Complex adaptive systems: using a free-market simulation to estimate attribute relevance

Authors:
Christopher N. Eichelberger;Mirsad Hadžikadić
Affiliations:
College of Information Technology, The University of North Carolina at Charlotte, Charlotte, NC;College of Information Technology, The University of North Carolina at Charlotte, Charlotte, NC
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

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Abstract

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.