Data mining with agent gaming

Authors:
Fidan Boylu;Haldun Aytug;Gary J. Koehler
Affiliations:
Operations and Information Management, School of Business, University of Connecticut, Storrs, USA;Information Systems and Operations Management Department, 351 Stuzin, The Warrington College of Business Administration, University of Florida, Gainesville, USA 32611;Information Systems and Operations Management Department, 351 Stuzin, The Warrington College of Business Administration, University of Florida, Gainesville, USA 32611
Venue:
Information Technology and Management
Year:
2010

Citing 5
Cited 0

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Abstract

A new type of data mining considers the case where the instances over which induction takes place are intelligent agents who might act strategically to thwart the learner. Instances comprised of humans, companies, or governments all have this capability. One paper calls this adversarial learning and proposes an iterated learning process--much like reinforcement learning--to determine a classifier. The current authors proposed a different approach that uses rational expectation ideas to alter the learner's problem to directly anticipate possible strategic gaming by agents during the induction process. This paper explores differences between solutions produced by these two approaches on a credit dataset and draws some general insights.