The standard mode of theorizing assumed in economics deductive--it assumes that human agents derive their conclusions by logical processes from complete, consistent and well-defined premises in a given problem. This works well in simple problems, but it breaks down beyond a "problem complexity boundary" where human computational abilities are exceeded or the assumptions of deductive rationality cannot be relied upon to hold. The paper draws upon what is known in psychology to argue that beyond this problem complexity boundary humans continue to reason well, but by using induction rather than deduction. That is, difficult or complex decision problems, humans transfer experience from other, similar problems they have faced before; they look for patterns and analogies that help them construct internal models of and hypotheses about the situation they are in; and they act more or less deductively on the basis of these. In doing so they constantly update these models and hypotheses by importing feedback--new observations--from their environment. Thus, in dealing with problems of high complexity humans live in a world of learning and adaptation. I illustrate these ideas by showing that the processes of pattern recognition, hypothesis formation and refutation over time are perfectly amenable to analysis; and by using them to explain supposedly "anomalous" behavior in financial markets
