by Anna Salamon and Steve Rayhawk (joint authorship)

Related to: Conjuring An Evolution To Serve You, Disguised Queries 

Let’s say you have a bucket full of “instances” (e.g., genes, hypotheses, students, foods), and you want to choose a good one.  You fish around in the bucket, draw out the first 10 instances you find, and pick the instance that scores highest on some selection criterion.

For example, perhaps your selection criterion is “number of polka dots”, and you reach into the bucket pictured below, and you draw out 10 instances.  What do you get?  Assuming some instances have more polka dots than others, you get hypotheses with an above average number of expected polka dots.  The point I want to dwell on, though -- which is obvious when you think about it, but which sheds significant light on everyday phenomena -- is that you don’t get instances that are just high in polka dots.  You get instances are also high in every trait that correlates with having the most polka dots.

For example, in the bucket above, selecting for instances that have many polka dots implies inadvertently selecting for instances that are red.  Selective processes bring tag-alongs, and the specific tag-alongs that you get (redness, in this case) depend on both the trait you’re selecting for, and the bucket from which you’re selecting.

Nearly all cases of useful selection (e.g., evolution, science) would be unable to produce the cool properties they produce (complex order in organisms, truth in theories) if they didn’t have particular, selection-friendly types of buckets, in addition to good selection criteria.  Zoom in carefully enough, and nearly all of the traits one gets by selection can be considered tag-alongs.  Conversely, if you are consciously selecting entities from buckets with a particular aim in view, you may want to consciously safeguard the “selection-friendliness” of the buckets you are using.