By Yakov Ben-Haim
Ben-Haim (mechanical engineering, Technion-Israel Institute of expertise) discusses using quantitative versions and computational equipment used for the formula of selection algorithms and function overview in occasions the place the decision-making procedure is open-ended and characterised through a critical lack of expertise. Rejecting the present types of chance idea, he argues new thought (termed ''info-gap'' theory), which arose within the technological sciences, bargains a technique that takes under consideration the potentials of significant luck and nice failure. the elemental selection features of the speculation are the robustness functionality, which assesses the immunity to failure, and the chance functionality, which assesses the immunity to providence. those ideas have interaction with services of price judgments, playing and risk-taking, price of data, assimilation of information, and coherent uncertainties to shape the fundamentals of info-gap concept.
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Extra info for Information Gap Decision Theory: Decisions under Severe Uncertainty
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The robustness a{q) of decision vector q is the greatest value of the horizon of uncertainty a for which specified minimal requirements are always satisfied. a{q) expresses robustness — the degree of resistance to uncertainty and immunity against failure — so a large value of a{q) is desirable. Eq. 2) states that the opportuneness f3{q) is the least level of uncertainty a which must be tolerated in order to enable the possibility of sweeping success as a result of decisions q. (5{q) is the immunity against windfall reward, so a small value of (3{q) is desirable.
17) J where r^ represents the vector of windfall rewards rw,2. 6. 4 by noting that the immunity functions are obtained by combining three distinct elements: a system model, a performance requirement, and an uncertainty model. An info-gap analysis begins with these three components. System model. The reward function R{q,u) (or the decision algorithm D{q, u)) expresses or entails the input/output structure of the system to which the decision is applied. R{q, u) assesses the response of the system, in terms of reward or quality of performance, to the decision maker's choices q and to the ambient uncertainty u.
5 shows the robustness versus time for three values of the damping ratio C, at fixed natural frequency. 03, shows only slightly better robustness than the lowest curve in fig. 01; (J = 1 in both cases. However, the middle and upper curves in fig. 5 are quite different from the behavior displayed in fig. 4. The trend in fig. 5 resulting from increasing the damping is marked by predominantly low a until the end of the nominal input at t = T = 5, and then rapid rise in a which stabilizes at a fixed value.