Mutli-Agent Based Modeling

Establishment of a chain of command in a situation such as a severe storm or other disaster is essential. Because there may be a disruption to commonly used manners of communication, such as television, telephones, and the internet, it is important to have a clear outline of where and to whom to report, how to find answers, and who will perform necessary tasks. In the simulation, the Public Health Department is both a link in the larger, county-wide chain of command and has its own hierarchy structure. Within the scope of the county, the Public Health Department reports to the Medical/Health Branch Director who is directly under the Operations Chief. The Operations Chief, who is in charge of managing and monitoring the actions of each department, reports to the Emergency Operations Commander. The Emergency Operations commander is the highest link in the chain, not only overseeing the operations of the various governmental departments, but also the officers in charge of Safety, Public Information, and Legal.

In the Disaster in Franklin County simulation (Regents of the University of Minnesota [UMN], 2006), there were several key personnel in the incident command team. This concept is utilized in real disasters when the Public

Disaster relief operations are complex systems having more to them than just a response mechanism. They require a significant amount of pre-planning.

Natural and man-made disasters have increased in the past decade, and due to these changes, Emergency Managers had to make drastic changes in order to improve the way first responders operate in a disaster area.

Today, the Incident Command System (ICS) is a major component of NIMS and is widely used in emergency management response. However, this was not always the case. According to David A. McEntire and Gregg Dawson, authors of the article, “The intergovernmental Context,” ICS was originally developed by the fire service in 1970. Its purpose was to assist in the command of wildfire events. It was unique because it standardized operations, yet offered flexibility so that it could be used on any number of events, regardless of size or type (McEntire & Dawson, 2007, p. 63).

Emergency management faces many challenges in today’s modern society. In the years prior to 9/11 emergency management was primarily focused on natural disasters. That has since changed; we now face a diverse variety of risks and hazards on a constant basis. As we continue to grow in population current and newer have compounded into more problems that emergency planner must face and find solutions for.

Data obtained by assessing social vulnerability must be implemented within each phase of the emergency management process; mitigation, response, and recovery. First, to effectively respond and recover from incidents emergency management agencies must concentrate on the mitigation phase to prevent incidents from happening in the first place. This is achieved through a thorough hazard/vulnerability analysis (HVA). This type of analysis assesses the risk of physical, economic, and social vulnerability within all communities of a given jurisdiction (Lindell et al., 2006, p. 165). Additionally, the basis of the HVA allows emergency managers to effectively plan for disaster by creating pre-planned responses to disasters (rather than improvised response) and staging resources to locations with the highest probability of risk; ultimately contributing to the mitigation and response phases.

Situational awareness is a crucial cog in the wheel of an efficient disaster response. Information on casualties, extent of damage, infrastructure and the present response efforts give emergency planners the way forward in the allocation of resources available. It helps in promoting preparedness, which requires the emergency response team to have detailed information about the risk that they are getting into (Haddow & Haddow, 2013).

Artificial intelligence, or AI, is a field of computer science that attempts to simulate characteristics of human intelligence or senses. These include learning, reasoning, and adapting. This field studies the designs of intelligent

This subject aims to teach students the main elements of emergency management for natural disasters and to a lesser degree terrorist attack. Students will understand the principles involved in emergency

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. Agent-based modeling is a new analytical method for the social sciences, but one that is quickly becoming popular. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use

A model is a representation of a real system and thus, it is an abstraction of the reality 4. “The word “modeling” comes from the Latin word modellus which describes a typical human way of coping with the reality” (Schichl, n.d.). Models can take various forms such as mathematical equation, drawing, computer code, etc. However, there is a common purpose of all designed models, which is to simplify the complexity presented in the real system or problem. Therefore, models usually contain only the main aspects of the real system (not all details).

Meta-matrix contains various kinds of nodes and internode type links. This network has sub-network such as agent-agent network, agent- knowledge network. By including these networks the interactions among the agents can be simulated. Illustrative example of Meta-matrix network is shown below

An agent-based model (ABM) is used here to examine what effect village attitudes toward forest conservation have on the future landscape and extent of forest cover in Bachauli, Nepal if improved forest conservation-related policies are implemented, population growth rate fluctuates, and villages are able to mimic one another’s attitudes toward forest conservation-related behaviors and land use/land cover change (LULCC) decisions. The model integrates land cover data and household attitudes toward forest conservation, community forestry, and forest governance institutions in Nepal. Results suggest that implementing policies aimed at improving individual attitudes toward forest conservation-oriented behaviors would affect forest cover over time. The ability for villages to mimic their neighbors, regardless of varying probabilities of occurrence, was found to have little effect on forest cover. Additionally, population growth rate was found to have a significant effect on LULCC. Despite clear strengths, many challenges still exist with modeling forest conservation dynamics and LULCC in developing countries such as Nepal. Here, we give an overview of some of the challenges we encountered with modeling LULCC in the place-specific context of Bachauli, Nepal—highlighting specific areas in the field which necessitate future improvement.

AGENT-BASED MODELING AND SIMULATION: DESKTOP ABMS Charles M. Macal Michael J. North *NetLogo is a free ABMS environment (Wilensky 1999) developed at Northwestern University’s Center for Connected Learning and Computer-Based Modeling (http://ccl.northwestern.edu/netlogo/). The NetLogo language uses a modified version of the Logo programming language (Harvey 1997). NetLogo is designed to provide a basic computational laboratory for teaching complex adaptive systems concepts.