Robust screening under ambiguity
Web WebRobust Screening under Distribution Ambiguity Mustafa C˘. P nar & Can K z lkale Department of Industrial Engineering Bilkent University 06800 Bilkent, Ankara, Turkey. July …
Robust screening under ambiguity
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WebAug 19, 2016 · Request PDF Robust screening under ambiguity We consider the problem of screening where a seller puts up for sale an indivisible good, and a buyer with a …
WebJan 3, 2024 · Download PDF Abstract: We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs … WebWe study stochastic optimization problems with chance and risk constraints, where in the latter, risk is quantified in terms of the conditional value-at-risk (CVaR). We consider the distributionally robust versions of these problems, where the constraints are required to hold for a family of distributions constructed from the observed realizations of the uncertainty …
We consider the problem of screening where a seller puts up for sale an indivisible good, and a buyer with a valuation unknown to the seller wishes to acquire the good. We assume that the buyer valuations are represented as discrete types drawn from some distribution, which is also unknown to the seller. The seller is averse to possible mis-specification of … WebFeb 9, 2024 · We investigate the robust reinsurance demand and price under learning and ambiguity aversion. In the reinsurance contract, the insurer is ambiguity neutral and believes that he is perfectly informed, and the reinsurer is a Bayesian learner and is aware that even the filtered model is the best description of the data-generating process, might not …
WebAug 8, 2024 · Abstract. This paper studies robust Bayesian persuasion of a privately informed receiver in a binary environment, where an ambiguity-averse sender with a …
Webproblems with ambiguity sets, it is naturally to find optimal decisions that work well under uncertainties. This gives rise to distributionally robust optimization like (1.1). We refer to [6, 21, 40, 50, 52, 54] for recent work on distributionally robust opti-mization. For the min-max robust optimization (1.2), we refer to [10, 45, 49]. The ship red jacketWebMay 1, 2024 · The seller is averse to possible mis-specification of types distribution, and considers the unknown type density as member of an ambiguity set and seeks an optimal … questions to ask in small talkWebNov 10, 2009 · We concur, and propose that rational choice under ambiguity aims at robustness rather than avoidance of ambiguity. A central argument explains why robust … ship redmarshallWebMar 5, 2014 · 2. Sensitivity analysis means that your results are not highly determined by your model specification (i.e. you could add an additional control variable, or a slightly … ship redmarshall menuWebThe seller is averse to possible mis-specification of types distribution, and considers the unknown type density as member of an ambiguity set and seeks an optimal pricing … ship reduction gearWebNov 10, 2009 · We concur, and propose that rational choice under ambiguity aims at robustness rather than avoidance of ambiguity. A central argument explains why robust choice is intrinsically context-dependent and legitimately violates standard choice consistency conditions. If choice consistency is forced, however, ambiguity-aversion … questions to ask internal candidateWebJan 26, 2024 · Abstract and Figures Distributionally robust optimization (DRO) is a modeling framework in decision making under uncertainty in which the probability distribution of a random parameter is... questions to ask internal applicants