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Computing Applications Emergency response information systems: emerging trends and technologies

Decision Support Systems

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  1. Introduction
  2. Fit Technology to Purpose
  3. A Knowledge Management Perspective and Collaboration
  4. Data Quality
  5. Audit
  6. Conclusion
  7. References
  8. Authors

Decision support system" (DSS) has become a multifaceted term covering a wide range of functions and uses, too many to list fully here. For example, systems might support operational, tactical, or strategic decision making. They might simply provide summaries of data; they might forecast future developments in the context of present circumstances or they might simulate the future after some postulated action has been taken; they might take account of uncertainties; and they might help the decision makers explore their own perceptions and values. Further, they might be designed to work with individuals or with groups, and the groups may work in the same time and place or at distant locations, working perhaps asynchronously over the Internet. The systems may be built on large databases or models or both, or they may simply seek to organize and communicate results to differently skilled groups of decision makers to build a shared understanding. Here, we shall focus on some issues that seem key to us in the role of DSS in emergency response and management (for more general discussions, see [2, 3].)

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Fit Technology to Purpose

All information systems must be tailored to purpose and context and embedded into the prevailing culture if they are to provide the support that is intended. This is particularly true in the case of DSS, which are designed specifically to affect the decision making and therefore the behavior of the organization concerned. This point is often overlooked by some of the purveyors and developers of technology. Artificial intelligence approaches, such as neural nets, case-based reasoning, and expert systems, which need to assimilate training sets based upon past similar events, may help in identifying evacuation routes and allocating transportation resources to use, but they are unlikely to help in a decision on whether to evacuate. Such decisions are much more strategic and are affected by unique circumstances; they require decision tools that promote the identification and the balancing of objectives, and often before that they need tools to catalyze issue identification and problem structuring. Underlying the DSS tools must be a decision transaction system that knows explicitly what roles are being performed and which roles are responsible for reacting to the events taking place. Whatever DSS tools are appropriate at a specific point in the emergency management, it is likely that other types of DSS technology will be appropriate at other points and all need to be tailored to integrate into the full management process [1, 5].

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A Knowledge Management Perspective and Collaboration

Recent work in knowledge management and the design of knowledge management systems (KMS) has recognized that it is necessary to manage both tacit and explicit knowledge and that in many emergencies the need to manage tacit knowledge is considerable. Many emergencies arise because of some unanticipated event or combination of events. In such circumstances, there is seldom sufficient explicit knowledge codified in databases and models to address the issues; one needs tacit knowledge. Within KMS tacit knowledge is handled through collaboration tools. While there are group DSS that support collaboration, the deployment of their collaborative technologies within the context of emergency management is not common [1].

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Data Quality

Data is seldom as clean as one might like; its collection and transmission are subject to a range of potential errors. Therefore its quality must be assessed and due account taken [4]. In the heat of an emergency, errors are all the more likely. Thus, one of the tasks of DSS is to identify conflicting data and bring it to the attention of the managers. When expert judgment is introduced—almost inevitably some aspect of an emergency will need to be managed on the basis of expert judgment—the potential for conflicting evidence is yet greater. There has been a tendency to suggest that the DSS should seek to construct some sort of average to resolve such conflicts or even to select the `correct’ data and advice; and there are statistical techniques to help in this. It is unwise, however, to rely on algorithms to remove the conflicts; these should be presented to the managers so that they appreciate the uncertainty that is present. Few emergency management DSS present the managers with a clear overview of the inherent uncertainties.

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Audit

All organizations face some sort of audit; emergency management organizations inevitably face very detailed audits during the post-mortems and inquiries that follow any event. Thus a key function of a DSS is to monitor, prompt, and continuously record the execution of decision processes to ensure their appropriateness [4]. This has two significant implications for emergency management:

  • The decision process should be monitored and prompted to ensure that the responses to many diverse events taking place are coordinated among the many different functions.
  • The systems should draw upon and integrate communication and visualization aids to build a collaborative understanding of what is happening; they should foster shared mental models among the management team [6].

To achieve this, the several DSS tools used in emergency management need embedding in workflow systems. Moreover, such workflow systems cannot be hardwired. Rather they should be built upon flexible templates to ensure the specific character of any event can be met appropriately. The Sarbanes-Oxley Act requires the same type of software to track and inform managers when to undertake a certain role in the event that transactions associated with any type of decision that will potentially affect the value of an organization. Since the computer does not care how fast a decision process is executed, this software could service the emergency management and business continuity function.

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Conclusion

We have barely sketched the subject of DSS, and the points we have made have been partial and selective. However, we believe the issues described here are key for the successful development and integration of DSS into emergency management processes. Further discussions of the design of DSS for emergency response are available in [1] and [4].

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    1. French, S., Carter, E., and Niculae, C. Decision support in nuclear and radiological emergency situations: Are we too focused on models and technology? International Journal of Risk Assessment and Management (2007).

    2. French, S. and Geldermann, J. The varied contexts of environmental decision problems and their implications for decision support. Environmental Science and Policy 8 (2005), 378–391.

    3. Marakas, G.M. Decision Support Systems in the 21st Century. Prentice Hall, Upper Saddle River, NJ, 2003.

    4. Turoff, M. et al. Assuring homeland security: Continuous monitoring, control, and assurance of emergency preparedness. Journal of Information Theory, Technology, and Applications 6, 3 (2004), 1–24.

    5. Turoff, M., Chumer, M., Van de Walle, B., and Yao, X. The design of a dynamic emergency response management information system (DERMIS). Journal of Information Theory, Technology, and Applications 5, 4 (2004), 1–36.

    6. Weick, K.E. and Sutcliffe, K. Managing the Unexpected: Assuring High Performance in an Age of Complexity. Jossey Bass, San Francisco, CA, 2001.

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