THE COMPLEXITY OF INFORMATION EXTRACTION

In order to determine the difficulty of decision problems based on natural data, decision problems are characterized by four measures defined on a Boolean function f of N variables: the implementation cost C(f), the randomness R(f), the deterministic entropy H(f), and the complexity K(f). It is shown that: 1) C(f) approximately equals R(f) approximately equals H(f) approximately equals K(f), all measured in bits; 2) decision problems based on natural data are partially random (in the Kolmogorov sense) and have low entropy with respect to their dimensionality, and the relations between the four measures translate to lower and upper bounds on the cost of solving these problems; and 3) allowing small errors in the implementation of f saves a lot in the low-entropy case but saves nothing in the high-entropy case. If f is partially structured, the implementation cost is reduced substantially.