Direct data-based decision making under uncertainty

Direct Data-Driven Methods for Decision Making under Uncertainty

where the future cost f depends both on the decision u ∈ U as well as the outcome of uncertain events, represented by a random variable X ∈ X . Here the random variable X follows a distribution P which is assumed to be known in order to form the expectation in problem (1). Examples includes making an inventory decision with uncertainty future demand, purchasing stocks with uncertain information...

Decision making under uncertainty

Decision making under uncertainty

1 Laboratory of Computational Neuroscience, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 2 Laboratoire de Recherché en Neuroimagerie, Le Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland 3 Information Processing and Economic Decision Making, Department of Psychology, Universität Konstanz, Konstanz, Germany 4 Economic Psychology, Department of Psychology, Univers...

Decision Making under Extreme Uncertainty

Beliefs about an unknown distribution can be defined as a measure on the space of probability density functions. This paper (1) characterizes the set of beliefs that satisfy invariance postulates (2) establishes that updating invariant beliefs leads to a posterior distribution with tail asymptotically approaching 1 x(lnx)n . Note to seminar participant at Wharton: Part I of this paper is primar...

Decision Making under Interval Uncertainty

To make a decision, we must find out the user’s preference, and help the user select an alternative which is the best – according to these preferences. Traditional decision theory is based on a simplifying assumption that for each two alternatives, a user can always meaningfully decide which of them is preferable. In reality, often, when the alternatives are close, the user is often unable to s...