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The purpose of this paper is to contribute information and recommendations that could better equip emergency managers to prepare for and respond to emergencies and disasters, with a focus on improving their decision‐making capabilities during response.

A questionnaire‐based survey approach was used in this research and 48 different local government organisations participated. These results were examined in conjunction with contemporary emergency management decision‐making literature. A combination of closed and open ended questions was used, enabling qualitative and quantitative analysis.

Results suggest that while there is information available about decision making, not all emergency managers are aware of the existence of this information or understand its relevance to emergency management. It is likely that those who did have a comprehensive understanding of decision making had gained this knowledge through non‐emergency management‐related courses. In total, 71 percent of participants said they would be interested in receiving more support regarding training and practice for decision making in Emergency Operations Centres.

A wide body of research has investigated decision‐making styles. However, this paper shows that in the local government emergency management sector there is little awareness of the understanding of the different decision‐making approaches. In addition, for those organisations surveyed, there is a great desire for further training and practice in decision making. It is thus vital that this need is addressed, to further improve the future response of these organisations to emergencies.

Decision-making problems in emergency plan selection for epidemic prevention and control (EPAC) are generally characterized by risky and uncertainty due to multiple possible emergency states and vagueness of decision information. In the process of emergency plan selection for EPAC, it is necessary to consider several obvious features, i.e. different states of epidemics, dynamic evolvement process of epidemics and decision-makers' (DMs') psychological factors such as risk preference and loss aversion.

In this paper, a novel decision-making method based on cumulative prospect theory (CPT) is proposed to solve emergency plan selection of an epidemic problem, which is generally regarded as hybrid-information multi-attribute decision-making (HI-MADM) problems in major epidemics. Initially, considering the psychological factors of DMs, the expectations of DMs are chosen as reference points to normalize the expectation vectors and decision information with three different formats. Subsequently, the matrix of gains and losses is established according to the reference points. Furthermore, the prospect value of each alternative is obtained and the comprehensive prospect values of alternatives under different states are calculated. Accordingly, the ranking of alternatives can be obtained.

The validity and robustness of the proposed method are demonstrated by a case calculation of emergency plan selection. Meanwhile, sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and TODIM (an acronym in Portuguese for interactive and MADM) method illustrate the effectiveness and superiority of the proposed method.

An emergency plan selection method is proposed for EPAC based on CPT, taking into account the psychological factors of DMs.

Highlights

1. This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

2. The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

3. This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

4. The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

This paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.

The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.

This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.

The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.

This study aims to explain the primacy that rapid, centralised decision-making gained in higher education institutions during the COVID-19 pandemic, with a particular focus on Australian universities.

This paper draws on discussions regarding policy problems of an international, purpose-convened on-line policy network involving over 100 registrations from multiple countries. It analyses emerging institutional policy governance texts and documents shared between network participants, applies policy science literature regarding traditional institutional policy-making routines and rapid decision-making, and references media reportage from 2020. The paper traces how higher education institutions rapidly adjusted to pandemic conditions and largely on-line operations.

The study finds that higher education institutions responded to the COVID-19 crisis by operationalising emergency management plans and introducing rapid, centralised decision-making to transition to remote modes of operation, learning and research under state-imposed emergency conditions. It highlights the need to ensure robust governance models recognising the ascendance of emergency decision-making and small-p policies in such circumstances, notwithstanding longstanding traditions of extended collegial policy-making routines for big-P (institutional) Policy. The pandemic highlighted practice and policy problems subject to rapid reform and forced institutions to clarify the relationship between emergency planning and decision-making, quality and institutional policy.

In covering a range of institutional responses, the study advances the possibility of institutions planning better for unexpected, punctuated policy shifts during an emergency through the incorporation of rapid decision-making in traditionally collegial environments. At the same time, the paper cautions against the normalisation of such processes. The study also highlights key practices and policies that require urgent reconsideration in an emergency. The study is designed as a self-contained and freestanding narrative to inform responses to future emergencies by roundly addressing the particularities of the 2020 phase of the COVID-19 pandemic as it affected higher education.

