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Minimax Regret Decision Criterion
"... Utility elicitation is a critical function of any automated decision aid, allowing decisions to be tailored to the preferences of a specific user. However, the size and complexity of utility functions often precludes full elicitation, requiring that decisions be made without full utility information ..."
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information. Adopting the minimax regret criterion for decision making with incomplete utility information, we describe and empirically compare several new procedures for incremental elicitation of utility functions that attempt to reduce minimax regret with as few questions as possible. Specifically, using
Minimax regret and strategic uncertainty
, 2008
"... This paper introduces a new solution concept, a minimax regret equilibrium, which allows for the possibility that players are uncertain about the rationality and conjectures of their opponents. We provide several applications of our concept. In particular, we consider pricesetting environments and s ..."
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Cited by 13 (1 self)
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This paper introduces a new solution concept, a minimax regret equilibrium, which allows for the possibility that players are uncertain about the rationality and conjectures of their opponents. We provide several applications of our concept. In particular, we consider pricesetting environments
on Minimax Regret and Efficient Bargaining
, 1998
"... Bargaining under uncertainty is modeled by the assumption that there are several possible states of nature, each of which is identified with a bargaining problem. We characterize bargaining solutions which generate ex ante efficient combinations of outcomes under the assumption that the bargainers ..."
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have minimax regret preferences. For the case of two bargainers a class of monotone utopiapath solutions is characterized by the efficiency criterion, but for more than two bargainers only dictatorial solutions are efficient. By incorporating scale covariance into the minimax regret preferences a
MINIMAX REGRET ESTIMATION IN LINEAR MODELS
"... We develop a new linear estimator for estimating an unknown vector x in a linear model, in the presence of bounded data uncertainties. The estimator is designed to minimize the worstcase regret across all bounded data vectors, namely the worstcase difference between the MSE attainable using a line ..."
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linear estimator that does not know the true parameters x, and the optimal MSE attained using a linear estimator that knows x. We demonstrate through several examples that the minimax regret estimator can significantly increase the performance over the conventional leastsquares estimator, as well
Asymptotic Minimax Regret for Data Compression, Gambling, and Prediction
"... Abstract—For problems of data compression, gambling, and prediction of individual sequences I the following questions arise. Given a target family of probability mass functions @ I A, how do we choose a probability mass function @ I A so that it approximately minimizes the maximum regret /belowdispl ..."
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/belowdisplayskip10ptminus6pt @�� � I @ I A �� � I @ I ” AA and so that it achieves the best constant in the asymptotics of the minimax regret, which is of the form @ PA ��� @ P AC C
Minimax regret based elicitation of generalized additive utilities
, 2007
"... We describe the semantic foundations for elicitation of generalized additively independent (GAI) utilities using the minimax regret criterion, and propose several new query types and strategies for this purpose. Computational feasibility is obtained by exploiting the local GAI structure in the model ..."
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Cited by 8 (1 self)
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We describe the semantic foundations for elicitation of generalized additively independent (GAI) utilities using the minimax regret criterion, and propose several new query types and strategies for this purpose. Computational feasibility is obtained by exploiting the local GAI structure
Minimax Regret Classifier for Imprecise Class Distributions
"... The design of a minimum risk classifier based on data usually stems from the stationarity assumption that the conditions during training and test are the same: the misclassification costs assumed during training must be in agreement with real costs, and the same statistical process must have generat ..."
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(conventional minimax) have been proposed previously in the literature, but they may achieve a robust classification at the expense of a severe performance degradation. In this paper we propose a minimax regret (minimax deviation) approach, that seeks to minimize the maximum deviation from the performance
Incremental Utility Elicitation with the Minimax Regret Decision Criterion
 In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence
, 2003
"... Utility elicitation is a critical function of any automated decision aid, allowing decisions to be tailored to the preferences of a specific user. However, the size and complexity of utility functions often precludes full elicitation, requiring that decisions be made without full utility inform ..."
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Cited by 36 (14 self)
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Utility elicitation is a critical function of any automated decision aid, allowing decisions to be tailored to the preferences of a specific user. However, the size and complexity of utility functions often precludes full elicitation, requiring that decisions be made without full utility information.
Achievability of Asymptotic Minimax Regret in Online and Batch Prediction
"... The normalized maximum likelihood model achieves the minimax coding (logloss) regret for data of fixed sample size n. However, it is a batch strategy, i.e., it requires that n be known in advance. Furthermore, it is computationally infeasible for most statistical models, and several computationally ..."
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The normalized maximum likelihood model achieves the minimax coding (logloss) regret for data of fixed sample size n. However, it is a batch strategy, i.e., it requires that n be known in advance. Furthermore, it is computationally infeasible for most statistical models, and several
Preliminary Test Estimation in the Pareto Distribution Using Minimax Regret Significance Levels
"... We consider preliminary test estimator based on the maximum likelihood estimator of the parameter of the pareto distribution. The optimal significance levels for the preliminary test are obtained using the minimax regret criterion. The corresponding critical values of the preliminary test are calcu ..."
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We consider preliminary test estimator based on the maximum likelihood estimator of the parameter of the pareto distribution. The optimal significance levels for the preliminary test are obtained using the minimax regret criterion. The corresponding critical values of the preliminary test
Results 1  10
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