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This paper presents a uni-processor real-time scheduling algorithm called the Generic Utility Scheduling algorithm (which we will refer to simply as GUS). GUS solves an open real-time scheduling problem -scheduling application activities that have time constraints specified using arbitrarily shaped time/utility functions, and have mutual exclusion resource constraints. A time/utility function is a time constraint specification that describes an activity's utility to the system as a function of that activity's completion time. Given such time and resource constraints, we consider the scheduling objective of maximizing the total utility that is accrued by the completion of all activities. Since this problem is N P-hard, GUS heuristically computes schedules with a polynomial-time cost of O(n 3 ) at each scheduling event, where n is the number of activities in the ready queue. We evaluate the performance of GUS through simulation and by an actual implementation on a real-time POSIX operating system. Our simulation studies and implementation measurements reveal that GUS performs close to, if not better than, the existing algorithms for the cases that they apply. Furthermore, we analytically establish several timeliness and non-timeliness properties of GUS.Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

. His work focuses on dynamic and distributed decision making, and man-machine interface issues. AbstractThis study examined laypeople's understanding of a simple dynamic system, expressed in reasoning and strategies used by the subjects, and how it affected performance. Participants were 15 undergraduate psychology students, 4 male and 11 female; median age was 24 years, ranging from 21 to 31 years. The subjects' task was to establish equilibrium in a simple predatorand-prey system. A task analysis was performed to identify the problem structure, the vital aspects of the task, and the ideal strategies to perform the task. The subjects' actual performance was compared to these strategies. The results revealed that, even though the task was structurally simple, it was still difficult. Much of these difficulties seemed to stem from a low ability to apply indirect reasoning and thinking in terms of discrete time steps instead of in terms of continuous time. Copyright  2003 John Wiley & Sons, Ltd. Syst. Dyn. Rev. 19, 119-137, (2003 People are inevitably confronted with the task of exercising control over dynamic systems such as company production, the economy of a country, global warming, or even interpersonal relationships. The situations at hand may be described as dynamic decision problems that require a series of related decisions, in which the situation changes both by itself and in response to actions taken. In most cases there is also a certain amount of time pressure; actions have to be taken at the right moment (Brehmer and Allard 1991).The literature on dynamic decision making has primarily focused on how people deal with feedback and delays within a system (e.g. Brehmer 1995;Brehmer and Allard 1991;Diehl and Sterman, 1995;Dörner and Preußler 1990; Sterman 1989a, b). Research has been concerned more with the subjects' achievement in terms of their ability to control the system than with their understanding of the system with which they are interacting.In the area of management there is an increasing interest in enhancing decision makers' understanding of the complex and dynamic systems they are required to control. The task is formulated as one of developing systems thinking (Senge 1990). Systems thinking skills include understanding how system behavior over time is generated by interactions between its parts,

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