The economy is studied at all scales, from micro to macro. With global trends toward rapid urbanization, one abstracted scale of the economy will become increasingly important to understand, that of a city economy. Working in close cooperation with the urban planning staff of a US city, the authors developed a system dynamics model of a city as a complex, adaptive, system of system. The economy sector of the model is distinguished by its incorporation of the citys highly porous boundaries and unification of multiple definitional approaches to the key measure of City Gross Domestic Product. The result is a system thinking tool for policy makers to explore the relationships between citywide, policy-initiated changes and the structurally determined performance of the city economy.
The purpose of the paper is to test whether people make different decisions when a task requires either a fixed delay or a continuous delay conceptualisation. With the help of a structurally simple dynamic decision making task, we test two conditions in a controlled experiment: hiring when personnel stays in an organisation for exactly ten years (fixed delay condition) or when personnel stays on average for ten years (continuous delay condition). In this preliminary study, 71 participants were tested. Findings so far show no differences in performance between the groups, indicating that they most likely use the same cognitive representation of the task. Since participants answers are substantially closer to the fixed delay condition, we assume that people have the tendency to conceptualise lags in the form of discrete delays, at least in the context of personnel hiring. Research implications comprise the repetition of the experiment to achieve a higher number of participants and to allow for a more extreme differentiation between the two conditions. Practical implications regard the formulation of decision making tasks within organisations, for instance in human resource management. The value of this paper lies in its rigorous usage of a structurally simple dynamic task to shed light on a fundamental trait of human decision making.
To better understand the performance of hospital operations in response to IT-enabled improvement, we report the results of a system dynamics model designed to improve core medical processes. Utilizing system dynamics modeling and emerging Health Information Systems (HIS) data, we demonstrate how current behavior within the hospital leads to a stove-pipe effect, in which each functional group employs policies that are rational at the group level, but that lead to inefficiencies at the hospital level. We recommend management improvements in both materials and staff utilization to address the stove-pipe effect, estimate the resultant cost-saving, and report the results of a new experiment conducted in the hospital to validate our approach. We believe that the major gains in health information systems use will accompany new information gathering capabilities, as these capabilities result in collections of data that can be used to greatly improve patient safety, hospital operations, and medical decision support.