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Adjustable robust optimization (ARO) is a technique to solve dynamic (multistage) optimization problems. In ARO, the decision in each stage is a function of the information accumulated from the previous periods on the values of the uncertain parameters. This information, however, is often...
Persistent link: https://www.econbiz.de/10014150072
The problem of choosing an optimal toolkit day after day, when the distribution of values of different toolkits is uncertain and can only be observed by carrying different toolkits, is a multi-armed bandit problem with non-independent arms. Accordingly, except for very simple specifications,...
Persistent link: https://www.econbiz.de/10011864877
This paper continues our study of heuristics employed to choose dynamically tools to put in a toolkit, where the value of any tool can be discovered only by choosing it. This is a multi-armed bandit problem with “arms” that are not independent, hence it is a problem for which the optimal...
Persistent link: https://www.econbiz.de/10011864881
We introduce an accurate, easily implementable, and fast algorithm to compute optimal decisions in discrete-time long-horizon welfaremaximizing problems. The algorithm is useful when interest is only in the decisions up to period T, where T is small. It relies on a flexible parametrization of...
Persistent link: https://www.econbiz.de/10014192999
Current time allocation and household production models face three major weaknesses: First, they only describe the average time allocation. Thus, information about the order of activities is lost. Therefore, it is impossible to describe the influence of activities on later ones. Such...
Persistent link: https://www.econbiz.de/10014480143
Understanding how decisions are made plays an important role in strategic management. Exploration is conducted as to whether the classical approach or the Bayesian approach is more frequently used in the strategic management decision-making literature. A sample of 51 decision-related articles is...
Persistent link: https://www.econbiz.de/10014140967
This paper proposes a procedure for the estimation of discrete Markov decision models and studies its statistical and computational properties. Our Nested Pseudo-Likelihood method (NPL) is similar to Rust's Nested Fixed Point algorithm (NFXP), but the order of the two nested algorithms is...
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