https://popups.uliege.be/3041-539x/index.php?id=1895 Index terms fr 0 https://popups.uliege.be/3041-539x/index.php?id=1883 This article is a small survey and pioneering as a starting point for a longer research project : to utilize generalized semi-infinite optimization for purposes of prediction. Firstly, it reflects tbe analytical and inverse (intrinsic) behaviour of generalized semi-infinite optimization problems P(f,h,g,u,v) and presents interpretations of them from the viewpoint of anticipatory systems. These differentiable problems admit an infinite set Y(x) of inequality constraints y, which depends on the state x. Under suitable assumptions, we present global stability properties of the feasible set and corresponding structural stability properties of the entire optimization problem (Weber, 2002 ; Weber, 2003). The achieved results are a basis of algorithm design. In the course of explanation, the perturbational approach gives rise to reconstructions. By studying three applications of generalized semi-infinite optimization, secondly, we interpret these aspects of inverse problems in the sense of prediction. The three anticipatory systems are : (i) Reverse Chebycchev approximation, where we describe a given system by a neighbouring easier one as long as possible under some error tolerance. We begin by a motivating problem from chemical engineering and turn then to time-dependent systems. (ii) Time-minimal or -maximal optimization problems, where we want to pull or push the time-horizon of some process to present time or into the future. We mention global warming and turn to further kinds of biosystems. (iii) Computational biology, where we are concerned with prediction and stability of DNA microarray gene-expression patterns. Wed, 17 Jul 2024 12:56:01 +0200 Thu, 10 Oct 2024 16:49:39 +0200 https://popups.uliege.be/3041-539x/index.php?id=1883 https://popups.uliege.be/3041-539x/index.php?id=3254 This paper addresses the influence of information feedback on a decision process supported by a simulation model. A group of 118 graduate students participated in the experiment under four conditions : a1) decision making with application of the simulation model with pretest, a2) decision making with application of the simulation model and group information feedback with pretest, a3) decision making with application of the simulation model without pretest, and a4) decision making with application of the simulation model and group information feedback without pretest. The criteria function and number of simulation runs were observed. The hypothesis that decision-making using a simulation model and group feedback improve criteria function was confirmed. The model of learning during the decision process was developed. Fri, 13 Sep 2024 13:26:30 +0200 Thu, 10 Oct 2024 10:39:49 +0200 https://popups.uliege.be/3041-539x/index.php?id=3254