Posted on

“Failure” is an option

This is about the hard choices that graduate students must make.

The cultural definition of success in academic research is to:

  1. Produce good research which many other people appreciate.
  2. Produce many students who go on to do the same.

There are fundamental reasons why this is success in the local culture. Good research appreciated by others means access to jobs. Many students succesful in the same way implies that there are a number of people who think in a similar way and appreciate your work.

In order to graduate, a phd student must live in an academic culture for a period of several years. It is common to adopt the culture’s definition of success during this time. It’s also common for many phd students discover they are not suited to an academic research lifestyle. This collision of values and abilities naturally results in depression.

The most fundamental advice when this happens is: change something. Pick a new advisor. Pick a new research topic. Or leave the program (and do something else with your life).

The first two are relatively easy, but “Do something else with your life” is a hard choice for a phd student to make because they are immersed in and adopt a value system that does not value that choice. Remember here that the academic value system is not a universal value system. For example, many people want to do something that is immediately constructive and find this at odds with academic research (which is almost defined by “not immediate”). The world is big enough and diverse enough to support multiple value systems. Realizing this may be the key to making very good decisions in your life. A number of my friends made this decision and went to google or investment banking places where they are deliriously happier (and more productive) than in their former lives.

Posted on

Online Learning as the Mathematics of Accountability

Accountability is a social problem. When someone screws up, do you fire them? Or do you accept the error and let them continue? This is a very difficult problem and we all know of stories where the wrong decision was made.

Online learning (as meant here), is a subfield of learning theory which analyzes the online learning model.

In the online learning model, there are a set of hypotheses or “experts”. On any instantance x, each expert makes a prediction y. A master algorithm A uses these predictions to form it’s own prediction yA and then learns the correct prediction y*. This process repeats.

The goal of online learning is to find a master algorithm A which uses the advice of the experts to make good predictions. In particular, we typically want to guarantee that the master algorithm performs almost as well as the best expert. If L(e) is the loss of expert e and L(A) is the loss of the master algorithm, it is often possible to prove:

L(A) less than mine L(e) + log(number of experts)

over all sequences.

In particular, there is no assumption of independent samples and there is no assumption that the experts perform well (or can perform well). This is not a high probability statement: it simply always holds. These assumption-free qualities are very important for application to the accountability problem, because the experts really can be adversarial.

In any situation where we have a set of human experts giving advice on the same subject, we can hope to apply online learning algorithms to better distill collective advice into single prediction. Examples include:

  1. stock picking Many of the big stocks have ‘analysts’ who predict whether they will go up or down. For an example, look here.
  2. surveys It is common to see things like “A gain of 1.4 percent was expected, based on the median estimate in a Bloomberg survey of 53 economists” in news articles. Presumably, these economists are reused frequently implying they have a record to which an online algorithm could be applied.

This application of online learning isn’t trivial. Even for the above examples, it isn’t clear how to handle issues like:

  1. A new expert starts making predictions. There are some reasonable ad-hoc mechanisms for coping with this in the context of particular algorithms.
  2. An expert declines to make a prediction. The modified “sleeping experts” setting handles this, but the results are not quite as tight.
  3. The loss associated with individual predictions is highly variable rather than something simple like “0/1-loss” or “squared error loss”. One approach to this is to combine regret minimizing learning reductions with online learning algorithms (drawback: the reduced predictions may not be of intuitive things). Another approach is simply trying to make very flexible master algorithms (drawback: flexibility often comes with a weakening in the theoretical guarantees).
  4. In the real world, we may not have feedback about a prediction until after the next 10 predictions (or more) need to be made.
  5. In the real world, there may be uncertainty about the measured truth. Quantifying GDP growth requires a lot of work and has some fundamental uncertainty associated with it, especially when immediate feedback is required.
Posted on

Site Update

I tweaked the site in a number of ways today, including:

  1. Updating to WordPress 1.5.
  2. Installing and heavily tweaking the Geekniche theme. Update: I switched back to a tweaked version of the old theme.
  3. Adding the Customizable Post Listings plugin.
  4. Installing the StatTraq plugin.
  5. Updating some of the links. I particularly recommend looking at the computer research policy blog.
  6. Adding threaded comments. This doesn’t thread old comments obviously, but the extra structure may be helpful for new ones.

Overall, I think this is an improvement, and it addresses a few of my earlier problems. If you have any difficulties or anything seems “not quite right”, please speak up. A few other tweaks to the site may happen in the near future.

Posted on

Science in the Government

I found the article on “Political Science” at the New York Times interesting. Essentially the article is about allegations that the US government has been systematically distorting scientific views. With a petition by some 7000+ scientists alleging such behavior this is clearly a significant concern.

One thing not mentioned explicitly in this discussion is that there are fundamental cultural differences between academic research and the rest of the world. In academic research, careful, clear thought is valued. This value is achieved by both formal and informal mechanisms. One example of a formal mechanism is peer review.

In contrast, in the land of politics, the basic value is agreement. It is only with some amount of agreement that a new law can be passed or other actions can be taken. Since Science (with a capitol ‘S’) has accomplished many things, it can be a significant tool in persuading people. This makes it compelling for a politician to use science as a mechanism for pushing agreement on their viewpoint.

Most scientists would not mind if their research is used in a public debate. The difficulty arises when the use of science is not representative of the beliefs of scientists. This can happen in many ways. For example, agreement is uncommon in research which implies that it is almost always possible, by carefully picking and choosing, to find one scientist who supports almost any viewpoint.

Such misrepresentations of scientific beliefs about the world violate the fundamental value of “careful, clear thought”, so they are regarded as fundamentally dangerous to the process of research. Naturally, fundamentally dangerous things are sensitive issues which can easily lead to large petitions.

This combination of mismatched values is what appears to be happening. It is less clear what should be done about it.

One response has been (as the article title suggests) politicization of science and scientists. For example the Union of Concerned Scientists (which organized the petition) has a viewpoint and is pushing it. As another example, anecdotal evidence suggests a strong majority of scientists in the US voted against Bush in the last presidential election.

I would prefer a different approach, which is essentially a separation of responsibilities. Given a sufficient separation of powers, scientists should be the most reliable source for describing and predicting the outcomes of some courses of action and the impact of new technologies. What is done with such information is up to the rest of the world. This style of “sharply defined well-separated powers” has worked fairly well elsewhere. Supreme court judges (who specialize in interpretation of law) are, by design, relatively unaffectable by the rest of politics. A newer example is the federal reserve board who have been relatively unaffected by changes in politics, even though it is easy to imagine their powers could dramatically effect election outcomes. This last example is a matter of custom rather than constitutional law.

Neither of the above examples are perfect—the separation of powers has failed on multiple occasions. Nevertheless, it seems to be a useful ideal.