Analysts in many domains must choose a design, a strategy, or an intervention without being able to test all relevant alternatives. We consider a situation in which one of two alternatives must be chosen, while only one alternative can be tested prior to decision. A well known probabilistic algorithm assures probability greater than 1/2 of choosing the better system based on a single test, even in the absence of prior knowledge of the probability distribution of the systems' attributes. If this distribution is known then the algorithm can be tuned to achieve probability of success substantially exceeding 1/2. If the distribution is poorly known, then info-gap theory can robustify the algorithm. Using the info-gap robustness function we show that robust-satisficing algorithms may differ from the nominally optimal algorithm when the attribute distribution is uncertain.
The paper is available in the following formats:
Plenary talk: file
Poster: file
Prof. Yakov Ben-Haim
Yitzhak Moda'i Chair in Technology and Economics
Faculty of Mechanical Engineering
Technion - Israel Institute of Technology
Haifa 32000 Israel
| Yakov Ben-Haim | yakov@technion.ac.il |
Send any remarks to isipta11@uibk.ac.at.