explanation for how complex thoughts in the human mind emerge from neuronal chatter.
A similar optimal balance between order and chaos exists in the scientific enterprise. There are cases, particularly in the softer sciences, where a prominent professor with a large ego—and a history of drawing in a large amount of grant money based on his well-established ideas—will do all he can to maintain these theories, including engaging in practices that are essentially unscientific and dogmatic. He may bend the rules to publish papers confirming his results, ignore experiments his lab carries out that contradict them, insist that his lab tow the party line, that those working under him always believe in his theory absolutely, and so on. He and his scientific progeny, his PhD students and postdoctoral assistants, may well be maintaining this viewpoint in the face of increasing evidence opposing it. For a while, due to his influence and personality, his theory may continue to flourish, but eventually it will be superseded, and his research staff will find it increasingly difficult to grab decent academic posts because of their long-standing defense of a scientific position shown to be wrong.
In a separate category are scientists who constantly generate outlandish ideas but are not particularly interested in testing them with carefully controlled experiments. Admittedly these rarely get past the PhD stage, but if they do, their careers always seem hampered by their overactive creativity.
The best scientists not only have the most respectable careers but also leave a lasting legacy of work, along with a new set of high flyers, who were former students. These renowned scientists are skilled at establishing successful theories and empirical results. But they are also quick to ditch these theories when the evidence racks up against them. They then generate new ways of perceiving the field—always with a qualified creativity.
The ability to settle on this healthy balance between stability and chaos is probably too much to ask of pre-life creatures, except for the most advanced—those on the cusp of life—because they would lack the complexity to support it. Specifically, for effective, flexible information processing skills related to survival and replication, you first need a means of storing many solid preexisting beliefs, which DNA, as I will discuss in the next section, is supreme at doing. You then need techniques for testing new hypotheses about the environment. In life, the main method for this involves creating a host of successful offspring subtly different from yourself, with a small proportion of those differences potentially being an improvement, reflecting useful novel innovations.
Let me illustrate the relationship between complexity and adaptability with another schematic example. Imagine you have 5 different words (analogous to different kinds of atoms within a proto-life object) by which to make up a sentence 5 words in length (analogous to a replicating chemical creature made up of 5 atoms). In each case, the sentence of 5 words gives you very little information. However, there are 3,125 possible different sentences you can make. This is a reasonable number, but in the face of an incredibly dynamic world, it is still potentially very limiting. Now imagine you still have 5 different words, but you can make up a sentence 100 words long (like a replicating chemical with 100 atoms in it). Each 100-word sentence potentially carries 20 times more information than was represented by the simpler creature with sentences of 5 words. A far more striking feature, though, is that, instead of 3,125 possible different sentences, there are now 8 × 10 69 ! Therefore, if the capacity to represent a greater variety of ideas is beneficial, the chemical object needs to be larger and more complex. Some chemical designs of equivalent size will be better than others at storing information and getting the balance of stability and