Interorganizational Information Exchange and Efficiency: Organizational Performance in Emergency Environments

Achieving efficiency in coordinated action in rapidly changing environments has challenged both researchers and practitioners. Emergency events require both rapid response and effective coordination among participating organizations. We created a simulated operations environment using agent-based modeling to test the efficiency of six different organizational designs that varied the exercise of authority, degree of uncertainty, and access to information. Efficiency is measured in terms of response time, identifying time as the most valuable resource in emergency response. Our findings show that, contrary to dominant organizational patterns of hierarchical authority that limit communication among members via strict reporting rules, any communication among members increases the efficiency of organizations operating in uncertain environments. We further found that a smaller component of highly interconnected, self adapting agents emerges over time to support the organization\'s adaptation in changing conditions. In uncertain environments, heterogeneous agents prove more efficient in sharing information that guides coordination than homogeneous agents.Agent-Based Simulation, Emergency Management, Network Evolution, Performance

An approximation of surprise index as a measure of confidence

Probabilistic graphical models, such as Bayesian networks, are intuitive and theoretically sound tools for modeling uncertainty. A major problem with applying Bayesian networks in practice is that it is hard to judge whether a model fits well a case that it is supposed to solve. One way of expressing a possible dissonance between a model and a case is the surprise index, proposed by Habbema, which expresses the degree of surprise by the evidence given the model. While this measure reflects the intuition that the probability of a case should be judged in the context of a model, it is computationally intractable. In this paper, we propose an efficient way of approximating the surprise index

Local Probability Distributions in Bayesian Networks: Knowledge Elicitation and Inference

Bayesian networks (BNs) have proven to be a modeling framework capable of capturing uncertain knowledge and have been applied successfully in many domains for over 25 years. The strength of Bayesian networks lies in the graceful combination of probability theory and a graphical structure representing probabilistic dependencies among domain variables in a compact manner that is intuitive for humans. One major challenge related to building practical BN models is specification of conditional probability distributions. The number of probability distributions in a conditional probability table for a given variable is exponential in its number of parent nodes, so that defining them becomes problematic or even impossible from a practical standpoint. The objective of this dissertation is to develop a better understanding of models for compact representations of local probability distributions. The hypothesis is that such models should allow for building larger models more efficiently and lead to a wider range of BN applications

The SERIES model: development of a practitioner focused emergency response evaluation system

Purpose - Effective Emergency Response Management (ERM) system evaluation is vital to the process of continual improvement within emergency response organizations. The purpose of this paper is to investigate if an entire ERM system can be captured and encoded within a standardized framework. Design/Methodology/Approach - Employing an exploratory approach we apply a mixed methods case study design and inductive reasoning to analyse documentary evidence provided during the inquest into the London Bombings 2005. We use content analysis to investigate the nature of ERM system data availability and apply principals of Network Theory to iteratively develop a framework within which data can be encoded. Findings - We find that complex ERM system data can be captured and stored within a standardized framework. We present a conceptual framework and multi-stage mixed methods process, the Standardized Emergency Response Incident Evaluation System (SERIES) model, to support data collection, storage and interpretation. Our findings demonstrate that ERM system evaluation can benefit from the adoption of a standardized mixed-methods approach employing data transformation and triangulation. We also demonstrate the potential of the proposed standardized model, by integrating qualitative and quantitative data, to support interpretation and reporting through the use of appropriate data visualization. Originality / Value – The SERIES model provides a practical tool and procedural guidelines to capture and share vital ERM system data and information across all emergency services. It also presents an opportunity to develop a large comprehensive multi-incident dataset to support academic inquiry and partnership between academics and practitioners

Participatory modelling for stakeholder involvement in the development of flood risk management intervention options

