Complex Adaptive Systems: Using a Free-Market Simulation to Estimate Attribute Relevance
The authors have implemented a complex adaptive simulation of an agent-based exchange to estimate the relative importance of attributes in a data set. This simulation uses an individual, transaction-based voting mechanism to help the system estimate the importance of each variable at the system/aggregate level. Two variations of information gain – one using entropy and one using similarity – were used to demonstrate that the resulting estimates can be computed using a smaller subset of the data and greater accommodation for missing and erroneous data than traditional methods.
KeywordsInformation Gain Complex Adaptive System Vote Mechanism Gain Method Purchasing Strategy
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