In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Decision Theory: An Introduction to the Mathematics of Rationality.

A prospect theory-based interval dynamic reference point method is proposed.TOPSIS method with interval reference point is also proposed.Decision maker's psychological behavior is discussed under emergency.Dynamic adjustment of alternatives and reference points are discussed. Urgent or critical situations, such as terrorist attacks and natural disasters, often require decision makers (DMs) to take crucial decisions. Emergency decision making (EDM) problems have become a very active research field in recent years. The existing studies focus mainly on the information inadequacy or incomplete information in emergencies, and selecting ideal emergency alternatives, neglecting the psychological behavior of DMs under emergencies. Few studies consider a DM's psychological behavior, although there is some focus on the dynamic features of emergency events and limited DM judgments under risk and uncertainty. Motivated by such problems, this study proposes a prospect theory-based interval dynamic reference point method for EDM. The technique for order preference by similarity to ideal solution (TOPSIS) method is a popular decision technique, as decision makers are always bounded rational under risk and uncertainty and their psychological behavior plays an important role in EDM. However, the existing TOPSIS methods are seldom concerned with this issue. Based on such a problem, this study proposes a TOPSIS method with an interval reference point, which considers the DM's psychological behavior. Two examples are presented to illustrate the feasibility and validity of the proposed methods for solving EDM problems in the real world. Based on the final rankings of alternatives in the examples, each of our methods validates the other and matches the actual EDM.

A prospect theory-based interval dynamic reference point method for emergency decision making

ervice is broadly considered as an application of specialized knowledge, skill, and experience, performed for co-creation of respective values of both consumer and provider. Services are engineered and delivered through a heterogeneous service system. Compared to physical goods in manufacturing, resources, largely people (end users as the service consumer and employees as the service provider) - the main focus of a service system, cannot be held and are more complex to model and manage as people participating in service production and consumption have physiological and psychological issues, cognitive capability, and sociological constraints, etc. As the world becomes more complex and uncertain socially and economically, this research proposes a computational thinking approach to modeling of the dynamics and adaptiveness of a service system, aimed at fully leveraging today’s ubiquitous digitalized information, computing capability and computational power so that the service system can be studied qualitatively and quantitatively. Ultimately, with this foundation we will successively and successfully develop the following mechanisms to implement and enhance service systems: • A mechanism to timely capture end users’ requirements, changes, expectation and satisfaction in a variety of technical, social, and cultural aspects; • A mechanism to efficiently and cost-effectively provide employees right means and assistances to engineer services while promptly responding the changes; • A mechanism to allow involved people consciously infuse as much intelligence as possible into all levels and aspects of decision-making to assure necessary system adaptiveness for smarter operations.

https://pubsonline.informs.org/doi/pdf/10.1287/serv.1.1.42

Computational Thinking of Service Systems: Dynamics and Adaptiveness Modeling

This paper examines and extends the theory of information systems success in the context of large-scale disaster management (DM) for public safety. In the recent past, various evaluation reports on DM efforts have concluded that information quality and system quality are major hurdles for efficient and effective multi-agency DM and are critical antecedents for information systems (IS) success. In contrast to the wealth of literature on IS success in profit-oriented business environments, research regarding drivers of public sector IS success is scarce. This research develops and empirically tests a model that explains IS usage intention as a reflective measure of IS success in the public sector DM domain. In this paper, the effects of the expected value of IS for the entire group of collaborating DM agencies, task support, user satisfaction, and three specific information/service quality dimensions on usage intention are examined. Data was collected from emergency responders using a questionnaire survey method during multi-agency, cross-national DM exercises at the Dutch-German border. The results of the data analysis revealed that expected group value is a key determinant of intention to use in the public sector DM domain. The data analysis also showed that perceived task support only has an indirect effect, through user satisfaction, on the usage intention. These findings imply that previously suggested IS success models for business environments are likely to fall short in their explanatory power and applicability for highly volatile complex disaster environments that require immediate coordinated responses from a large number of organizations. Possible directions for future research are also discussed along with other findings and implications.

Group value and intention to use - A study of multi-agency disaster management information systems for public safety

Editorial: Towards service science, engineering and practice

Review on group decision support systems (GDSS) indicates that traditional GDSS are not specifically designed to support mission-critical group decision-making tasks that require group decision-making to be made effectively within short time. In addition, prior studies in the research literature have not considered group decision preference adjustment as a continuous process and neglected its impact on group decision-making. In reality, group members may dynamically change their decision preferences during group decision-making process. This dynamic adjustment of decision preferences may continue until a group reaches consensus on final decision. This article intends to address this neglected group decisionmaking research issue in the literature by proposing a new approach based on the Markov chain model. Furthermore, a new group decision weight allocation approach is also suggested. A real case example of New Orleans Hurricane Katrina is used to illustrate the usefulness and effectiveness of the proposed approaches. Finally, the article concludes with the discussion on the proposed approaches and presents directions for future research.

Mission-Critical Group Decision-Making: Solving the Problem of Decision Preference Change in Group Decision- Making Using Markov Chain Model

According to the characters of emergency decision-making in crisis management, this paper proposes a special decision-making method to deal with the inadequate information, uncertainty and dynamical trend. This CBR-based decision support method retrieves similar cases from Case Base and forecasts the prior distribution of absent feature values using Bayesian Dynamic Forecasting Model. Then the result is put into Markov-based state transition matrix to order suggested solutions by suitability and assist consensus achieving among decision makers. This novel method is suitable to emergency decision making as it provides support for the dynamic and evolutionary character of emergency response.

A case-based evolutionary group decision support method for emergency response

What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics

To support the group decision-making in emergency response, it is important to research on how to reach consensus quickly and reliably in the procedure of group decision-making. This paper proposes a new planning method to improve the efficiency of group decision-making based on Markov chain model and uses a real case example of New Orleans Hurricane Katrina to illustrate the usefulness and effectiveness of the proposed approach. The approach is quicker than conventional process in terms of two points. (1) It minimizes the human interventions and makes the most of computer system support to speed up the interactions between the decision-maker and the system. (2) It researches into the important hidden pattern in the group decision-making process, predicts the possible decision in the future and thus further speeds up the decision-making process. The authors hope that analysis of the dynamic procedure of decision-maker's preference adjustment will assist researchers not only in better understanding of group decision-making procedure but also in better design of Group Decision Support Systems for emergency response.

Huizhang Shen

Papers

Editorial: Towards service science, engineering and practice

Mission-Critical Group Decision-Making: Solving the Problem of Decision Preference Change in Group Decision- Making Using Markov Chain Model

A case-based evolutionary group decision support method for emergency response

What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics

A Quick Group Decision-making Planning Method for Emergency Response