The effects of community attachment and information seeking on displaced disaster victims’ decision making

This paper uses original survey data of the Great East Japan earthquake disaster victims to examine their decision to apply for the temporary housing as well as the timing of application. We assess the effects of victims’ attachment to their locality as well as variation in victims’ information seeking behavior. We additionally consider various factors such as income, age, employment and family structure that are generally considered to affect the decision to choose temporary housing as victims’ solution for their displacement. Empirical results indicate that, ceteris paribus, as the degree of attachment increases, victims are more likely to apply for the temporary housing but attachment does not affect the timing of application. On the other hand, the victims who actively seek information and are able to collect higher quality information are less likely to apply for the temporary housing and if they do apply then they apply relatively later.

Communicative and Anticipatory Decision-making Supported by Bayesian Networks

The purpose of this research is to draw up a clear construction of an anticipatory communicative decision-making process and a successful implementation of a Bayesian application that can be used as an anticipatory communicative decision-making support system. This study is a decision-oriented and constructive research project, and it includes examples of simulated situations. As a basis for further methodological discussion about different approaches to management research, in this research, a decision-oriented approach is used, which is based on mathematics and logic, and it is intended to develop problem solving methods. The approach is theoretical and characteristic of normative management science research. Also, the approach of this study is constructive. An essential part of the constructive approach is to tie the problem to its solution with theoretical knowledge. Firstly, the basic definitions and behaviours of an anticipatory management and managerial communication are provided. These descriptions include discussions of the research environment and formed management processes. These issues define and explain the background to further research. Secondly, it is processed to managerial communication and anticipatory decision-making based on preparation, problem solution, and solution search, which are also related to risk management analysis. After that, a solution to the decision-making support application is formed, using four different Bayesian methods, as follows: the Bayesian network, the influence diagram, the qualitative probabilistic network, and the time critical dynamic network. The purpose of the discussion is not to discuss different theories but to explain the theories which are being implemented. Finally, an application of Bayesian networks to the research problem is presented. The usefulness of the prepared model in examining a problem and the represented results of research is shown. The theoretical contribution includes definitions and a model of anticipatory decision-making. The main theoretical contribution of this study has been to develop a process for anticipatory decision-making that includes management with communication, problem-solving, and the improvement of knowledge. The practical contribution includes a Bayesian Decision Support Model, which is based on Bayesian influenced diagrams. The main contributions of this research are two developed processes, one for anticipatory decision-making, and the other to produce a model of a Bayesian network for anticipatory decision-making. In summary, this research contributes to decision-making support by being one of the few publicly available academic descriptions of the anticipatory decision support system, by representing a Bayesian model that is grounded on firm theoretical discussion, by publishing algorithms suitable for decision-making support, and by defining the idea of anticipatory decision-making for a parallel version. Finally, according to the results of research, an analysis of anticipatory management for planned decision-making is presented, which is based on observation of environment, analysis of weak signals, and alternatives to creative problem solving and communication.

A Geometric Algorithm for Learning Oblique Decision Trees

In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy, based on some recent variants of SVM, to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. We show through empirical studies that our method is effective.

Integration or disintegration? Human resource implications of a common corporate language decision in a crossborder merger

The primary purpose of introducing a common corporate language in crossborder mergers is to integrate two previously separate organizations and facilitate communication. However, the present case study of a cross-border merger between two Nordic banks shows that the common corporate language decision may have disintegrating effects, particularly at organizational levels below top management. We identify such effects on performance appraisal, language training and management development, career paths, promotion and key personnel. Our findings show that top management needs to work through the consequences of the language decision upon those who are expected to make such a decision work.

A reinforcement learning based algorithm for finite horizon Markov decision processes

We develop a simulation based algorithm for finite horizon Markov decision processes with finite state and finite action space. Illustrative numerical experiments with the proposed algorithm are shown for problems in flow control of communication networks and capacity switching in semiconductor fabrication.

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

Theoretical foundations for multiple rendezvous of glowworm-inspired mobile agents with variable local-decision domains

We present the theoretical foundations for the multiple rendezvous problem involving design of local control strategies that enable groups of visibility-limited mobile agents to split into subgroups, exhibit simultaneous taxis behavior towards, and eventually rendezvous at, multiple unknown locations of interest. The theoretical results are proved under certain restricted set of assumptions. The algorithm used to solve the above problem is based on a glowworm swarm optimization (GSO) technique, developed earlier, that finds multiple optima of multimodal objective functions. The significant difference between our work and most earlier approaches to agreement problems is the use of a virtual local-decision domain by the agents in order to compute their movements. The range of the virtual domain is adaptive in nature and is bounded above by the maximum sensor/visibility range of the agent. We introduce a new decision domain update rule that enhances the rate of convergence by a factor of approximately two. We use some illustrative simulations to support the algorithmic correctness and theoretical findings of the paper.