There is only limited research on policy-making in higher education institutions. This research offers an original contribution on institutional policy-making during a prolonged emergency that deeply changed higher education institution’s governance, operations and outlook. Particularly significant is the synthesis of experiences from a wide range of sector personnel, documenting punctuated policy shifts in policy governance (meta-policy), institutional policy-making routines and quality assurance actions under great pressure. This paper is substantially developed from a paper given at the Association for Tertiary Education Management Institutional Policy Seminar, 26th October 2020.

For some specific multi-criteria decision-making (MCDM) problems, especially in emergency situations, because of the feature of criteria and other fuzzy factors, it is more appropriate that values of different criteria are expressed in their correspondingly appropriate value types. The purpose of this paper is to build a multi-criteria group decision-making (MCGDM) model dealing with heterogeneous information based on distance-based VIKOR to solve emergency supplier selection in practice appropriately and flexibly, where a compromise solution is more acceptable and suitable.

This paper extends the classical VIKOR to a generalized distance-based VIKOR to handle heterogeneous information containing crisp number, interval number, intuitionistic fuzzy number and hesitant fuzzy linguistic value, and develops an MCGDM model based on the distance-based VIKOR to handle the multi-criteria heterogeneous information in practice. This paper also introduces a parameter called non-fuzzy degree for each type of heterogeneous value to moderate the computation on aggregating heterogeneous hybrid distances.

The proposed distance-based model can handle the heterogeneous information appropriately and flexibly because the computational process is directly operated on the heterogeneous information based on generalized distance without a transformation process, which can improve the decision-making efficiency and reduce information loss. An example of emergency supplier selection is given to illustrate the proposed method.

This paper develops an MCGDM model based on the distance-based VIKOR to handle heterogeneous information appropriately and flexibly. In emergency supplier selection situations, the proposed decision-making model allows the decision-makers to express their judgments on criteria in their appropriate value types.

The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology.

In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory.

This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events.

The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.

This paper aims to demonstrate how to make emergency decision when decision makers face a complex and turbulent environment that needs quite different decision-making processes from conventional ones. Traditional decision techniques cannot meet the demands of today’s social stability and security.

The main work is to develop an instance-driven classifier for the emergency categories based upon three fuzzy measures: features for an instance, solution for the instance and effect evaluation of the outcome. First, the information collected from the past emergency events is encodes into a prototype model. Second, a three-dimensional space that describes the locations and mutual distance relationships of the emergency events in different emergency prototypes is formulated. Third, for any new emergency event to be classified, the nearest emergency prototype is identified in the three-dimensional space and is classified into that category.

An instance-driven classifier based on prototype theory helps decision makers to describe emergency concept more clearly. The maximizing deviation model is constructed to determine the optimal relative weights of features according to the characteristics of the new instance, such that every customized feature space maximizes the influence of features shared by members of the category. Comparisons and discusses of the proposed method with other existing methods are given.

To reduce the affection to economic development, more and more countries have recognized the importance of emergency response solutions as an indispensable activity. In a new emergency instance, it is very challengeable for a decision maker to form a rational and feasible humanitarian aids scheme under the time pressure. After selecting a most suitable prototype, decision makers can learn most relevant experience and lessons in the emergency profile database and generate plan for the new instance. The proposed approach is to effectively make full use of inhomogeneous information in different types of resources and optimize resource allocation.

The combination of instances can reflect different aspects of a prototype. This feature solves the problem of insufficient learning data, which is a significant characteristic of emergency decision-making. It can be seen as a customized classification mechanism, while the previous classifiers always assume key features of a category.

The purpose of this study is to explore the applicability of analytic hierarchy process (AHP) to collective decision-making of local and state disaster managers for their efficient and effective allocation of limited financial resources.

For the implementation of AHP, this study conducted an AHP survey with state and local disaster managers in post-Katrina Louisiana, the USA.

The AHP analysis disclosed a preference gap between local and state managers. It also identified frequent interaction with partners, leadership and communication, as the most critical capabilities to develop for effective emergency management.

This study discussed the value of consistent and careful management of the collaborative relationship. This study is context-specific in disaster type (Hurricane) and locality (Louisiana). Other AHP studies or similar multi-criteria decision-making models should be implemented in different contexts.

