Info-gap decision theory seeks to optimize robustness to failure under severe uncertainty, in particular applying sensitivity analysis of the stability radius type to perturbations in the value of a given estimate of the parameter of interest. It has some connections with Wald's maximin model; some authors distinguish them, others consider them instances of the same principle.
It has been developed by Yakov Ben-Haim, and has found many applications and described as a theory for decision-making under "severe uncertainty". It has been criticized as unsuited for this purpose, and alternatives proposed, including such classical approaches as robust optimization.
Info-gap is a theory: it assists in decisions under uncertainty. It does this by using models, each built on the last. One begins with a model for the situation, where some parameter or parameters are unknown.
Then takes an estimate for the parameter, and one analyzes how sensitive the outcomes under the model are to the error in this estimate.
Uncertainty model Starting from the estimate, an uncertainty model measures how far away other values of the parameter are: as uncertainty increases, the set of values increase.
Robustness/opportuneness model Given an uncertainty model, then for each decision, how uncertain can you be and be confident succeeding? (robustness) Also, given a windfall, how uncertain must you be for this result to be plausible? (opportuneness)
Decision-making model One optimizes the robustness on the basis of the model. Given an outcome, which decision can stand the most uncertainty and give the outcome? Also, given a windfall, which decision requires the least uncertainty for the outcome?
Info-gap theory models uncertainty as subsets around a point estimate : the estimate is accurate, and uncertainty increases, in general without bound.
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
In decision theory and game theory, Wald's maximin model is a non-probabilistic decision-making model according to which decisions are ranked on the basis of their worst-case outcomes – the optimal decision is one with the least bad worst outcome. It is one of the most important models in robust decision making in general and robust optimization in particular. It is also known by a variety of other titles, such as Wald's maximin rule, Wald's maximin principle, Wald's maximin paradigm, and Wald's maximin criterion.
La théorie du regret ou de l'aversion au regret ou du regret anticipé est un modèle de théorie économique développé simultanément en 1982 par Graham Loomes et Robert Sugden, David E. Bell, et Peter C. Fishburn. Elle permet de développer des modèles de choix dans un contexte d'incertitude qui tiennent compte des effets anticipés du regret. Cette théorie a par la suite été développée par d'autres auteurs. Elle incorpore un terme regret dans la fonction d'utilité qui dépend négativement du produit obtenu et positivement du meilleur produit alternatif l'incertitude étant donnée.
L'algorithme minimax (aussi appelé algorithme MinMax) est un algorithme qui s'applique à la théorie des jeux pour les jeux à deux joueurs à somme nulle (et à information complète) consistant à minimiser la perte maximum (c'est-à-dire dans le pire des cas). Pour une vaste famille de jeux, le théorème du minimax de von Neumann assure l'existence d'un tel algorithme, même si dans la pratique il n'est souvent guère aisé de le trouver.
This course is an introduction to linear and discrete optimization.Warning: This is a mathematics course! While much of the course will be algorithmic in nature, you will still need to be able to p
Couvre l'estimation maximale de la probabilité, en mettant l'accent sur l'estimation-distribution ML, l'estimation de la réduction et les fonctions de perte.
Introduit les principes fondamentaux de l'apprentissage statistique, couvrant l'apprentissage supervisé, la théorie de la décision, la minimisation des risques et l'ajustement excessif.
The presence of competing events, such as death, makes it challenging to define causal effects on recurrent outcomes. In this thesis, I formalize causal inference for recurrent events, with and without competing events. I define several causal estimands an ...
EPFL2023
We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...
It is natural for humans to judge the outcome of a decision under uncertainty as a percentage of an ex-post optimal performance. We propose a robust decision-making framework based on a relative performance index. It is shown that if the decision maker's p ...