Strategic learning.
详细信息
文摘
It is reasonable to anticipate that rational agents who are subject to classification by a principal using a discriminant function might attempt to alter their true attribute values so as to achieve a positive classification. In this study, we explore this potential strategic gaming and develop inference methods for the principal to determine discriminant functions in the presence of strategic behavior by agents and show that this strategic behavior results in an alteration of the usual learning rule.;Although induction methods differ from each other in various aspects, there is one essential issue that is common to all: the assumption that there is no strategic behavior inherent in the sample data generation process. In that respect, the main purpose of this study is to research the question, "What if the observed attributes will be deliberately modified by the acts of some self-interested agents who will gain a preferred classification by engaging in such behavior."? Hence, we investigate the need for anticipating this kind of strategic behavior and incorporate it into the learning process. Since classical learning approaches do not consider the existence of such behavior, we aim to contribute by using rational expectations theory to determine optimal classifiers to correctly classify instances when such instances are strategic decision making agents. We carry out our analysis for a powerful induction method known as support vector machines.;First, we define the framework of Strategic Learning. For separable data sets, we characterize an optimal strategy for the principal that fully anticipates agent behavior in a setting where agents have fixed reservation costs. For non-separable data sets, we provide an MIP formulation and apply it to a credit-risk evaluation setting. Then, we modify our framework by considering a setting where agent costs and reservations are both unknown by the principal. Later, we develop a Genetic Algorithm for Strategic Learning to solve larger versions of the problem. Finally, we investigate the situation where there is a need to enforce constraints on agent behavior in the context of Strategic Learning and thus we extend the concept of Strategic Learning to constrained agents.