a new hashtag: diversity.


Why am I skeptical?









SYDELL: Gaunt says she’s made new friends through Twitter.





Ideas behind their time: formal causal inference?

Alex Tabarrok at Marginal Revolution blogs about how some ideas seem notably behind their time:


In comparing ideas behind and ahead of their times, it’s worth considering the processes that identify them as such.

In the case of ideas ahead of their time, we rely on records and other evidence of their genesis (e.g., accounts of the use of flamethrowers at sea by the Byzantines ). Later users and re-discoverers of these ideas are then in a position to marvel at their early genesis. In trying to see whether some idea qualifies as ahead of its time, this early genesis, lack or use or underuse, followed by extensive use and development together serve as evidence for “ahead of its time” status.

On the other hand, in identifying ideas behind their time, it seems that we need different sorts of evidence. Taborrok uses the standard of whether their fruits could have been produced a long time earlier (“A lot of the papers in say experimental social psychology published today could have been written a thousand years ago so psychology is behind its time”). We need evidence that people in a previous time had all the intellectual resources to generate and see the use of the idea. Perhaps this makes identifying ideas behind their time harder or more contentious.


Perhaps formal causal inference — and some kind of corresponding new notation, such as Pearl’s do(x) operator or potential outcomes — is an idea behind its time.1 Judea Pearl’s account of the history of structural equation modeling seems to suggest just this: exactly what the early developers of path models (Wright, Haavelmo, Simon) needed was new notation that would have allowed them to distinguish what they were doing (making causal claims with their models) from what others were already doing (making statistical claims).2

In fact, in his recent talk at Stanford, Pearl suggested just this — that if the, say, the equality operator = had been replaced with some kind of assignment operator (say, :=), formal causal inference might have developed much earlier. We might be a lot further along in social science and applied evaluation of interventions if this had happened.

This example raises some questions about the criterion for ideas behind their time that “people in a previous time had all the intellectual resources to generate and see the use of the idea” (above). Pearl is a computer scientist by training and credits this background with his approach to causality as a problem of getting the formal language right — or moving between multiple formal languages. So we may owe this recent development to comfort with creating and evaluating the qualities of formal languages for practical purposes — a comfort found among computer scientists. Of course, e.g., philosophers and logicians also have been long comfortable with generating new formalisms. I think of Frege here.

So I’m not sure whether formal causal inference is an idea behind its time (or, if so, how far behind). But I’m glad we have it now.

  1. There is a “lively” debate about the relative value of these formalisms. For many of the dense causal models applicable to the social sciences (everything is potentially a confounder), potential outcomes seem like a good fit. But they can become awkward as the causal models get complex, with many exclusion restrictions (i.e. missing edges). []
  2. See chapter 5 of Pearl, J. (2009). Causality: Models, Reasoning and Inference. 2nd Ed. Cambridge University Press. []






