How to evaluate something you don’t know

A fascinating post by Robin Hanson—We Add Near, Average Far—describes some of the difficulty of presenting an idea like FIML to an Internet audience.

The problem is lots of detail and many bits of evidence make it difficult for people to evaluate the overall worth of a complex idea because people tend to evaluate information of that type by averaging the data rather than adding it up.

Should we just say that FIML will make you and your partner smarter and happier? Maybe we should when discussing it online, though of course, we won’t do that.

In person, we have found people quite receptive, but that is probably due to the same effect—in person we focus on one or two results of FIML practice and we only do that if people show interest.

I think Buddhism probably has a similar problem getting it’s message across through books or film. You really have to go to a temple or spend time with people who understand the Dharma to want to take on Buddhism as a way of thinking or living.

Up close and personal, most of us realize that we live in a very complex world and that our capacities for understanding our conditions cannot be taken for granted. But when it comes to learning how to hone or augment our skills for dealing with speech and symbolic communication, we tend to look for simple answers, or abstract ones, that do not include the kinds of detail we must pay attention to. Broad extrinsic theories that provide a general picture without essential detail—and these are everywhere in psychology, religion, sociology, the humanities—simply cannot do for you what a technique like FIML can because FIML is entirely based on the actual data of your actual life, and there is a great deal of that.

I do understand why it is hard to see this. At the same time, I wonder why it is so obvious in the physical sciences and engineering that we can’t do anything properly if we don’t make sure of our data.

Why should the humanities be different? We simply cannot communicate well or understand ourselves well without good data. FIML provides damn good data.

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