Decision making study: methods and applications of evidential reasoning and judgment analysis

Decision making study has been the multi-disciplinary research involving operations researchers, management scientists, statisticians, mathematical psychologists and economists as well as others. This study aims to investigate the theory and methodology of decision making research and apply them to different contexts in real cases. The study has reviewed the literature of Multiple Criteria Decision Making (MCDM), Evidential Reasoning (ER) approach, Naturalistic Decision Making (NDM) movement, Social Judgment Theory (SJT), and Adaptive Toolbox (AT) program. On the basis of these literatures, two methods, Evidence-based Trade-Off (EBTO) and Judgment Analysis with Heuristic Modelling (JA-HM), have been proposed and developed to accomplish decision making problems under different conditions. In the EBTO method, we propose a novel framework to aid people s decision making under uncertainty and imprecise goal. Under the framework, the imprecise goal is objectively modelled through an analytical structure, and is independent of the task requirement; the task requirement is specified by the trade-off strategy among criteria of the analytical structure through an importance weighting process, and is subject to the requirement change of a particular decision making task; the evidence available, that could contribute to the evaluation of general performance of the decision alternatives, are formulated with belief structures which are capable of capturing various format of uncertainties that arise from the absence of data, incomplete information and subjective judgments. The EBTO method was further applied in a case study of Soldier system decision making. The application has demonstrated that EBTO, as a tool, is able to provide a holistic analysis regarding the requirements of Soldier missions, the physical conditions of Soldiers, and the capability of their equipment and weapon systems, which is critical in domain. By drawing the cross-disciplinary literature from NDM and AT, the JA-HM extended the traditional Judgment Analysis (JA) method, through a number of novel methodological procedures, to account for the unique features of decision making tasks under extreme time pressure and dynamic shifting situations. These novel methodological procedures include, the notion of decision point to deconstruct the dynamic shifting situations in a way that decision problem could be identified and formulated; the classification of routine and non-routine problems, and associated data alignment process to enable meaningful decision data analysis across different decision makers (DMs); the notion of composite cue to account for the DMs iterative process of information perception and comprehension in dynamic task environment; the application of computational models of heuristics to account for the time constraints and process dynamics of DMs decision making process; and the application of cross-validation process to enable the methodological principle of competitive testing of decision models. The JA-HM was further applied in a case study of fire emergency decision making. The application has been the first behavioural test of the validity of the computational models of heuristics, in predicting the DMs decision making during fire emergency response. It has also been the first behavioural test of the validity of the non-compensatory heuristics in predicting the DMs decisions on ranking task. The findings identified extend the literature of AT and NDM, and have implications for the fire emergency decision making

A Comparison of Emergency Management Social Media Use in the United States and England

A survey was distributed to U.S. county-level emergency managers in 2014 which included questions exploring the importance of barriers to use of Social Media (SM) for dissemination and collection of information during disasters. Key questions were replicated in a survey of emergency responders in England in 2015-2016. There are many similarities in the perceived importance of various specific barriers, but also many significant differences in results. For example, in both samples, trustworthiness of data and information overload are among the top barriers to collecting SM data. However, agencies in England are more likely to have official policies prohibiting the use of SM (58% in England vs. 25% in the U.S.). The differences suggest that software enhancements to deal with the technical problems of trustworthiness and information overload may be universally useful, but other barriers to use need to be addressed through organizational and policy measures

Soldier system assessment under uncertainty with evidential reasoning

Along with the increasing of new equipment based capabilities, the physiological burden on the dismounted soldier keeps on growing, which leads to the limitation in the quantity and types of missions that can be carried out. In this research, a methodology is developed to solve the burden problem from the system assessment point of view. Comparing with other relevant research, the new methodology not only provides quantitative performance estimate of the soldier with the capability of handling fragmentary and incomplete data with hybrid format in nature (qualitative and quantitative), but also restrains the assessment complexity to an acceptable level

Household water consumption: Insight from a survey in Greece and Poland

© 2015 The Authors. Published by Elsevier Ltd. Determining the behavior of domestic water consumers can facilitate a more proactive approach to water demand management, and serves as the foundation for the development of any intervention strategies that seek to bring about sustained and substantial reductions in domestic water consumption. As part of the European Union (EU) funded project Integrated Support System for Efficient Water Usage and Resources Management (ISS-EWATUS), a household water consumption survey was administered to address the question of how water was used within the home in the EU. The survey was distributed by the University of Thessaly in Greece, and the Institute for Ecology of Industrial Areas in Poland. This paper represents the research output of the survey, including the analysis of three major elements pertinent to the behavior of domestic water consumers: end use behaviors; socio-demographic and property characteristics; and psychosocial constructs such as attitudes and beliefs

