Information In Decision-making Process Research Articles

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

Pythagorean probabilistic hesitant triangular fuzzy aggregation operators with applications in multiple attribute decision making

As a generalization of fuzzy set, hesitant probabilistic fuzzy set and pythagorean triangular fuzzy set have their own unique advantages in describing decision information. As modern socioeconomic decision-making problems are becoming more and more complex, it also becomes more and more difficult to appropriately depict decision makers' cognitive information in decision-making process. In order to describe the decision information more comprehensively, we define a pythagorean probabilistic hesitant triangular fuzzy set (PPHTFS) by combining the pythagorean triangular fuzzy set and the probabilistic hesitant fuzzy set. Firstly, the basic operation and scoring function of the pythagorean probabilistic hesitant triangular fuzzy element (PPHTFE) are proposed, and the comparison rule of two PPHTFEs is given. Then, some pythagorean probabilistic hesitant triangular fuzzy aggregation operators are developed, and their properties are also studied. Finally, a multi-attribute decision-making (MADM) model is constructed based on the proposed operators under the pythagorean probabilistic hesitant triangular fuzzy information, and an illustration example is given to demonstrate the practicability and validity of the proposed decision-making method.

Evidential Markov decision-making model based on belief entropy to predict interference effects

Some cognitive and decision making experiments have demonstrated the classical decision theory may be violated. Recently, the interference effects of quantum theory have attracted a strong interest in applying some fields outside physics. It can be also used to explain the paradox in decision models. Existing some experiments and studies attribute the main reason for the existence of interference effects to uncertain information in decision process. Dempster-Shafer evidence theory extends the framework of discernment to power sets so it can describe unknown and imprecise information. This paper proposes evidential Markov decision-making model based on belief entropy to quantitatively predict and determine the value of interference effects. In new model, the frame of discernment is extended by introducing hesitant or unknown states which could be hidden by participants. Moreover, new model assumes there is no input of any information at initial states so it has the most chaotic states and is determined according to the maximum belief entropy. Finally, this paper discusses the effectiveness of new model by comparing with other methods as studying the interference effects of decision process.

Meta-path guided graph attention network for explainable herb recommendation.

Traditional Chinese Medicine (TCM) has been widely adopted in clinical practice by Eastern Asia people for thousands of years. Nowadays, TCM still plays a critical role in Chinese society and receives increasing attention worldwide. The existing herb recommenders learn the complex relations between symptoms and herbs by mining the TCM prescriptions. Given a set of symptoms, they will provide a set of herbs and explanations from the TCM theory. However, the foundation of TCM is Yinyangism (i.e. the combination of Five Phases theory with Yin-yang theory), which is very different from modern medicine philosophy. Only recommending herbs from the TCM theory aspect largely prevents TCM from modern medical treatment. As TCM and modern medicine share a common view at the molecular level, it is necessary to integrate the ancient practice of TCM and standards of modern medicine. In this paper, we explore the underlying action mechanisms of herbs from both TCM and modern medicine, and propose a Meta-path guided Graph Attention Network (MGAT) to provide the explainable herb recommendations. Technically, to translate TCM from an experience-based medicine to an evidence-based medicine system, we incorporate the pharmacology knowledge of modern Chinese medicine with the TCM knowledge. We design a meta-path guided information propagation scheme based on the extended knowledge graph, which combines information propagation and decision process. This scheme adopts meta-paths (predefined relation sequences) to guide neighbor selection in the propagation process. Furthermore, the attention mechanism is utilized in aggregation to help distinguish the salience of different paths connecting a symptom with a herb. In this way, our model can distill the long-range semantics along meta-paths and generate fine-grained explanations. We conduct extensive experiments on a public TCM dataset, demonstrating comparable performance to the state-of-the-art herb recommendation models and the strong explainability.

Neutrosophic QUALIFLEX based on neutrosophic hesitancy index for selecting a potential antivirus mask supplier over COVID-19 pandemic.

