From solitary to an adaptive continuum process: Toward a new framework of natural disaster emergency decision-making

Major studies in emergency decisions are focusing on how techno-rational approaches applied in early warning systems to produce an output; rarely explore its opponent, the naturalistic intervention, or how both paradigms function in a crisis decision process. This research aims to identify the actual process of emergency decision making in the context of natural hazard studies, whether it employs the techno-rational or purely naturalistic approach. A systematic review is adopted to assess papers in the period 2000-2018 within the ‘emergency decision making’ AND “natural disaster” keywords. Research finds a non-techno-rational paradigm that contributes to producing a decision outcome. Instead of categorizing it the naturalistic paradigm as named by the scholars, we labelled it a non-technological paradigm. It consists of two main instruments: individual and institutional interventions, that together with the techno-rational instrument develop an adaptive continuum behavior while operating in uncertainty condition in order to generate an effective evacuation order for vulnerable people

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

Extreme Events Decision Making in Transport Networks: A Holistic Approach Using Emergency Scenarios and Decision Making Theory

This paper proposes a novel method to analyse decision-making during extreme events. The method is based on Decision-making Theory and aims at understanding how emergency managers make decisions during disasters. A data collection framework and an analysis method were conceptualized to capture participant’s behaviour, perception and understanding throughout a game-board simulation exercise, which emulates an earthquake disaster scenario affecting transport systems. The method evaluates the participant’s actions in order to identify decision-making patterns, strengths and weaknesses. A set of case studies has shown two typical patterns, namely: a) Support immediate rescue; b) Support lifelines recovery. Good decision-making practices regard to objective-oriented decision making, understanding of conflicting priorities and appropriate resource management. Weaknesses are associated with comprehending relationships between community/environment and projecting future scenarios. Overall, the case study’s results demonstrate the efficiency and robustness of the proposed method to analyse decision making during disasters

Uncertainty and decision making: Volcanic crisis scenarios

AbstractThe impact of uncertainty on Disaster Risk Reduction decision-making has become a pressing issue for debate over recent years. How do key officials interpret and accommodate uncertainty in science advice, forecasts and warnings into their decision making? Volcanic eruptions present a particularly uncertain hazard environment, and to accommodate this scientists utilize probabilistic techniques to inform decision-making. However, the interpretation of probabilities is influenced by their framing. We investigate how verbal or numerical probabilities affect decisions to evacuate a hypothetical town, and reasons given for that decision, based upon a volcanic eruption forecast. We find fewer evacuations for verbal terms than for equivalent numerical terms, and that the former is viewed as more ambiguous. This difference is greater for scientists, which we suggest is due to their greater familiarity with numerical probabilities and a belief that they are more certain. We also find that many participants have a poor understanding of the relationship between probability and time window stated, resulting in an incorrect assessment of overall likelihood and more evacuations for the lower likelihood version of two scenarios. Further, we find that career sector (scientist or non-scientist) influences evacuation decisions, with scientists tending to reduce the uncertainty by focusing on the quality and volume of information provided, while non-scientists tended to either acknowledge or suppress the uncertainty, focusing on actions to take. These findings demonstrate the importance of identifying communication strategies that mitigate different perceptions of forecasts, to both enhance end-user decision making and to prevent premature, delayed, or unnecessary actions

DECISIONS IN THE DARK: A FRAMEWORK FOR DECISION-MAKING IN UNFAMILIAR SITUATIONS

This thesis seeks to understand an appropriate decision-making framework for the fire service to use in unfamiliar situations. Firefighters and emergency responders rely on pattern recognition when they are presented with familiar situations; however, relying on such intuition can result in costly time delays. A case study method was used to evaluate decision-making during disasters in the fire service and the mining industry. The fire service cases include the 1949 Mann Gulch Fire and the fire service response to the 9/11 attack on the World Trade Center. The mining case studies, both of which occurred in 2010, include the Deepwater Horizon oil spill and the Chilean mine collapse. The fire service cases were assessed to determine which decision-making tools were utilized and what additional factors influenced positive and negative outcomes throughout the events. The mining cases were evaluated to understand organizational structures and response systems. This thesis recommends that fire service leaders utilize expanded interdisciplinary teams to creatively seek alternative solutions when addressing unfamiliar problems. Using such teams will require leaders to expand response frameworks and alter familiar patterns of response to include outside agencies and nontraditional emergency responders. Finally, leaders should deliberately encourage open communication about successes and failures to encourage collaboration and innovation throughout the response.Civilian, Portland Fire and RescueApproved for public release. Distribution is unlimited

Accelerating Expertise to Facilitate Decision Making in High-Risk Professions Using the DACUM System

