Add to ORKGStarting with the seminal paper of Gilboa and Schmeidler (1989) an analogy between the maxmin approach of Decision Theory under Ambiguity and the minimax approach of Robust Statistics -- e.g. Huber and Strassen (1973) -- has been hinted at. The present paper formally clarifies this relation by showing the conditions under which the two approaches are actually equivalent.
This paper argues that the similarities between Ellsberg's and Shackle's frameworks for discussing t...
Decision theory is a cornerstone of Statistics, providing a principled framework in which to act und...
International audienceWe review recent advances in the field of decision making under uncertainty or...
Starting with the seminal paper of Gilboa and Schmeidler (1989) an analogy between the maxmin approa...
When a decision analyst\u27s goal is to establish a partial ordering of alternatives through dominan...
We propose a Bayesian framework for assessing the relative strengths of data-driven ambiguity sets i...
International audienceThis paper provides a model of beliefs representation in which ambiguity and u...
show that ambiguity-averse decision functionals matched with the multiple-prior learning model are m...
Two concepts of optimality corresponding to Bayesian robust analysis are considered: conditional Γ-m...
Decision making formulated as finding a strategy that maximizes a utility function de-pends critical...
This paper provides an in-depth overview of results and concepts in minimax robust hypothesis testin...
Recent advances in decision making have incorporated both risk and ambiguity in decision theory and ...
In this paper, we consider the problem of finding optimal portfolios in cases when the underlying pr...
We study monetary policy under uncertainty. A policy which ameliorates a worst case may differ from ...
This paper argues that the similarities between Ellsberg's and Shackle's frameworks for discussing t...
This paper argues that the similarities between Ellsberg's and Shackle's frameworks for discussing t...
Decision theory is a cornerstone of Statistics, providing a principled framework in which to act und...
International audienceWe review recent advances in the field of decision making under uncertainty or...
Starting with the seminal paper of Gilboa and Schmeidler (1989) an analogy between the maxmin approa...
When a decision analyst\u27s goal is to establish a partial ordering of alternatives through dominan...
We propose a Bayesian framework for assessing the relative strengths of data-driven ambiguity sets i...
International audienceThis paper provides a model of beliefs representation in which ambiguity and u...
show that ambiguity-averse decision functionals matched with the multiple-prior learning model are m...
Two concepts of optimality corresponding to Bayesian robust analysis are considered: conditional Γ-m...
Decision making formulated as finding a strategy that maximizes a utility function de-pends critical...
This paper provides an in-depth overview of results and concepts in minimax robust hypothesis testin...
Recent advances in decision making have incorporated both risk and ambiguity in decision theory and ...
In this paper, we consider the problem of finding optimal portfolios in cases when the underlying pr...
We study monetary policy under uncertainty. A policy which ameliorates a worst case may differ from ...
This paper argues that the similarities between Ellsberg's and Shackle's frameworks for discussing t...
This paper argues that the similarities between Ellsberg's and Shackle's frameworks for discussing t...
Decision theory is a cornerstone of Statistics, providing a principled framework in which to act und...
International audienceWe review recent advances in the field of decision making under uncertainty or...