Laurie B. Waisel

Visualization and the process of modeling: a cognitive-theoretic view

are artifacts used to better understand our world. In the context of mining data to create knowledge, the modeler is often faced with discovering and understanding relationships in data that have no apparent analog in the laws of physical science. Much of these data are increasingly abstract; not directly related to the physical, perceivable world. The use of sketches and diagrams as aids in problem solving and as a means of communication is as old as recorded history. Can visualization help us not only to discover the patterns and relationships in these data, but also to use newly discovered knowledge to build computational models? The purpose of this paper is to provide a description of the process of modeling and, based upon theories of cognition, show how visualization can assist in developing and assessing computational models for very large, high dimensional data sets.

Employing virtual reality to support decision making in emergency management

Abstract Virtual reality is a popular term that refers to a variety of computer-generated and synthetic experiences. The core of virtual reality is an advanced interface to a human-machine simulation system, in which the system amplifies human intelligence rather than replacing it. Virtual reality consists of virtual sensations, visions, sounds, and other kinds of perceptual input. Technical components used in virtual reality range from mouse and voice input devices to motion detection systems and artificial skin. The user lives and acts in Cyberspace — the virtual world can be connected to the real world through teleoperation. The field of virtual reality is still in its infancy. Moving, feeling, and acting in Cyberspace is still a primitive technology. However, it is in this early stage of development that potential applications in emergency management should be investigated. The potential applications include training of emergency responders, virtual world navigation, tele-virtual conferencing, teleoperation, and telepresence. The purpose of this paper is twofold: (1) to provide the emergency management community with a pragmatic overview of virtual reality technology; and (2) to propose where the technology, as it emerges, could support problem solving and decision making in emergency management from prevention and mitigation to response and recovery.

Visualization and model formulation: an analysis of the sketches of expert modellers

We report on an exploratory study of the use of sketches by experts engaged in the formulation of operational research models. We outline a methodology to identify sketches, link them to text in an associated verbal protocol, classify them, and relate their use to the modelling process. We found that use of sketches is part of the personal modelling style of experts, that sketches are used more when the focus is on model structure or realization than on model context or assessment, that sentential sketches tend to be started sooner than diagrammatic sketches, and that sketching generally begins earlier on more difficult problems. We expect that such descriptive studies of expert behaviour will be useful in developing a science of modelling and in teaching novice modellers.

The Pothole Lake fire: an analysis of emotion in a successful emergency response

This study explores the emotional content of an effective disaster response situation and its coordination by the Incident Command System. Twenty-seven firefighters and local residents were interviewed during a major wildfire in 1991. The resulting videotapes were transcribed, coded, and analyzed for their emotional content. Results indicated that a variety of factors led to the success of the disaster response effort, including the use of the Incident Command System, luck, the nature of the disaster, and the cooperation of the town and local agencies. This methodology is recommended for future case studies researching emotions and crises.

Bad numbers: coping with flawed decision support

Evidence is accumulating that many spreadsheet-based decision support systems contain errors. These errors can result in bad numbers, which in turn could lead to bad decisions. We review the literature on the origins and consequences of bad (erroneous) numbers produced by models and/or decision support systems built around them. Then, we present a case study in which an experiment in visual support for a sequential decision-making task was tainted by bad numbers. Both the literature review and the experiment indicate a robust human ability to overcome flawed decision support. We conclude with questions that need to be addressed in order to better understand the capabilities of humans to deal with erroneous results from decision support systems.