Searching NK fitness landscapes: On the trade off between speed and quality in complex problem solving
The complexity of problems is often too high for people or organizations, having to solve them, to do so in an optimal way. In order to cope with such problems, either the search space has to be decomposed, or it has to be searched by random trial and error processes. Kauffman´s NK model offers a way to depict such problem space decompositions and the search for solutions in them. However, papers on the effect of different decompositions on solution quality come to differing conclusions as to the advantages or disadvantages of incorrect modularization assumptions. The current paper thus examines the results of more empirically based search strategies. Some trade offs become visible, but the sometimes observed initial advantage of a too deep modularization could not be confirmed.
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