Advancing stakeholder participation beyond consultation offers a range of benefits for local flood risk management, particularly as responsibilities are increasingly devolved to local levels. This paper details the design and implementation of a participatory approach to identify intervention options for managing local flood risk. Within this approach, Bayesian networks were used to generate a conceptual model of the local flood risk system, with a particular focus on how different interventions might achieve each of nine participant objectives. The model was co-constructed by flood risk experts and local stakeholders. The study employs a novel evaluative framework, examining both the process and its outcomes (short-term substantive and longer-term social benefits). It concludes that participatory modelling techniques can facilitate the identification of intervention options by a wide range of stakeholders, and prioritise a subset for further investigation. They can help support a broader move towards active stakeholder participation in local flood risk management

Prognostic Modelling with Dynamic Bayesian Networks

In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not possible and then how a simple change to the model allows us to represent different maintenance policies for repairable equipme

Developing a Decision Analytic Framework Based on Influence Diagrams in Relation to Mass Evacuations

Presented at International Conference on Emergency Preparedness "The Challenges of Mass Evacuation" 21st - 23rd September 2010 Aston Business SchoolIn this paper, we examine the role which decision analysis can play in a situation requiring a mass evacuation. In particular, we focus on the influence diagram as a tool for reasoning and supporting decision-makers under conditions of risk and uncertainty. This powerful modelling tool can help to bridge multiple specialist domains and provide a common framework for supporting decision-makers in different agencies. An influence diagram is also referred to as a decision network and can be considered as an extension of a Bayesian network. Like a Bayesian network, it contains chance nodes which represent random variables and deterministic nodes which represent deterministic functions of input variables. However, in addition, an influence diagram contains decision nodes which represent decisions under local control and utility nodes which can represent a variety of costs and benefits. These might be measured in several dimensions including casualties and monetary units. Advantages of Bayesian networks and influence diagrams over more traditional risk and safety modelling approaches such as event trees and fault trees are discussed - in particular, the ease with which they represent dependencies between many factors and the different types of reasoning supported at the same time, e.g. predictive reasoning and diagnostic reasoning. An illustrative, generic influence diagram is presented of a situation corresponding to a CBRNE attack. We then consider how this generic model can be applied to a more specific scenario such as an attack at a sporting event. A variety of potential uses of the model are identified and discussed, along with problems which are likely to be encountered in model development. We argue that this modelling approach provides a useful framework to support cost-effectiveness studies and high-level trade-offs between alternative possible security measures and other resources impacting on response and recovery operations

Zastosowanie fuzji danych z czujników i eksploracji danych w prognozowaniu stężenia metanu w kopalniach węgla

In recent years we have experienced unprecedented increase of use of sensors in many industrial applications. Modern sensors are capable of not only generating large volumes of data but as well transmit ting that data through network and storing it for further analysis. These enable to create systems capable of real-time data fusion in order to predict events of interest. The goal of this work is to predict methane concentration levels in coal mines using data fusion and data mining techniques. The paper describes an application of a generic method that can be applied to arbitrary set of multivariate time series data in order to perform classification or regression tasks. The solution presented here was developed within the framework of IJCRS‘15 data mining competition and resulted in the winning model outperforming other solutions.W ostatnich latach można było zaobserwować niespotykany wzrost użycia czujników w wielu zastosowaniach przemysłowych. Nowoczesne czujniki są w stanie nie tylko generować duże ilości danych, lecz równie ż przysyłać te dane za pomocą sieci i przechowywać je do późniejszej analizy. Umożliwia to opracowanie systemów do łączenia danych w czasie rzeczywistym w celu prognozowania określonych zdarzeń. Celem niniejszej pracy jest prognozowanie poziomów stężenia m etanu w kopalniach węgla za pomoc ą technik fuzji danych i eksploracji danych. Artykuł przedstawia zastosowanie generycznej metody, która może być użyta do dowolnego zbioru danych wielowymiarowych szeregów czasowych w celu przeprowadzenia zadań klasyfikacji lub regresji. Zaprezentowane rozwiązanie zostało opracowane w ramach konkursu eksploracji danych IJCRS’15 i – pokonując inne rozwiązania – zostało jego zwycięzcą