Learning the Network of Graphs for Graph Neural Networks

Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured data. However, in many real applications, there are three issues when applying GNNs: graphs are unknown, nodes have noisy features, and graphs contain noisy connections. Aiming at solving these problems, we propose a new graph neural network named as GL-GNN. Our model includes multiple sub-modules, each sub-module selects important data features and learn the corresponding key relation graph of data samples when graphs are unknown. GL-GNN further obtains the network of graphs by learning the network of sub-modules. The learned graphs are further fused using an aggregation method over the network of graphs. Our model solves the first issue by simultaneously learning multiple relation graphs of data samples as well as a relation network of graphs, and solves the second and the third issue by selecting important data features as well as important data sample relations. We compare our method with 14 baseline methods on seven datasets when the graph is unknown and 11 baseline methods on two datasets when the graph is known. The results show that our method achieves better accuracies than the baseline methods and is capable of selecting important features and graph edges from the dataset. Our code will be publicly available at \url{https://github.com/Looomo/GL-GNN}

Incorporating persuasion into a decision support system: The case of the water user classification function

Utility stakeholders often view autonomic feedback systems as valuable tools for moderating consumption of household resources (e.g. electricity). However, to be successful, such technology must be not only informative but also persuasive. This paper presents the water user classification (WUC) function of a decision support system (DSS) for residential water consumers. This function has been designed to harness personal value systems and wider social norms in order to promote water conservation. It uses data on home appliance efficiency, routine water usage and environmental values to attribute DSS users with a water user identity. Where the attributed identity is at odds with a self-defined 'green' identity, users may be prompted to reevaluate their everyday practices. The function also offers 'smart' personalized water saving advice. In these ways, it aims to encourage consumers to adopt sustainable water saving behaviors. This paper describes the design of the WUC function and its contribution to the DSS. It additionally highlights the crucial role of behavior change theory in the delivery of successful technology-based interventions

Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval

Grounded on pre-trained language models (PLMs), dense retrieval has been studied extensively on plain text. In contrast, there has been little research on retrieving data with multiple aspects using dense models. In the scenarios such as product search, the aspect information plays an essential role in relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A common way of leveraging aspect information for multi-aspect retrieval is to introduce an auxiliary classification objective, i.e., using item contents to predict the annotated value IDs of item aspects. However, by learning the value embeddings from scratch, this approach may not capture the various semantic similarities between the values sufficiently. To address this limitation, we leverage the aspect information as text strings rather than class IDs during pre-training so that their semantic similarities can be naturally captured in the PLMs. To facilitate effective retrieval with the aspect strings, we propose mutual prediction objectives between the text of the item aspect and content. In this way, our model makes more sufficient use of aspect information than conducting undifferentiated masked language modeling (MLM) on the concatenated text of aspects and content. Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings. Code and related dataset will be available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.Comment: accepted by cikm202

Fast and frugal heuristics and naturalistic decision making: a review of their commonalities and differences

© 2016 Taylor & Francis Both the fast and frugal heuristics (FFHs) and the naturalistic decision making (NDM) research programmes have identified important areas of inquiry previously neglected in the traditional study of human judgment and decision making, and have greatly contributed to the understanding of people's real-world decision making under environmental constraints. The two programmes share similar theoretical arguments regarding the rationality, optimality, and role of experience in decision making. Their commonalities have made them appealing to each other, and efforts have been made, by their leading academics, to promote synergy and integration. However, there has been little progress towards this during the last decade. This paper seeks to address this gap by seeking to better understand their commonalities and differences. To do so, literature relating to the two programmes is reviewed. The findings of the review indicated that an integration of the two could enhance FFHs' field research in applied settings, facilitate its investigation on boundary conditions of people's decision strategy selection, enable NDM to embrace emerging research opportunities in the age of big data, as well as permit each programme to enlighten the research topics and to validate the research findings of the other

High efficiency and stability of ink-jet printed quantum dot light emitting diodes

The low efficiency and fast degradation of devices from ink-jet printing process hinders the application of quantum dot light emitting diodes on next generation displays. Passivating the trap states caused by both anion and cation under-coordinated sites on the quantum dot surface with proper ligands for ink-jet printing processing reminds a problem. Here we show, by adapting the idea of dual ionic passivation of quantum dots, ink-jet printed quantum dot light emitting diodes with an external quantum efficiency over 16% and half lifetime of more than 1,721,000hours were reported for the first time. The liquid phase exchange of ligands fulfills the requirements of ink-jet printing processing for possible mass production. And the performance from ink-jet printed quantum dot light emitting diodes truly opens the gate of quantum dot light emitting diode application for industry. Designing efficient and scalable quantum dot LEDs meeting industrial requirements remains a challenge. Here, the authors, by leveraging the liquid phase exchange of d-MX2 ligands, present printed quantum dot LEDs with external quantum efficiency over 16% and half lifetime of more than 1,721,000hours