In order to handle simultaneously the cardinal and ordinal information in decision-making process, QUALIFLEX (QUALItative FLEXible multiple criteria method) is a very well-known decision-making approach. In this work, we extend the classical QUALIFLEX method to neutrosophic environment and develop a neutrosophic QUALIFLEX (N-QUALIFLEX) method that uses the newly defined distance-based comparison approach. It is highly effective in solving multi-criteria decision problems in which both ratings of alternatives on criteria and weights of criteria are single-valued neutrosophic numbers (SVNNs), and their aggregated values are single-valued neutrosophic hesitant fuzzy numbers (SVNHFNs). A neutrosophic hesitancy index (NHI) of a SVNHN is introduced based on degrees of the truth-membership, indeterminacy-membership and falsity-membership, which is used to measure the degree of hesitancy of SVNHN. Considering the NHIS of SVNHFNs, we propose a distance-based comparison approach to determine the magnitude of the SVNHFNs. Then, we apply the comparison approach to define the concordance/discordance index, the weighted concordance/discordance index and the comprehensive concordance/discordance index that are steps of the developed N-QUALIFLEX. By taking all possible permutations of alternatives with respect to the level of concordance/discordance into account, we determine the order of alternatives in final decision. Finally, a practical example on antivirus mask selection over the COVID-19 pandemic is provided to present the effectiveness and applicability of the proposed method, and a comparative study is conducted to show the advantages of the proposed method over other existing methods.

The Two Blades of the Scissors: Performance Feedback and Intrinsic Attributes in Organizational Risk Taking

We draw on the behavioral theory of the firm and prospect theory to examine how performance feedback (decision context) and the characteristics of the alternatives (decision content) that decision makers face jointly determine organizational risk-taking choices. While the behavioral theory of the firm has identified performance feedback’s important role in driving organizational risk-taking decisions, it has not considered the intrinsic attributes of alternatives, specifically the magnitude and likelihood of their outcomes, which have been the focus of prospect theory. We argue that these two attributes play a key role in decision makers’ assessment of alternatives, but because achieving organizational goals is the prime objective in organizations, performance feedback drives how decision makers process information regarding these attributes. Analyzing 23,895 fourth-down decisions from the U.S. National Football League, we find that decision makers weigh attainment discrepancy and the magnitude and likelihood of outcomes in their choices, depending on deadline proximity. Furthermore, the size and valence of attainment discrepancy modify the weight of the magnitude and likelihood of outcomes in risky choices. Our arguments and findings suggest extensions to the behavioral theory of the firm and imply modifications to prospect theory when applied to the organizational context.

Probabilistic analysis of long-term loss incorporating maximum entropy method and analytical higher-order moments

Quantifying economic losses of civil infrastructures subjected to various hazards under a life-cycle context is of vital importance for risk assessment and management. In previous studies, the expected long-term loss has been widely applied as a standard decision criterion during the life-cycle analysis. However, the expectation may not be informative enough to illustrate uncertainties associated with the long-term loss. Therefore, the higher-order moments and the probability distribution should be investigated. In this paper, a probabilistic analysis framework is proposed to construct the probability density function and cumulative distribution function of long-term loss by assessing the analytical statistical moments. The stochastic renewal process is utilized to assess the long-term loss by considering uncertainties associated with stochastic occurrence and frequency of the hazards. Based on the maximum entropy method, the proposed approach shows superior efficiency to assess the probability distribution of long-term loss than crude Monte Carlo simulation. The probability distribution can be essential information for decision-making process of risk management. An illustrative example is investigated to show the probability density function of long-term loss of civil infrastructure subjected to hurricane hazards. A good agreement of results obtained by the proposed approach and Monte Carlo simulation has verified the accuracy and effectiveness of the proposed method.

Identifying Minimum Information Requirements to Improve Integrated Modeling Capabilities: Lessons Learned From Dynamic Adaptive Policy Pathways

Integrated Assessment Models (IAMs) were initially developed to inform decision processes relating to climate change and then extended to other natural resource management decisions, including issues around integrated water resources management. Despite their intention to support long-term planning decisions, model uptake has generally been limited, partly due to their unfulfilled capability to manage deep uncertainty issues and consider multiple perspectives and trade-offs involved when solving problems of interest. In recent years, more emphasis has been put on the need for existing models to evolve to be used for exploratory modeling and analysis to capture and manage deep uncertainty. Building new models is a solution but may face challenges in terms of feasibility and the conservation of knowledge assets. Integration and augmentation of existing models is another solution, but little guidance exists on how to realize model augmentation that addresses deep uncertainty and how to use such models for exploratory modeling purposes. To provide guidance on how to augment existing models to support decisions under deep uncertainty we present an approach for identifying minimum information requirements (MIRs) that consists of three steps: (1) invoking a decision support framework [here, Dynamic Adaptive Policy Pathways (DAPP)] to synthesize information requirements, (2) characterizing misalignment with an existing integrated model, (3) designing adjustable solutions that align model output with immediate information needs. We employ the Basin Futures model to set up the approach and illustrate outcomes in terms of its effectiveness to augment models for exploratory purposes, as well as its potential for supporting the design of adaptative pathways. The results are illustrated in the context of the Brahmani River Basin (BRB) system and discussed in terms of generalization and transferability of the approach to identifying MIRs. Future work directions include the refinement and evaluation of the approach in a planning context and testing of the approach with other models.

