How important is overcoming bias?

Arnold Kling summarizes Robin’s argument:

If you have a cause, then other people probably disagree with you (if nothing else, they don’t think your cause is as important as you do). When other people disagree with you, they are usually more right than you think they are. So you could be wrong. Before you go and attach yourself to this cause, shouldn’t you try to reduce the chances that you are wrong? Ergo, shouldn’t you work on trying to overcome bias? Therefore, shouldn’t overcoming bias be your number one cause?

Here is Robin’s very similar statement.  I believe these views are tautologically true and they simply boil down to saying that any complaint can be expressed as a concern about error of some kind or another.  I cannot disagree with this view, for if I do, I am accusing Robin of being too biased toward eliminating bias, thus reaffirming that bias is in fact the real problem.

I find it more useful to draw an analogy with statistics.  Biased estimators are one problem but not the only problem.  There is also insufficient data, lazy researchers, inefficient estimators, and so on.  Then I don’t see why we should be justified in holding a strong preference for overcoming bias, relative to other ends.

When I think of a blog that tries to eliminate or reduce bias, say by considering a wide variety of views and methods, I think of Dan Drezner or Matt Yglesias.  I view Robin’s blog as exemplifying bias, and indeed showing that bias can be very useful, especially if embedded in a broader discovery process with checks and balances.  (I would describe Robin’s blog as one of the dozen "must reads" out there.)  Robin’s blog is one very select group of very smart people, pushing one unpopular, specialized, but very interesting and analytically powerful research method as far as it can go.

If I were allowed to retitle Robin’s blog (and I am not), I would call it "Reaping the Fruits of Bias."


I also think that "overcoming laziness" is important.

I have to ask since there are about 30 blogs under your "blogs we like" what are the other 11 "must reads"

I've always thought of Megan McArdle as the blogger most concerned about dealing with her own bias.

An even cleaner analogy with statistics follows from the relation

mean square error of out-of-sample predictions = (bias of predictions)^2 + variance of predictions

Reducing bias is great, but needs to be traded off against the benefit of reducing variance. Another way of saying that is that you want your model to be correct on average (unbiased) but you also want to make sure that your predictions in a given situation don't depend too strongly on the particular, finite, random data set you use to fit your model.

Often this trade off leads one to prefer simpler, somewhat biased models over more complicated, less biased models.

You once asserted that, while economists was a good topic for a blog, philosophy was especially unsuited. I remember mentally agreeing with you.

Overcoming Bias has caused me to reconsider that.

Another approach is to find an analytical market niche that is underutilized for reasons of bias. Mine, for example, is that I'm always on the lookout for the importance of relatives.

robertdfeinman, you might be interested in this from Jared Diamond where he explains how the "hard sciencies" are really "easy sciences" and the "soft sciences" are "difficult sciences". I came to a different conclusion than (I infer) Diamond wanted me to: I respect the "difficult" sciences less because we get less out of them for the same effort. Perhaps if I believed in the labor theory of value I would respect them more, but I am a staunch subjectivist.

say by considering a wide variety of views and methods

That is far more likely to reduce variance, than to reduce bias. If there is something systematically wrong, then a wide variety of views does not help, and can be a great hinderance. Considering a wide variety of views on economics or on traffic management is utterly useless.

Of course, we could only consider some views (those of the 'experts', say). But that means that we've already drawn firm conclusions as to who's biased and who's not. What is that conclusion based on in the first place?

The truth is not the midpoint between the two talking faces.

I agree with Chris Masse.

I think my point and (poorly structured) comment I made on Robins post bears repeating:

I think why it is not a highly held value is that it is very resourse intesive. The effort to overcome bias is great and would consume lot of time and energy. As a wealth society, we have the time to do this. But during the evolution of society we had to remain active to simply survive. Taking the time to think things through could cost you your life.

Perhaps that is why depression is tied to rational thought. The prospect that there is no god or that life is meaningless isn't of itself good or bad, yet this often leads to suicidal thoughts (the idea being that being to contemplative and not social or productive enough could burden a social group).

In my IOE courses in undergrad, when evaluating projects, due to the time value of money, it is often optimal to have a good solution fast than to take the time to calculate the best solution.

Also, even knowing the truth, reality isn't very certain. Our biases push us to pursue diverse courses of action which improves the likelyhood that some of the courses of action will turn out to be net beneficial.

Aaron, you are suspended from posting in this comment thread (I wish I had that power).

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