La concentración de los lodos separados se determina considerando el proceso de su manipulación y vertido Pueden llevarse a cabo ensayos para determinar la correlación

Simulations are based on mathematical models. Although it seems that the notions of mathematical model and that of mathematical modeling are interchangeable, these two concepts have different implications. Modeling is a delicate process, the product of which is the actual model. Both of these concepts intend to represent some segment of reality, by using certain symbols that are written in a specific way, together with significant simplifications of the real processes.

In modeling, we move constantly between reality and mathematical theory. The process starts with observation of a (complex) real situation, and gradually develops towards its description through a set of relations. These are combined together with certain restrictions defining the observational domain. When this is done, we have a model of the given situation, i.e. a set of equations, functions and graphs that are combined together through implementation of mathematical skills and algorithms. Some of these algorithms already exist, and new ones may emerge during the analysis of the problem.

Simplification is an important part of the modeling process, since real phenomena tend to be too complicated for an in-depth analysis. Therefore, the key elements must be extracted and the non-important elements should be

IMPLEMENTATION OF THE TECHNOLOGY AND METHODOLOGY FOR MODELING AND SIMULATIONS IN CRISIS MANAGEMENT

applications include: evaluation of security plans and procedures at a nuclear plant; evaluation of city emergency response plans; etc.

Third area of application of MS tools in crisis management is identification & detection. Simulation tools for identification and detection of disaster events can be used for detailed analysis and developing techniques for identifying the possibility of occurrence prior to the event. The identification of factors that provide an early warning of impending disaster events can provide a valuable means to mitigate or even prevent the occurrence.

The identification and detection application will include use of tools that study given scenarios and determine the possibility of the occurrence of a disaster event. It is anticipated that such tools will use pattern matching logic and past history databases to identify and detect potential threats. Examples of identification and detection applications include: selecting security sweep targets in areas with majority of inhabitants from a target background, identifying the potential of tornado occurrence given the weather conditions, etc.

The most important area of application of MS tools in crisis management is training. Simulation tools for emergency response training mimic and present situations created by occurrence of a disaster event to human training subjects with the intent to improve their capabilities for emergency response. These tools extend from those targeted at decision makers to those targeted at first responders.

There is a growing need for preparedness for emergency response both for man-made and natural disaster events. The man-made disaster risk has increased due to a rise in possibility of terrorist attacks. Effective emergency response presents a number of challenges to the responsible agencies. One major challenge is the lack of opportunities to effectively train the emergency responders and the decision makers in dealing with the emergencies. On top of this, a real-event training approach is not appropriate, given the relatively infrequent occurrence of such events. The responsible agencies have tried to meet the need through organization of live exercises, but such events are hard to organize and expensive.

The integrated set of simulation tools should be used for training the emergency responders at all levels. It is important that first responders and emergency managers go through training experiences on the same scenarios to effectively work as a team. Human beings can make good decisions under a stressful situation if they can recognize the pattern as similar to something they

have experienced in the past (Klein 1989). If they have not experienced a similar situation in the past, their capability to make right decisions is impaired as there isn’t enough time to evaluate all the possible options and select the right one.

The last area of application of MS tools in crisis management is real-time response support. Disaster impact modeling tools focus on studying and projecting the impact of a disaster event. The projections can then be used for planning the response to the disaster event. In fact, a number of such tools exist. Mazzola et al. (1995) surveyed 94 tools for modeling of atmospheric dispersion.

The response application includes tools that evaluate the impact of a disaster through real-time updates on the situation, and uses available information to project current and future impact of the disaster. It also includes tools for evaluating alternative response actions and strategies based on the current and projected impact. The evaluations are then used to direct the response actions on the ground. Examples of response applications include: antidote deployment sequence; evacuation management; etc.

Specifics of mathematical models used in crisis management

simulations

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disregarded. When determining variables, the known should be separated from the unknown in the context of the information sought. The analysis must answer questions such as: What can we learn from the model? Is it compatible with the reality? Does it make sense? Does it answer the question? Among the problems that may surface are: non-reference to our aims, non-suitability of the problem for mathematical modeling, over-simplification, over-complexity, over-sensitivity to the initial conditions, results are too technical or cannot be implemented, the resources are not adequate for implementation of the suggested solution.

