A tweet by Matt Yglesias made reference to a new essay by Aaron Swartz, the inventor, entrepreneur, and left-wing activist, on the concept of “delegations,” a kind of service that involves giving the institution or person you hire a goal and the autonomy and (presumably) the resources to achieve it:

A delegation is like a service, except instead of asking someone to do a specific thing, you ask them to achieve some goal. Hiring someone to paint your wall white would be a service, hiring someone to make your house pretty is a delegation.

As Swartz goes on to argue, it is very different to give appropriate feedback when the goals in mind are ambiguous and take a long time to achieve.

The delegation problem is of particular interest because it captures many of the agency problems we find in the delivery of public services. Swartz’s basic takeaway is that randomized controlled trials and other data collection efforts are of vital importance in this space, a point that Jim Manzi has made on numerous occasions. The following is from an essay he published in City Journal in the summer of last year:

What businesses have figured out is that they can deal with the problem of causal density by scaling up the testing process. Run enough tests, and you can find predictive rules that are sufficiently nuanced to be of practical use in the very complex environment of real-world human decision making. This approach places great emphasis on executing many fast, cheap tests in rapid succession, rather than big, onetime “moon shots.” It’s something like the replacement of craft work by mass production. The crucial step was to lower the cost and time of each test, which doesn’t simply make the process more efficient but, by allowing many more test iterations, leads to faster and more useful learning.
Many of the same techniques that businesses use to lower the cost per test—integration with operational data systems, standardization of test design, and so on—could be applied to social policy experiments. In fact, they were applied in a limited way during the execution of more than 30 randomized experiments during the welfare-reform debate of the 1990s, which was one of the most fruitful sequences of social policy experiments ever done. Businesses have demonstrated that the concept of replication of field experiments can be pushed much further than most social scientists had imagined.

Manzi has been keen to stress the limits of social-scientific tools, despite his enthusiasm for their potential value.

It is tempting to argue that we are at the beginning of an experimental revolution in social science that will ultimately lead to unimaginable discoveries. But we should be skeptical of that argument. The experimental revolution is like a huge wave that has lost power as it has moved through topics of increasing complexity. Physics was entirely transformed. Therapeutic biology had higher causal density, but it could often rely on the assumption of uniform biological response to generalize findings reliably from randomized trials. The even higher causal densities in social sciences make generalization from even properly randomized experiments hazardous. It would likely require the reduction of social science to biology to accomplish a true revolution in our understanding of human society—and that remains, as yet, beyond the grasp of science.

This intuition is part of why Manzi believes that there is great wisdom in the principle of subsidiarity, and I am inclined to agree with him.