The Value of Being on the Net- the effects of Web sites of companies on consumer decision making

The unique characteristics of marketspace in combination with the fast growing number of consumers interested in e-commerce have created new research areas of interest to both marketing and consumer behaviour researchers. Consumer behaviour researchers interested in the decision making processes of consumers have two new sets of questions to answer. The first set of questions is related to how useful theories developed for a marketplace are in a marketspace context. Cyber auctions, Internet communities and the possibilities for consumers to establish dialogues not only with companies but also with other consumers make marketspace unique. The effects of these distinctive characteristics on the behaviour of consumers have not been systematically analysed and therefore constitute the second set of questions which have to be studied. Most companies feel that they have to be online even though the effects of being on the Net are not unambiguously positive. The relevance of the relationship marketing paradigm in a marketspace context have to be studied. The relationship enhancement effects of websites from the customers’ point of view are therefore emphasized in this research paper. Representatives of the Net-generation were analysed and the results show that companies should develop marketspace strategies while Net presence has a value-added effect on consumers. The results indicate that the decision making processes of the consumers are also changing as a result of the progress of marketspace

New Governance and the EU Courts : The Experimentalist Architecture of Judicial Decision-Making

The dissertation examines the role of the EU courts in new governance. New governance has raised unprecedented interest in the EU in recent years. This is manifested in a plethora of instruments and actors at various levels that challenge more traditional forms of command-and-control regulation. New governance and political experimentation more generally is thought to sap the ability of the EU judiciary to monitor and review these experiments. The exclusion of the courts is then seen to add to the legitimacy problem of new governance. The starting point of this dissertation is the observation that the marginalised role of the courts is based on theoretical and empirical assumptions which invite scrutiny. The theoretical framework of the dissertation is deliberative democracy and democratic experimentalism. The analysis of deliberative democracy is sustained by an attempt to apply theoretical concepts to three distinctive examples of governance in the EU. These are the EU Sustainable Development Strategy, the European Chemicals Agency, and the Common Implementation Strategy for the Water Framework Directive. The case studies show numerous disincentives and barriers to judicial review. Among these are questions of the role of courts in shaping governance frameworks, the reviewability of science-based measures, the standing of individuals before the courts, and the justiciability of soft law. The dissertation analyses the conditions of judicial review in each governance environment and proposes improvements. From a more theoretical standpoint it could be said that each case study presents a governance regime which builds on legislation that lays out major (guide)lines but leaves details to be filled out at a later stage. Specification of detailed standards takes place through collaborative networks comprising members from national administrations, NGOs, and the Commission. Viewed this way, deliberative problem-solving is needed to bring people together to clarify, elaborate, and revise largely abstract and general norms in order to resolve concrete and specific problems and to make law applicable and enforceable. The dissertation draws attention to the potential of peer review included there and its profound consequences for judicial accountability structures. It is argued that without this kind of ongoing and dynamic peer review of accountability in governance frameworks, judicial review of new governance is difficult and in some cases impossible. This claim has implications for how we understand the concept of soft law, the role of the courts, participation rights, and the legitimacy of governance measures more generally. The experimentalist architecture of judicial decision-making relies upon a wide variety of actors to provide conditions for legitimate and efficient review.

Information and Decision Technologies

Production scheduling in a flexible manufacturing system (FMS) is a real-time combinatorial optimization problem that has been proved to be NP-complete. Solving this problem needs on-line monitoring of plan execution and requires real-time decision-making in selecting alternative routings, assigning required resources, and rescheduling when failures occur in the system. Expert systems provide a natural framework for solving this kind of NP-complete problems.In this paper an expert system with a novel parallel heuristic approach is implemented for automatic short-term dynamic scheduling of FMS. The principal features of the expert system presented in this paper include easy rescheduling, on-line plan execution, load balancing, an on-line garbage collection process, and the use of advanced knowledge representational schemes. Its effectiveness is demonstrated with two examples.

Speedup Macroblock Mode Decision in H.264/SVC Encoding Using Cost-Sensitive Learning

In this paper we present a novel macroblock mode decision algorithm to speedup H.264/SVC Intra frame encoding. We replace the complex mode-decision calculations by a classifier which has been trained specifically to minimize the reduction in RD performance. This results in a significant speedup in encoding. The results show that machine learning has a great potential and can reduce the complexity substantially with negligible impact on quality. The results show that the proposed method reduces encoding time to about 70% in base layer and up to 50% in enhancement layer of the reference implementation with a negligible loss in quality.

An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes

We develop in this article the first actor-critic reinforcement learning algorithm with function approximation for a problem of control under multiple inequality constraints. We consider the infinite horizon discounted cost framework in which both the objective and the constraint functions are suitable expected policy-dependent discounted sums of certain sample path functions. We apply the Lagrange multiplier method to handle the inequality constraints. Our algorithm makes use of multi-timescale stochastic approximation and incorporates a temporal difference (TD) critic and an actor that makes a gradient search in the space of policy parameters using efficient simultaneous perturbation stochastic approximation (SPSA) gradient estimates. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal policy. (C) 2010 Elsevier B.V. All rights reserved.