Even with clear advantages of collective decision-making in the emergency management field, a model for collective decision-making has been rare. This study explored the applicability of AHP to the collective decision-making for the efficient and effective allocation of limited financial resources.

In modern urban governance, rescue materials storage points (RMSP) are a vital role to be considered in responding to public emergencies and improving a city's emergency management. This study analyzes the siting of community-centered relief supply facilities.

Combining grey relational analysis, complex network and relative entropy, a new multi criteria method is proposed. It pays more attention to the needs of the community, taking into account the use of community hospitals, fire centers and neighborhood offices to establish small RMSP.

The research results firstly found suitable areas for RMSP site selection, including Hanyang, Qiaokou, Jiangan and Wuchang. The top 10 nodes in each region are found as the location of emergency facilities, and the network parameters are higher than ordinary nodes in traffic networks. The proposed method was applied in Wuhan, China and the method was verified by us-ing a complex network model combined with multi-criteria decision-making for emergency facility location.

This method solves the problem of how to choose the optimal solution and reduces the difficulty for decision makers. This method will help emergency managers to locate and plan RMSP more simply, especially in improving emergency siting modeling techniques and additionally in providing a reference for future research.

The method proposed in this study is beneficial to improve the decision-making ability of urban emergency departments. Using complex networks and comprehensive evaluation techniques, RMSP is incorporated into the urban community emergency network as a critical rescue force. More importantly, the findings highlight a new direction for further research on urban emergency facilities site selection based on a combination of sound theoretical basis as well as empirical evidence gained from real life case-based analysis.

1. Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

2. Build emergency site selection facilities centered on urban communities.

3. Use a complex network model to select the location of emergency supplies storage sites.

Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

Build emergency site selection facilities centered on urban communities.

Use a complex network model to select the location of emergency supplies storage sites.

Grey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.

Firstly, the bull's eye matrix of the scheme stage is derived based on the principle of group negotiation and maximum satisfaction deviation. Secondly, a nonlinear programming model for stage weight is constructed by using an improved Orness measure constraint to determine the stage weight. Finally, the quantum-like Bayesian network is constructed to explore the interference effect between stages. In this process, the decision of each stage is regarded as a wave function which occurs synchronously, with mutual interference impacting the aggregate result. Finally, the effectiveness and rationality of the model are verified through a public health emergency.

The research shows that there are interference effects between each stage. Both the dynamic grey target group decision model and the dynamic multi-attribute group decision model based on quantum-like Bayesian network proposed in this paper are scientific and effective. They enhance the flexibility and stability of actual decision-making and provide significant practical value.

To address issues like stage interference effects, subjective bull's eye settings and the absence of participative behavior in decision-making groups, this paper develops a grey target decision model grounded in group negotiation and maximum satisfaction deviation. Furthermore, by integrating the quantum-like Bayesian network model, this paper offers a novel perspective for addressing information fusion and subjective cognitive biases during decision-making.

There has been little research about incident management decision making within real-life, dynamic emergencies such as urban fire settings. So this research addresses the research problem: how do incident managers make decisions in urban fire settings? These decision behaviours cover five areas: assessment of the fireground situation, selection of a decision strategy, determination of incident objectives, deployment and management of firefighting resources and ongoing review of the incident. The paper aims to discuss these issues.

Case research was used to examine management of different types of fires, through in-depth interviews with a range of incident managers.

This research identified five key behavioural elements associated with incident management in urban fire settings such as their application of a mix of recognition-primed, value based, procedural and formal decision strategies throughout the course of an incident rather than a single style.

The in-depth framework of decision making could provide foundations for later research about other emergency settings. And this research is limited to analytic generalisation (Yin, 2009); so quantitative research such as surveys and large scale interviews could be done to further extend the research for statistical generalisation.

The decision procedures uncovered in this research will assist incident managers in many emergencies, assist policy making and foster the development of future incident managers.

The findings expand the knowledge of how incident managers develop situation awareness, make decisions and plans, implement them, and review the incident as it evolves. Another contribution is the comprehensive framework of decision making developed from these findings.