Need for Haptic Information and Purchase Decision Process in the Case of Online E-commerce Stores

Need for Haptic Information and Purchase Decision Process in the Case of Online E-commerce Stores

Change point detection with multiple alternatives reveals parallel evaluation of the same stream of evidence along distinct timescales

In order to behave appropriately in a rapidly changing world, individuals must be able to detect when changes occur in that environment. However, at any given moment, there are a multitude of potential changes of behavioral significance that could occur. Here we investigate how knowledge about the space of possible changes affects human change point detection. We used a stochastic auditory change point detection task that allowed model-free and model-based characterization of the decision process people employ. We found that subjects can simultaneously apply distinct timescales of evidence evaluation to the same stream of evidence when there are multiple types of changes possible. Informative cues that specified the nature of the change led to improved accuracy for change point detection through mechanisms involving both the timescales of evidence evaluation and adjustments of decision bounds. These results establish three important capacities of information processing for decision making that any proposed neural mechanism of evidence evaluation must be able to support: the ability to simultaneously employ multiple timescales of evidence evaluation, the ability to rapidly adjust those timescales, and the ability to modify the amount of information required to make a decision in the context of flexible timescales.

Urgency, leakage, and the relative nature of information processing in decision-making.

Over the last decade, there has been a robust debate in decision neuroscience and psychology about what mechanism governs the time course of decision-making. Historically, the most prominent hypothesis is that neural architectures accumulate information over time until some threshold is met, the so-called Evidence Accumulation hypothesis. However, most applications of this theory rely on simplifying assumptions, belying a number of potential complexities. Is changing stimulus information perceived and processed in an independent manner or is there a relative component? Does urgency play a role? What about evidence leakage? Although the latter questions have been the subject of recent investigations, most studies to date have been piecemeal in nature, addressing one aspect of the decision process or another. Here we develop a modeling framework, an extension of the Urgency Gating Model, in conjunction with a changing information experimental paradigm to simultaneously probe these aspects of the decision process. Using state-of-the-art Bayesian methods to perform parameter-based inference, we find that (a) information processing is relative with early information influencing the perception of late information, (b) time varying urgency and evidence accumulation are of roughly equal strength in the decision process, and (c) leakage is present with a time scale of ∼200-250 ms. We also show that these effects can only be identified in a changing information paradigm. To our knowledge, this is the first comprehensive study to utilize a changing information paradigm to jointly and quantitatively estimate the temporal dynamics of human decision-making. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.

Uncertainties and nonstationarity in streamflow projections under climate change scenarios and the ensuing adaptation strategies in Subarnarekha river basin, India

<p>The present study analyses the various uncertainties and nonstationarity in the streamflow projections of Subarnarekha river basin in Eastern India using two widely used hydrological climate models: 1) general circulation model (GCM), and 2) forcing climate change scenarios. These two climate models are used to force the ArcSWAT model. Subsequently this model is calibrated using SUFI-2 optimization technique. The downscaled and bias-corrected data from an ensemble of 10 climate projections with representative concentration pathways (RCP) 4.5 and 8.5 scenarios (five each) were used in first model, whereas in second model a total of 63 (7 perturbed precipitations and 9 perturbed temperatures) combinations of hypothetical climate change scenarios were used. The results show very good correlation during monthly calibration time steps and relatively good agreement between the observed and simulated streamflows in daily calibration time steps. The uncertainties are expressed in probabilistic terms using probability density function (PDF) and cumulative distribution function (CDF) as they provide significant information for decision process in climate change adaptation in the river basin. The uncertainties associated with climate models, return periods and streamflow extremes are also analysed in the present work. The RCP 8.5 scenarios seem more appropriate than RCP 4.5 scenarios in quantifying the uncertainties under nonstationarity assumptions. The mean values of water balance components and their percentage variation for both historic and future periods reveal that the water balance components get affected significantly due to climate change in a future period. Consequently, the streamflows are likely to decline in the river basin. The present study also highlights the comprehensive approaches that are being planned to facilitate adaptation to climate change as well as those that are specific to the water resources management in the study region. The findings in this work are useful for overall well-being of people in the study area.</p>

Combining UML Profiles to Design Serious Games Dedicated to Trace Information in Decision Processes

An expert assessment consists of an ordered series of decisions that have to respond to time-evolving information contexts. Improving decisions made in a risk context requires better knowledge of reasoning mechanisms. The authors think that serious games can constitute a rich observatory for reasoning and decisions. However, the design of these games is not trivial and is rarely scalable or reusable. This paper proposes a UML profile library for generically modeling expert reasoning in situations using serious games that involve risks. Two main UML profiles are dedicated to both serious games and gamer decisions traceability modeling. Complementary profiles address risk expert reasoning modeling and data quality modeling. The authors illustrate the approach using the design of a serious game about avalanche risk analysis.

Consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q‐rung orthopair fuzzy set based on correlation measure

The paper proposes a consensus reaching process for fuzzy behavioral TOPSIS method with probabilistic linguistic q-rung orthopair fuzzy sets (PLq-ROFSs) based on correlation measure. First, the operational laws of adjusted PLq-ROFSs based on linguistic scale function (LSF) for semantics of linguistic terms are introduced, where the PLq-ROFSs have same probability space. In addition, we define the score function and accuracy function of PLq-ROFS based on the proposed operational laws to compare the PLq-ROFSs. Furthermore, we propose the probabilistic linguistic q-rung orthopair fuzzy weighted averaging (PLq-ROFWA) operator and the probabilistic linguistic q-rung orthopair fuzzy order weighted averaging (PLq-ROFOWA) operator to aggregate the linguistic decision information. Considering the inconsistency between the individual information and aggregated information in decision-making process and the demiddle of given linguistic sets tocision makers' behavioral factors, we define a new correlation measure based on LSF to develop a consensus reaching process for fuzzy behavioral TOPSIS method with PLq-ROFSs. Finally, a numerical example concerning the selection of optimal green enterprise is given to illustrate the feasibility of the proposed method and some comparative analyses with the existing methods are given to show its effectiveness. The sensitivity analysis and stability analysis of the proposed method on the ranking results are also discussed.

Identifying Non-Traditional Epidemic Disease Risk Factors Associated to Major Health Events from WHO and World Bank Open Data

Objectives: Health events emerge from a multifactorial milieu involving host, community, environment, and pathogen factors. Therefore, developing accurate forecasting models to improve epidemic prediction towards better prevention and capabilities management is a complex task. Here, we describe an exploratory analysis to identify non-health risk factors that could improve the forecast and events risk signals using a feasible and practical approach by combining surveillance report data with non-health data from open data sources. Methods: A line listing was developed using the World Health Organization Disease Outbreaks News from 2016-2018 and merged with non-health indicators data from the World Bank. Poisson regression models employing forward imputations were used to establish relationships and predict values over the dependent variable (health event frequency). Findings: The resulting regression model provided evidence that changes in non-health factors important to community experiences impact the risk of the number of major health events that a country could experience. Three non-health indicators (extrinsic factors) were associated significantly to event frequency (population urban change, gross domestic product change per capita—a novel factor, and average forest area). An exploratory analysis of the current COVID-19 pandemic suggested similar associations, but confounding by global disease burden is likely. Interpretation: Continued development of forecasting approaches capturing available whole-of-society extrinsic factors (non-health factors); could improve the risk management process through earlier hazard identification, and as importantly inform strategic decision processes in multisectoral strategies to preventing, detecting, and responding to pandemic-threat events.Funding: USUHS intramural funding to a Henry M. Jackson Foundation post-doctoral fellowship award.Declaration of Interests: The authors declare no conflicts of interest.Ethics Approval Statement: This manuscript was approved by USUHS clearance publication committee.

A new decision method for service supplier selection based on Choquet integral and generalized evidence theory

Service supplier selection is the most important problem in the process of service outsourcing. Aiming at the fuzziness of supplier evaluation index and subjectivity of decision-making process, a decision method for service supplier selection based on Choquet integral and generalized evidence theory is proposed. First, the fuzziness and uncertainty of information in decision-making process are solved by using interval rough number. The importance degree acquisition method of attribute index is corrected, and the relative weight and absolute weight of attribute index are obtained. Second, considering the interaction between attribute indexes, n-additive Choquet integral model is used to evaluate the service suppliers. According to the characteristics of service suppliers, the decision index system is proposed that is made up of different target variables, and the selection evaluation model of service supplier is established. Then, in order to eliminate the differences and conflicts of the expert evaluation opinions, the generalized evidence theory group decision-making method is introduced, and the expert evaluation opinions are fused so as to obtain the final result. Finally, a case study is provided to verify the validity and feasibility of the proposed method.

