Is complexity valuable or costly? Can we measure the complexity of an object?
Counting the number of unique bits needed to describe an object starts down the right path, but we need to go further. A hot gas "contains", or requires to be described, a large number of unique bits, just as does the DNA of a California condor. The concept of _logical depth_, which measures the amount of computation needed to produce the bit pattern (to discriminate it from other possible bit patterns), comes closer to measuring value. The hot gas is logically shallow because the process that creates it doesn't discriminate between the possible configurations of "bits", and it's trivial to create such another such pattern in that class. The California condor DNA is logically deep because evolution has over several million years has discriminated it from its nearest non-endangered relative and a vast number of failed bird configurations.
Another way to say this is that the condor is _functionally improbable_. Most configurations of genes end up in a gooey mess. Only a vanishingly small fraction of random genes specifying large animals (assuming development gets even that far) specify animals that can fly. Dawkins in _Climbing Mount Improbable_ describes how natural selection produces improbable functionality.
Uniqueness can now be factored back in. A cow by itself is as logically deep as a condor (perhaps a little deeper if we consider intelligent breeding superior to natural selection), but one cow among a billion contains far fewer unique deep bits because its DNA patterns are copied across the billion other cows with much higher probability than the condor's across the handful of other condors.
Logically deep objects tend to be valuable because they tend to be reusable: a part evolved, designed, or computed for one use is more likely to be useful for something else. But there are plenty of exceptions. The last California condor costs much more than the 1 billionth cow to replace, but if want something immediately useful, such as a meal or a jacket, the cow is far more handy.
Thus logical depth in some cases correlates to value, but what it measures objectively is _cost_. To call something valuable just because it is expensive is to fall into a trap similar to Marx's labor theory of value.
In the case of tools, all other things being equal they are more valuable if they are simpler, if they contain _fewer_ bits. Simpler tools are less expensive to design, make, and/or use. A knife requires more information in our brains to use as effectively as a gun, therefore it's less valuable for the user (all other things being equal). A valuable tool is usually both as function and as simple as possible. Computation (evolution, learning, design effort, etc.) is needed both to create a structure deeply adapted to its environment or desired use, and to find the simplest form for that functionality. This happens both in the the process of designing a tool and using it. In both cases funtionality vs. simplicity is a fundamental tradeoff. Objects on which a great deal of evolution/design/computational effort have been spent to obtain functionality and simplicity are logically deep.
The MacIntosh user interface shows that it often pays to move complexity out of the mind of the user and into the mind of the designer -- up to a point. Better still is to drive complexity into the "mind" of the software itself, for example moving the complexity of program language translation and optimization out of the mind of the programmer and into the compiler. A main goal of evolutionary design software is to reduce the complexity that needs to be specified by the human engineer while greatly increasing the amount of functionality (and concommitant complexity) that can be designed into the product.
A simple scientific theory is also more valuable than a complex one that explains the same data -- both because it is more likely to be true, and because it is easier to understand and communicate.
A formal proof of Occam's Razor and the formula 1 - c1^(|p|-|x|+c2) that gives the probability of the regularity/predictive power of a model p to explain data x, can be found in Li & Vitanyi, _An Introduction to Kolmogorov Complexity_, Springer & Verlag 1993, along with computationally formal measures of logical depth, information content, and much else. Recommended for those who like mathemetical challenge, and looking up references in the library when Li & Vitanyi condense entire papers into a single sentence or formula! For an easier overview see my introduction to algorithmic information theory.
Dani Eder has observed that scientific data from new instruments and frontiers tends to be more valauble than old repetitive scientific data. Old data have already been gleaned for their information content. To create new, unique theories we need new, unique information: more bits of precision, different wavelengths, different phenomenon, etc.
Formalizations of information content, design cost, and scientific induction relate intimately to our interest in evolutionary design. We can measure cost, and in some cases value, by determining the logical depth of an object. In evoluntarionary design we might use Kolmogorov complexity theory to trade off the simplicity, error, and computational costs of our simulations, measure the effects of fitness and selection functions, choose design primitives, and to generalize and predict the consequences of these choices. Much promise lies in uniting the new formal theories of general induction with evolutionary search techniques.
