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. Adopting the minimax regret criterion for decision making with incomplete utility information, ...

In a finite partial-monitoring game against Nature, the Learner repeatedly chooses one of finitely many actions, the Nature responds with one of finitely many outcomes, the Learner suffers a loss and receives feedback signal, both of which are fixed functions of the action and the outcome. The goal of the Learner is to minimize its total cumulative loss. We make progress towards classification ...

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. Adopting the minimax regret criterion for decision making with incomplete utility information, ...

We consider the problem of testing many statistical hypotheses with incomplete information on a priori distribution of hypotheses. A minimax deviation (regret) of the average risk from the Bayes risk for a known a priori distribution serves as the optimality criterion. In contrast to traditional minimax method (with respect to a conditional risk) our approach makes it possible to control the ex...

We consider on-line density estimation with a parameterized density from an exponential family. In each trial t the learner predicts a parameter t. Then it receives an instance xt chosen by the adversary and incurs loss ln p(xtj t) which is the negative log-likelihood of xt w.r.t. the predicted density of the learner. The performance of the learner is measured by the regret de ned as the total ...

Researches in Bayesian sensitivity analysis and robustness have mainly dealt with the computation of the range of some quantities of interest when the prior distribution varies in some class. Recently, researchers’ attention turned to the loss function, mostly to the changes in posterior expected loss and optimal actions. In particular, the search for optimal actions under classes of priors and...

In this study, an interval-based regret-analysis (IBRA) model is developed for supporting long-term planning of municipal solid waste (MSW) management activities in the City of Changchun, the capital of Jilin Province, China. The developed IBRA model incorporates approaches of interval-parameter programming (IPP) and minimax-regret (MMR) analysis within an integer programming framework, such th...

We study a general class of online learning problems where the feedback is specified by a graph. This class includes online prediction with expert advice and the multiarmed bandit problem, but also several learning problems where the online player does not necessarily observe his own loss. We analyze how the structure of the feedback graph controls the inherent difficulty of the induced T -roun...

We introduce a simple procedure to be used for selecting the strategies most likely to be played by inexperienced agents who interact in one shot 2x2 games. We start with an axiomatic description of a function that may capture players’ beliefs. Various proposals connected with the concept of mixed strategy Nash equilibrium do not match this description. On the other hand minimax regret obeys al...

The Choquet integral is one of the most sophisticated and expressive preference models used in decision theory for multicriteria decision making. It performs a weighted aggregation of criterion values using a capacity function assigning a weight to any coalition of criteria, thus enabling positive and/or negative interactions among criteria and covering an important range of possible decision b...