The Impact of Producers’ Cognitive Styles on their Purchasing Behavior

This paper inquires on the impact of cognitive styles over the decision-making process producers perform while making input and capital equipment purchases. We will question if Argentine farmers with diverse cognitive styles have different purchasing behaviors when buying the inputs they use in their farming operations. Cognitive styles express differences in the way decision-makers process information and organize knowledge. Results show that different segments of producers have distinctive purchasing behaviors. Specifically, analytic-oriented producers tend to focus more on product performance and less on the relationship with suppliers when buying their inputs. They also tend to be loyal to input brands, they rely less on dealers/retailers and salespeople, and they are willing to change suppliers more often than other producers. Intuitive-oriented producers value more the relationship with the supplier and are interested in contacting the salesperson if they need a product. While balanced-oriented producers declare to be less loyal to brands but are more stable in terms of not changing input suppliers frequently. This characterization of producers has important business implications, since identifying and segmenting the different types of producers with different cognitive styles and distinctive buying profiles is a key aspect of the strategic marketing plan of any company in the input markets. To our knowledge, this is the first paper on the topic of cognitive styles of Argentine producers and their impact on their purchasing behaviors.

Using Accounting Information In Decision-Making Process

Accounting system represents as the language of business because cost, price, sales volume, profits and return on investment are all accounting measurements and the purpose of accounting is to provide information that is useful for decision-making.
 This paper aimed to explain and clarify the important of using accounting information in decision-making process, to meet the requested administrative needs to rationalize the administrative decision of the company.
 In the field of data, we used primary sources and secondary sources to achieve the objectives of the study and answer its questions. Through this approach, we obtained the data constituting the theoretical framework of the study. While the practical setting depends on an interview.

Bioscience Data Literacy At The Interface Of The Environment, Human And Wildlife: One Health-centred education, research and practice perspectives in Rwanda

Advances in information technology have led to the availability of state-of-the-art technologies which in turn have been enabling the generation of unprecedented amounts of complex, structured or unstructured data sets that are sometimes difficult to process using conventional techniques. In particular, handling these large scale data in terms of collection, and aggregation, synthesis and analysis, interpretation, reporting, sharing and archiving processes, and interpreting them into descriptive models and enable effective interpretation requires continued development of robust computational models, algorithms and interoperable analytical frameworks (Hampton et al. 2017). This also involves the vital availability of data management expertise and reflects an imperative need for data science professionals, especially in the context of generating the most informative data for use and drive evidence-based decisions. Considering this, Rwanda has been fueling its economic transformation agenda, and, while this solely depends on natural resources exploitation, the scenario has led to critically concerning anthropogenic threats and unprecedented environmental vulnerability. Acknowledging the urgency to achieve its development needs while at the same time safeguarding the environmental sustainability, Rwanda has been promoting technology-enabled systems and approaches for sustainable management of environment and natural resources. Learning from global initiatives, Rwanda’s journey targets the effective use of technology-supported systems and data science expertise to effectively drive management and decision making needs in environmental management, health research systems and biodiversity conservation planning (Karame et al. 2017). Rwanda champions the adoption and effective use of technology towards delivering its vision of knowledge-based economy. A particular emphasis relates to streamlining the education, research and application of technology-supported systems and platforms and strengthening their effective use. From a practical One Health perspective, Rwanda has been bridging inter-sectoral gaps related to joint planning and resource sharing for informed decision processes. This One Health concept emphasizes the interconnection of the health of human, animals and ecosystems and involves the applications of multidisciplinary, coordinated, cross-sectoral collaborative efforts to attain optimal health for people, animals and the environment (Buttke et al. 2015). One Health constitutes a promising approach in the advancement of biosciences. For example, big data and ecological and digital epidemiology analysis has led to promising progress beyond the traditional transdisciplinary conservation medicine approach, and One Health is now driving solutions to major conservation and health challenges. This paper aims to explore the perspectives of solving challenges in handling heterogeneous data and sources of uncertainty, the progress and feasibility of adopting (or developing, adapting and customizing) open code- and data-sharing platforms, and integrating the application of flexible statistical models and cloud-computing, all within the confines of limited resources. Africa needs to engage in data science to build and sustain capacity and to effectively use acquired knowledge and skills. Further, Africa can strategically align and tailor existing technology data science platforms to the unique context of this continent. It is time to assess the boundaries, explore new horizons, and reach beyond the limits of current practice in order to enable researchers to get the most from generated data. We envision a long-term integrative and digital approach to handling and processing health, environment, and wildlife data to mark the beginning of our journey forward.