The model establishes dependence between several variables that are deemed important for the observed problem/phenomenon. At the end of the modeling process, we actually have a mathematical representation of a (nonmathematical) real situation. A new challenge follows: finding a solution to the model, i.e. an answer to the triggering question. The solution is then interpreted within the observed context. If it fails in matching the reality, some of the modeling phases will be additionally investigated and if needed, the whole process may be repeated from start. Table 1 below summarizes the modeling activities.

Table 1. Modeling phases and their respective activities.

Modeling phases Activities

Variables definition Extraction of important elements Definition of the problem Definition of input and output variables Model definition Relations among variables Formulation

Conjectures and initial conditions Solution Mathematical tools’ application _{Analysis of dependencies} Model interpretation _{Evaluation of the model}Solution analysis

Validation Improvements of the model Comparison to real data Revision of obtained solutions Presentation Summary of the results Solution interpretation

Comments

In crisis management, mathematical models can enable decision makers and crisis managers to:6

 model possible multi-sectoral crisis scenarios and assess the consequences of an incident;

 simulate possible impacts resulting from alternative actions;

 support strategic decisions on capabilities, related investments, reserves, inventories;

 optimize the deployment of resources dedicated to crisis response in- line with the evolvement of a crisis, and

 improve action plans for preparedness and response phases of the crisis management.

Within the process of producing scenarios, models can provide decision- making support in defining explicit and efficient strategies and subsequently, in comparing and ranking of scenarios. Having in mind the organizational specifics of crisis management structures as Multi-Agent Systems (MAS), it is important that the participating groups and their roles are clearly identified, as well as the coordination of the interaction patterns between agents, together with the interaction between agents and the changing environment.

In this sense, most current MS systems provide coordination and planning capabilities for teams of agents, often assuming the emergence of group behavior. The highly formalized organization processes defined by governments and aid agencies define a strict frame for action where the objectives are achieved through efficient coordinated actions of agents (the right agent is doing the right

thing), and this specifics should make an integral part of the simulation model.

Conclusion

The need for improved emergency response can be met by extensive use of MS tools. A number of individual M&S efforts for emergency response are already in progress; however, each individually can address only a small part of the problem. Effective use of MS requires study and analysis of all aspects of a disaster event occurrence and its follow through. Modeling and simulation of all aspects can be achieved by integrating the individual tools that model complementary aspects of the disaster event.

The capability to train responders and commanders together on a wide range of scenarios will enable development of effective emergency response

6_{CRISMA Integration Project, European Project FP7-SECURITY-284552} IMPLEMENTATION OF THE TECHNOLOGY AND METHODOLOGY FOR MODELING AND

Table 1. Modeling phases and their respective activities.

Modeling phases Activities

Variables definition Extraction of important elements Definition of the problem Definition of input and output variables Model definition Relations among variables Formulation

Conjectures and initial conditions Solution Mathematical tools’ application _{Analysis of dependencies} Model interpretation _{Evaluation of the model}Solution analysis

Validation Improvements of the model Comparison to real data Revision of obtained solutions Presentation Summary of the results Solution interpretation

Comments

In crisis management, mathematical models can enable decision makers and crisis managers to:6

 model possible multi-sectoral crisis scenarios and assess the consequences of an incident;

 simulate possible impacts resulting from alternative actions;

 support strategic decisions on capabilities, related investments, reserves, inventories;

 optimize the deployment of resources dedicated to crisis response in- line with the evolvement of a crisis, and

 improve action plans for preparedness and response phases of the crisis management.

thing), and this specifics should make an integral part of the simulation model.

Conclusion

The capability to train responders and commanders together on a wide range of scenarios will enable development of effective emergency response

6_{CRISMA Integration Project, European Project FP7-SECURITY-284552}

184

teams. Simulation and gaming-based technologies can together provide highly effective means for incident management training, if integrated correctly using an appropriate architecture.

The usefulness of a mathematical model does not consist only in obtaining a solution to an issue. It also helps in prioritizing the facts and determining the focal point of our observations. In crisis management, mathematical models can enable decision makers and crisis managers to model possible multi-sectoral crisis scenarios, assess the consequences of an incident and optimize the deployment of resources dedicated to crisis response in-line with the evolvement of a crisis.

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