The purpose of this research was to determine whether the process of achieving occupational expertise could be accelerated enabling operators in high risk vocations to make effective decisions earlier in their careers. Scholars have hypothesized good decision making skills are largely a result of relevant experience within the specific domain. The rationale being that the greater the experience an individual has the more likely the operator has experienced similar situations and can apply solutions that have been successful in the past. Two distinct methods of decision making have been identified: traditional decision making and naturalistic decision making (NDM). The ability to implement the traditional decision making method effectively is contingent on the availability of sufficient information and adequate time for the individual to examine the information, construct and weigh options, and ultimately choose the action that the operator deems most appropriate given the data at the time. Naturalistic decision making is a process an operator can employ in a high risk, dynamic situation (e.g., military personnel in combat, fireground commanders on-scene, police officers confronting armed criminals) to make decisions when data may be incomplete and time is critically short. Both processes depend on the operator\u27s domain expertise. Research has shown the naturalistic decision making process is the method many high risk operators revert to when conditions do not permit a deliberate, analytical decision-making approach. These conditions include ambiguous situations, serious time constraints, or inadequate information. Studies have determined that the fundamental element of NDM is domain experience, i.e., the seasoned decision-maker compares the current situation to a similar experience from the past. This pattern recognition enables the decision maker to apply tactics that successfully resolved previous problems. The overarching limitation in NDM is gaining the requisite domain experience. One pedagogical process that has been recognized to enable occupational instructors to identify requisite skills and accelerate the process of placing operators in their chosen vocation is the method known as Design A Curriculum (DACUM). The DACUM process breaks an occupation down into areas of competence and the skills required within each area. Each skill level is given a numerical rating indicating the minimum performance standard for that skill. An operator with skills from a similar occupation can test for that skill and if the minimum performance level is achieved the operator is given credit for that skill and can focus subsequent efforts on other areas or skills. The DACUM process can help accelerate the training process and place an operator into the vocation sooner and thus begin gaining experience in the domain. The DACUM process was employed for this research. A panel of expert firefighter instructors were assembled and spent two days analyzing the occupation of acquired structure live burn instructor

Role of opinion sharing on the emergency evacuation dynamics

Emergency evacuation is a critical research topic and any improvement to the existing evacuation models will help in improving the safety of the evacuees. Currently, there are evacuation models that have either an accurate movement model or a sophisticated decision model. Individuals in a crowd tend to share and propagate their opinion. This opinion sharing part is either implicitly modeled or entirely overlooked in most of the existing models. Thus, one of the overarching goal of this research is to the study the effect of opinion evolution through an evacuating crowd. First, the opinion evolution in a crowd was modeled mathematically. Next, the results from the analytical model were validated with a simulation model having a simple motion model. To improve the fidelity of the evacuation model, a more realistic movement and decision model were incorporated and the effect of opinion sharing on the evacuation dynamics was studied extensively. Further, individuals with strong inclination towards particular route were introduced and their effect on overall efficiency was studied. Current evacuation guidance algorithms focuses on efficient crowd evacuation. The method of guidance delivery is generally overlooked. This important gap in guidance delivery is addressed next. Additionally, a virtual reality based immersive experiment is designed to study factors affecting individuals\u27 decision making during emergency evacuation

AGENT-BASED DISCRETE EVENT SIMULATION MODELING AND EVOLUTIONARY REAL-TIME DECISION MAKING FOR LARGE-SCALE SYSTEMS

Computer simulations are routines programmed to imitate detailed system operations. They are utilized to evaluate system performance and/or predict future behaviors under certain settings. In complex cases where system operations cannot be formulated explicitly by analytical models, simulations become the dominant mode of analysis as they can model systems without relying on unrealistic or limiting assumptions and represent actual systems more faithfully. Two main streams exist in current simulation research and practice: discrete event simulation and agent-based simulation. This dissertation facilitates the marriage of the two. By integrating the agent-based modeling concepts into the discrete event simulation framework, we can take advantage of and eliminate the disadvantages of both methods.Although simulation can represent complex systems realistically, it is a descriptive tool without the capability of making decisions. However, it can be complemented by incorporating optimization routines. The most challenging problem is that large-scale simulation models normally take a considerable amount of computer time to execute so that the number of solution evaluations needed by most optimization algorithms is not feasible within a reasonable time frame. This research develops a highly efficient evolutionary simulation-based decision making procedure which can be applied in real-time management situations. It basically divides the entire process time horizon into a series of small time intervals and operates simulation optimization algorithms for those small intervals separately and iteratively. This method improves computational tractability by decomposing long simulation runs; it also enhances system dynamics by incorporating changing information/data as the event unfolds. With respect to simulation optimization, this procedure solves efficient analytical models which can approximate the simulation and guide the search procedure to approach near optimality quickly.The methods of agent-based discrete event simulation modeling and evolutionary simulation-based decision making developed in this dissertation are implemented to solve a set of disaster response planning problems. This research also investigates a unique approach to validating low-probability, high-impact simulation systems based on a concrete example problem. The experimental results demonstrate the feasibility and effectiveness of our model compared to other existing systems